# migrations

dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: sysdb
filename: 00004-tenants-databases.sqlite.sql
hash: 048867ce8fcdefe4023c7110e4433591
sql:
CREATE TABLE IF NOT EXISTS tenants (
    id TEXT PRIMARY KEY,
    UNIQUE (id)
);

CREATE TABLE IF NOT EXISTS databases (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per tenant
    tenant_id TEXT NOT NULL REFERENCES tenants(id) ON DELETE CASCADE,
    UNIQUE (tenant_id, name) -- Ensure that a tenant has only one database with a given name
);

CREATE TABLE IF NOT EXISTS collections_tmp (
    id TEXT PRIMARY KEY, -- unique globally
    name TEXT NOT NULL, -- unique per database
    topic TEXT NOT NULL,
    dimension INTEGER,
    database_id TEXT NOT NULL REFERENCES databases(id) ON DELETE CASCADE,
    UNIQUE (name, database_id)
);

-- Create default tenant and database
INSERT OR REPLACE INTO tenants (id) VALUES ('default_tenant'); -- The default tenant id is 'default_tenant' others are UUIDs
INSERT OR REPLACE INTO databases (id, name, tenant_id) VALUES ('00000000-0000-0000-0000-000000000000', 'default_database', 'default_tenant');

INSERT OR REPLACE INTO collections_tmp (id, name, topic, dimension, database_id)
    SELECT id, name, topic, dimension, '00000000-0000-0000-0000-000000000000' FROM collections;
DROP TABLE collections;
ALTER TABLE collections_tmp RENAME TO collections;


dir: sysdb
filename: 00005-remove-topic.sqlite.sql
hash: b1367c826b8fba5f96f27befdc1d42d2
sql:
-- Remove the topic column from the Collections and Segments tables

ALTER TABLE collections DROP COLUMN topic;
ALTER TABLE segments DROP COLUMN topic;


dir: sysdb
filename: 00006-collection-segment-metadata.sqlite.sql
hash: 4eea7468935bf25d4604a0fed2366116
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE collection_metadata ADD COLUMN bool_value INTEGER;
ALTER TABLE segment_metadata ADD COLUMN bool_value INTEGER;


dir: sysdb
filename: 00007-collection-config.sqlite.sql
hash: 1c7e63bba346a42a18b6ab7f1c989bed
sql:
-- Stores collection configuration dictionaries.
ALTER TABLE collections ADD COLUMN config_json_str TEXT;


dir: sysdb
filename: 00008-maintenance-log.sqlite.sql
hash: 0a0e7e93111a01789addf64961c6127c
sql:
-- Records when database maintenance operations are performed.
-- At time of creation, this table is only used to record vacuum operations.
CREATE TABLE maintenance_log (
  id INT PRIMARY KEY,
  timestamp INT NOT NULL,
  operation TEXT NOT NULL
);


dir: sysdb
filename: 00009-segment-collection-not-null.sqlite.sql
hash: 054355aef9e63702bf54ea29e61563f1
sql:
-- This makes segments.collection non-nullable.
CREATE TABLE segments_temp (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    collection TEXT REFERENCES collection(id) NOT NULL
);

INSERT INTO segments_temp SELECT * FROM segments;
DROP TABLE segments;
ALTER TABLE segments_temp RENAME TO segments;


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


dir: metadb
filename: 00004-metadata-indices.sqlite.sql
hash: fb36603a45ee2cd0254cef3ef86585e8
sql:
CREATE INDEX IF NOT EXISTS embedding_metadata_int_value ON embedding_metadata (key, int_value) WHERE int_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_float_value ON embedding_metadata (key, float_value) WHERE float_value IS NOT NULL;
CREATE INDEX IF NOT EXISTS embedding_metadata_string_value ON embedding_metadata (key, string_value) WHERE string_value IS NOT NULL;


dir: metadb
filename: 00005-max-seq-id-int.sqlite.sql
hash: 0e9de46758761b373ce682925edcc326
sql:
ALTER TABLE max_seq_id ADD COLUMN int_seq_id INTEGER;

-- Convert 8 byte wide big-endian integer as blob to native 64 bit integer.
-- Adapted from https://stackoverflow.com/a/70296198.
UPDATE max_seq_id SET int_seq_id = (
  SELECT (
       (instr('123456789ABCDEF', substr(hex(seq_id), -1 , 1)) <<  0)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -2 , 1)) <<  4)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -3 , 1)) <<  8)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -4 , 1)) << 12)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -5 , 1)) << 16)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -6 , 1)) << 20)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -7 , 1)) << 24)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -8 , 1)) << 28)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -9 , 1)) << 32)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -10, 1)) << 36)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -11, 1)) << 40)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -12, 1)) << 44)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -13, 1)) << 48)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -14, 1)) << 52)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -15, 1)) << 56)
     | (instr('123456789ABCDEF', substr(hex(seq_id), -16, 1)) << 60)
    )
);

ALTER TABLE max_seq_id DROP COLUMN seq_id;
ALTER TABLE max_seq_id RENAME COLUMN int_seq_id TO seq_id;


dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


# collections

id: 0cc11db2-9aa1-48d4-ab09-c25c03dc2063
name: huggingface_datasets
database_id: 00000000-0000-0000-0000-000000000000
config_json_str:
{"vector_index":{"hnsw":{"space":"cosine","ef_construction":100,"ef_search":100,"max_neighbors":16,"resize_factor":1.2,"sync_threshold":1000}},"embedding_function":{"type":"known","name":"default","config":{}}}


# embedding_metadata

key: chroma:document
string_value:
# Dataset: `fka/awesome-chatgpt-prompts`

## 📝 Metadata
- **Author:** fka
- **Downloads:** 34418
- **Likes:** 9309
- **Tags:** task_categories:question-answering, license:cc0-1.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ChatGPT
- **License:** Not specified

## 📖 Description
```text
🧠 Awesome ChatGPT Prompts [CSV dataset]

This is a Dataset Repository of Awesome ChatGPT Prompts
View All Prompts on GitHub

	
		
		License
	

CC-0...
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `prompts.csv`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `act` | `str` |
| `prompt` | `str` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceFW/fineweb`

## 📝 Metadata
- **Author:** HuggingFaceFW
- **Downloads:** 277712
- **Likes:** 2405
- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:10B<n<100B, modality:tabular, modality:text, arxiv:2306.01116, arxiv:2109.07445, arxiv:2406.17557, doi:10.57967/hf/2493, region:us
- **License:** Not specified

## 📖 Description
```text
🍷 FineWeb
	


    



15 trillion tokens of the finest data the 🌐 web has to offer


	
		
		What is it?
	

The 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. 
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/CC-MAIN-2013-20/000_00000.parquet`
- `data/CC-MAIN-2013-20/000_00001.parquet`
- `data/CC-MAIN-2013-20/000_00002.parquet`
- `data/CC-MAIN-2013-20/000_00003.parquet`
- `data/CC-MAIN-2013-20/000_00004.parquet`
- `data/CC-MAIN-2013-20/000_00005.parquet`
- `data/CC-MAIN-2013-20/000_00006.parquet`
- `data/CC-MAIN-2013-20/000_00007.parquet`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |

### Config: `sample-10BT`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |

### Config: `sample-100BT`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |


key: chroma:document
string_value:
# Dataset: `Anthropic/hh-rlhf`

## 📝 Metadata
- **Author:** Anthropic
- **Downloads:** 33017
- **Likes:** 1464
- **Tags:** license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2204.05862, region:us, human-feedback
- **License:** Not specified

## 📖 Description
```text
Dataset Card for HH-RLHF
	


	
		
		Dataset Summary
	

This repository provides access to two different kinds of data:

Human preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `harmless-base/test.jsonl.gz`
- `harmless-base/train.jsonl.gz`
- `helpful-base/test.jsonl.gz`
- `helpful-base/train.jsonl.gz`
- `helpful-online/test.jsonl.gz`
- `helpful-online/train.jsonl.gz`
- `helpful-rejection-sampled/test.jsonl.gz`
- `helpful-rejection-sampled/train.jsonl.gz`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `chosen` | `str` |
| `rejected` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `chosen` | `str` |
| `rejected` | `str` |


key: chroma:document
string_value:
# Dataset: `Open-Orca/OpenOrca`

## 📝 Metadata
- **Author:** Open-Orca
- **Downloads:** 8268
- **Likes:** 1457
- **Tags:** task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us
- **License:** Not specified

## 📖 Description
```text
🐋 The OpenOrca Dataset! 🐋



We are thrilled to announce the release of the OpenOrca dataset!
This rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.
It has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!

	
		
	
	
		Official Models
	


	
	
	
		Mistral-7B-OpenOrca
	

Our latest model, the first 7B to score better overall than all… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/OpenOrca....
```

## 📂 File System Sample
- `.gitattributes`
- `1M-GPT4-Augmented.parquet`
- `3_5M-GPT3_5-Augmented.parquet`
- `OpenOrcaLogo.png`
- `README.md`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `str` |
| `system_prompt` | `str` |
| `question` | `str` |
| `response` | `str` |


key: chroma:document
string_value:
# Dataset: `OpenAssistant/oasst1`

## 📝 Metadata
- **Author:** OpenAssistant
- **Downloads:** 8123
- **Likes:** 1440
- **Tags:** language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback
- **License:** Not specified

## 📖 Description
```text
OpenAssistant Conversations Dataset (OASST1)
	


	
		
		Dataset Summary
	

In an effort to democratize research on large-scale alignment, we release OpenAssistant 
Conversations (OASST1), a human-generated, human-annotated assistant-style conversation 
corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 
quality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus 
is a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1....
```

## 📂 File System Sample
- `.gitattributes`
- `2023-04-12_oasst_all.messages.jsonl.gz`
- `2023-04-12_oasst_all.trees.jsonl.gz`
- `2023-04-12_oasst_prompts.messages.jsonl.gz`
- `2023-04-12_oasst_ready.messages.jsonl.gz`
- `2023-04-12_oasst_ready.trees.jsonl.gz`
- `2023-04-12_oasst_spam.messages.jsonl.gz`
- `LICENSE`
- `README.md`
- `data/train-00000-of-00001-b42a775f407cee45.parquet`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `message_id` | `str` |
| `parent_id` | `NoneType` |
| `user_id` | `str` |
| `created_date` | `str` |
| `text` | `str` |
| `role` | `str` |
| `lang` | `str` |
| `review_count` | `int` |
| `review_result` | `bool` |
| `deleted` | `bool` |
| `rank` | `NoneType` |
| `synthetic` | `bool` |
| `model_name` | `NoneType` |
| `detoxify` | `dict` |
| `message_tree_id` | `str` |
| `tree_state` | `str` |
| `emojis` | `dict` |
| `labels` | `dict` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `message_id` | `str` |
| `parent_id` | `NoneType` |
| `user_id` | `str` |
| `created_date` | `str` |
| `text` | `str` |
| `role` | `str` |
| `lang` | `str` |
| `review_count` | `int` |
| `review_result` | `bool` |
| `deleted` | `bool` |
| `rank` | `NoneType` |
| `synthetic` | `bool` |
| `model_name` | `NoneType` |
| `detoxify` | `dict` |
| `message_tree_id` | `str` |
| `tree_state` | `str` |
| `emojis` | `dict` |
| `labels` | `dict` |


key: chroma:document
string_value:
# Dataset: `gsdf/EasyNegative`

## 📝 Metadata
- **Author:** gsdf
- **Downloads:** 63081
- **Likes:** 1158
- **Tags:** license:other, size_categories:n<1K, format:imagefolder, modality:image, library:datasets, library:mlcroissant, region:us
- **License:** Not specified

## 📖 Description
```text
Negative Embedding
	

This is a Negative Embedding trained with Counterfeit. Please use it in the "\stable-diffusion-webui\embeddings" folder.It can be used with other models, but the effectiveness is not certain.  

	
		
		Counterfeit-V2.0.safetensors
	



	
		
		AbyssOrangeMix2_sfw.safetensors
	



	
		
		anything-v4.0-pruned.safetensors...
```

## 📂 File System Sample
- `.gitattributes`
- `EasyNegative.pt`
- `EasyNegative.safetensors`
- `README.md`
- `sample01.png`
- `sample02.png`
- `sample03.png`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `image` | `PngImageFile` |


key: chroma:document
string_value:
# Dataset: `togethercomputer/RedPajama-Data-1T`

## 📝 Metadata
- **Author:** togethercomputer
- **Downloads:** 1009
- **Likes:** 1105
- **Tags:** task_categories:text-generation, language:en, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us
- **License:** Not specified

## 📖 Description
```text
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `RedPajama-Data-1T.py`
- `urls/arxiv.txt`
- `urls/c4.txt`
- `urls/common_crawl.txt`
- `urls/github.txt`
- `urls/stackexchange.txt`
- `urls/wikipedia.txt`

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset scripts are no longer supported, but found RedPajama-Data-1T.py
```


key: chroma:document
string_value:
# Dataset: `wikimedia/wikipedia`

## 📝 Metadata
- **Author:** wikimedia
- **Downloads:** 58900
- **Likes:** 955
- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, language:ab, language:ace, language:ady, language:af, language:alt, language:am, language:ami, language:an, language:ang, language:anp, language:ar, language:arc, language:ary, language:arz, language:as, language:ast, language:atj, language:av, language:avk, language:awa, language:ay, language:az, language:azb, language:ba, language:ban, language:bar, language:bbc, language:bcl, language:be, language:bg, language:bh, language:bi, language:bjn, language:blk, language:bm, language:bn, language:bo, language:bpy, language:br, language:bs, language:bug, language:bxr, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:ch, language:chr, language:chy, language:ckb, language:co, language:cr, language:crh, language:cs, language:csb, language:cu, language:cv, language:cy, language:da, language:dag, language:de, language:dga, language:din, language:diq, language:dsb, language:dty, language:dv, language:dz, language:ee, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fat, language:ff, language:fi, language:fj, language:fo, language:fon, language:fr, language:frp, language:frr, language:fur, language:fy, language:ga, language:gag, language:gan, language:gcr, language:gd, language:gl, language:glk, language:gn, language:gom, language:gor, language:got, language:gpe, language:gsw, language:gu, language:guc, language:gur, language:guw, language:gv, language:ha, language:hak, language:haw, language:hbs, language:he, language:hi, language:hif, language:hr, language:hsb, language:ht, language:hu, language:hy, language:hyw, language:ia, language:id, language:ie, language:ig, language:ik, language:ilo, language:inh, language:io, language:is, language:it, language:iu, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kbp, language:kcg, language:kg, language:ki, language:kk, language:kl, language:km, language:kn, language:ko, language:koi, language:krc, language:ks, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lbe, language:lez, language:lfn, language:lg, language:li, language:lij, language:lld, language:lmo, language:ln, language:lo, language:lt, language:ltg, language:lv, language:lzh, language:mad, language:mai, language:map, language:mdf, language:mg, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mni, language:mnw, language:mr, language:mrj, language:ms, language:mt, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nan, language:nap, language:nds, language:ne, language:new, language:nia, language:nl, language:nn, language:no, language:nov, language:nqo, language:nrf, language:nso, language:nv, language:ny, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pcm, language:pdc, language:pfl, language:pi, language:pih, language:pl, language:pms, language:pnb, language:pnt, language:ps, language:pt, language:pwn, language:qu, language:rm, language:rmy, language:rn, language:ro, language:ru, language:rue, language:rup, language:rw, language:sa, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:se, language:sg, language:sgs, language:shi, language:shn, language:si, language:sk, language:skr, language:sl, language:sm, language:smn, language:sn, language:so, language:sq, language:sr, language:srn, language:ss, language:st, language:stq, language:su, language:sv, language:sw, language:szl, language:szy, language:ta, language:tay, language:tcy, language:te, language:tet, language:tg, language:th, language:ti, language:tk, language:tl, language:tly, language:tn, language:to, language:tpi, language:tr, language:trv, language:ts, language:tt, language:tum, language:tw, language:ty, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wo, language:wuu, language:xal, language:xh, language:xmf, language:yi, language:yo, language:yue, language:za, language:zea, language:zgh, language:zh, language:zu, license:cc-by-sa-3.0, license:gfdl, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for Wikimedia Wikipedia
	


	
		
		Dataset Summary
	

Wikipedia dataset containing cleaned articles of all languages.
The dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)
with one subset per language, each containing a single train split.
Each example contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
All language subsets have already been processed for recent dump, and you… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia....
```

## 📂 File System Sample
- `.gitattributes`
- `20231101.ab/train-00000-of-00001.parquet`
- `20231101.ace/train-00000-of-00001.parquet`
- `20231101.ady/train-00000-of-00001.parquet`
- `20231101.af/train-00000-of-00001.parquet`
- `20231101.als/train-00000-of-00001.parquet`
- `20231101.alt/train-00000-of-00001.parquet`
- `20231101.am/train-00000-of-00001.parquet`
- `20231101.ami/train-00000-of-00001.parquet`
- `20231101.an/train-00000-of-00001.parquet`
- ... and more.

## 📊 Data Structure
### Config: `20231101.ab`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `str` |
| `url` | `str` |
| `title` | `str` |
| `text` | `str` |

### Config: `20231101.ace`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `str` |
| `url` | `str` |
| `title` | `str` |
| `text` | `str` |

### Config: `20231101.ady`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `str` |
| `url` | `str` |
| `title` | `str` |
| `text` | `str` |


key: chroma:document
string_value:
# Dataset: `allenai/dolma`

## 📝 Metadata
- **Author:** allenai
- **Downloads:** 1045
- **Likes:** 950
- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:n>1T, arxiv:2402.00159, arxiv:2301.13688, region:us, language-modeling, casual-lm, llm
- **License:** Not specified

## 📖 Description
```text
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research...
```

## 📂 File System Sample
- `.gitattributes`
- `.gitignore`
- `LICENSE.md`
- `README.md`
- `dolma.py`
- `urls/v1.txt`
- `urls/v1_5-sample.txt`
- `urls/v1_5.txt`
- `urls/v1_6-sample.txt`
- `urls/v1_6.txt`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset scripts are no longer supported, but found dolma.py
```


key: chroma:document
string_value:
# Dataset: `Nerfgun3/bad_prompt`

## 📝 Metadata
- **Author:** Nerfgun3
- **Downloads:** 3360
- **Likes:** 926
- **Tags:** language:en, license:creativeml-openrail-m, size_categories:n<1K, format:imagefolder, modality:image, library:datasets, library:mlcroissant, region:us, stable-diffusion, text-to-image, image-to-image
- **License:** Not specified

## 📖 Description
```text
Negative Embedding / Textual Inversion
	




	
		
		Idea
	

The idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. 
Side note: Embedding has proven to be very helpful for the generation of hands! :)

	
		
		Usage
	

To use this embedding you have to download the file aswell as drop it into the "\stable-diffusion-webui\embeddings" folder.
Please put the embedding in the negative… See the full description on the dataset page: https://huggingface.co/datasets/Nerfgun3/bad_prompt....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `bad_prompt.pt`
- `bad_prompt_showcase.jpg`
- `bad_prompt_version2.pt`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `image` | `JpegImageFile` |


key: chroma:document
string_value:
# Dataset: `openai/gsm8k`

## 📝 Metadata
- **Author:** openai
- **Downloads:** 386964
- **Likes:** 915
- **Tags:** annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2110.14168, region:us, math-word-problems
- **License:** Not specified

## 📖 Description
```text
Dataset Card for GSM8K
	


	
		
		Dataset Summary
	

GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.

These problems take between 2 and 8 steps to solve.
Solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `main/test-00000-of-00001.parquet`
- `main/train-00000-of-00001.parquet`
- `socratic/test-00000-of-00001.parquet`
- `socratic/train-00000-of-00001.parquet`

## 📊 Data Structure
### Config: `main`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `answer` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `answer` | `str` |

### Config: `socratic`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `answer` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `answer` | `str` |


key: chroma:document
string_value:
# Dataset: `FreedomIntelligence/medical-o1-reasoning-SFT`

## 📝 Metadata
- **Author:** FreedomIntelligence
- **Downloads:** 8138
- **Likes:** 913
- **Tags:** task_categories:question-answering, task_categories:text-generation, language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.18925, region:us, medical, biology
- **License:** Not specified

## 📖 Description
```text
News
	

[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.
[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.
[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `medical_o1_sft.json`
- `medical_o1_sft_Chinese.json`
- `medical_o1_sft_mix.json`
- `medical_o1_sft_mix_Chinese.json`

## 📊 Data Structure
### Config: `en`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `Question` | `str` |
| `Complex_CoT` | `str` |
| `Response` | `str` |

### Config: `zh`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `Question` | `str` |
| `Complex_CoT` | `str` |
| `Response` | `str` |

### Config: `en_mix`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `Question` | `str` |
| `Complex_CoT` | `str` |
| `Response` | `str` |


key: chroma:document
string_value:
# Dataset: `tiiuae/falcon-refinedweb`

## 📝 Metadata
- **Author:** tiiuae
- **Downloads:** 18435
- **Likes:** 874
- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.01116, arxiv:2203.15556, arxiv:2107.06499, arxiv:2104.08758, arxiv:2109.07445, arxiv:1911.00359, arxiv:2112.11446, doi:10.57967/hf/0737, region:us
- **License:** Not specified

## 📖 Description
```text
📀 Falcon RefinedWeb
	

Falcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.
See the 📓 paper on arXiv for more details. 
RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data. 
RefinedWeb is also "multimodal-friendly": it contains links and alt… See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/train-00000-of-05534-b8fc5348cbe605a5.parquet`
- `data/train-00001-of-05534-9bca3ce859516338.parquet`
- `data/train-00002-of-05534-01680948bd81de83.parquet`
- `data/train-00003-of-05534-b7806bb8ca893c23.parquet`
- `data/train-00004-of-05534-cbe1137f523084a7.parquet`
- `data/train-00005-of-05534-e4a5eae6c1419c9b.parquet`
- `data/train-00006-of-05534-5bc49be138fd315b.parquet`
- `data/train-00007-of-05534-c185da59aece723d.parquet`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `content` | `str` |
| `url` | `str` |
| `timestamp` | `datetime` |
| `dump` | `str` |
| `segment` | `str` |
| `image_urls` | `list` |


key: chroma:document
string_value:
# Dataset: `databricks/databricks-dolly-15k`

## 📝 Metadata
- **Author:** databricks
- **Downloads:** 17397
- **Likes:** 873
- **Tags:** task_categories:question-answering, task_categories:summarization, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.02155, region:us
- **License:** Not specified

## 📖 Description
```text
Summary
	

databricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several 
of the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, 
closed QA, generation, information extraction, open QA, and summarization.
This dataset can be used for any purpose, whether academic or commercial,  under the terms of the 
Creative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `databricks-dolly-15k.jsonl`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `context` | `str` |
| `response` | `str` |
| `category` | `str` |


key: chroma:document
string_value:
# Dataset: `bigcode/the-stack`

## 📝 Metadata
- **Author:** bigcode
- **Downloads:** 35469
- **Likes:** 865
- **Tags:** task_categories:text-generation, language_creators:crowdsourced, language_creators:expert-generated, multilinguality:multilingual, language:code, license:other, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2211.15533, arxiv:2107.03374, arxiv:2207.14157, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for The Stack
	



	
		
		Changelog
	


	
		
Release
Description


		
v1.0
Initial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.


v1.1
The three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/abap/train-00000-of-00001.parquet`
- `data/actionscript/train-00000-of-00002.parquet`
- `data/actionscript/train-00001-of-00002.parquet`
- `data/ada/train-00000-of-00001.parquet`
- `data/agda/train-00000-of-00001.parquet`
- `data/ags-script/train-00000-of-00001.parquet`
- `data/alloy/train-00000-of-00001.parquet`
- `data/ampl/train-00000-of-00001.parquet`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'bigcode/the-stack' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/bigcode/the-stack to ask for access.
```


key: chroma:document
string_value:
# Dataset: `anon8231489123/ShareGPT_Vicuna_unfiltered`

## 📝 Metadata
- **Author:** anon8231489123
- **Downloads:** 43328
- **Likes:** 822
- **Tags:** language:en, license:apache-2.0, region:us
- **License:** Not specified

## 📖 Description
```text
Further cleaning done. Please look through the dataset and ensure that I didn't miss anything.
Update: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c
Two choices:

Removes instances of "I'm sorry, but": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json
Has instances of "I'm sorry, but":… See the full description on the dataset page: https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered....
```

## 📂 File System Sample
- `.gitattributes`
- `HTML_cleaned_raw_dataset/sg_90k_part1.json`
- `HTML_cleaned_raw_dataset/sg_90k_part1_html_cleaned.json`
- `HTML_cleaned_raw_dataset/sg_90k_part2.json`
- `HTML_cleaned_raw_dataset/sg_90k_part2_html_cleaned.json`
- `README.md`
- `ShareGPT_V3_unfiltered_cleaned_split.json`
- `ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json`
- `Vicuna_unfiltered_train.ipynb`
- `clean_sharegpt.py`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: No (supported) data files found in anon8231489123/ShareGPT_Vicuna_unfiltered
```


key: chroma:document
string_value:
# Dataset: `tatsu-lab/alpaca`

## 📝 Metadata
- **Author:** tatsu-lab
- **Downloads:** 38066
- **Likes:** 812
- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction-finetuning
- **License:** Not specified

## 📖 Description
```text
Dataset Card for Alpaca
	


	
		
		Dataset Summary
	

Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:

The text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/train-00000-of-00001-a09b74b3ef9c3b56.parquet`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
| `text` | `str` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceFW/fineweb-edu`

## 📝 Metadata
- **Author:** HuggingFaceFW
- **Downloads:** 373200
- **Likes:** 784
- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.17557, arxiv:2404.14219, arxiv:2401.10020, arxiv:2109.07445, doi:10.57967/hf/2497, region:us
- **License:** Not specified

## 📖 Description
```text
📚 FineWeb-Edu
	


    



1.3 trillion tokens of the finest educational data the 🌐 web has to offer

Paper: https://arxiv.org/abs/2406.17557

	
		
	
	
		What is it?
	

📚 FineWeb-Edu  dataset consists of 1.3T tokens  and  5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.
To enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/CC-MAIN-2013-20/train-00000-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00001-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00002-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00003-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00004-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00005-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00006-of-00014.parquet`
- `data/CC-MAIN-2013-20/train-00007-of-00014.parquet`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `NoneType` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |
| `score` | `float` |
| `int_score` | `int` |

### Config: `sample-10BT`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |
| `score` | `float` |
| `int_score` | `int` |

### Config: `sample-100BT`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `token_count` | `int` |
| `score` | `float` |
| `int_score` | `int` |


key: chroma:document
string_value:
# Dataset: `roneneldan/TinyStories`

## 📝 Metadata
- **Author:** roneneldan
- **Downloads:** 43353
- **Likes:** 767
- **Tags:** task_categories:text-generation, language:en, license:cdla-sharing-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.07759, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.
Described in the following paper: https://arxiv.org/abs/2305.07759. 
The models referred to in the paper were trained on TinyStories-train.txt  (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.
Additional resources:
tinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories....
```

## 📂 File System Sample
- `.gitattributes`
- `Evaluation prompts.yaml`
- `MLP_input_output.npy`
- `README.md`
- `TinyStories-train.txt`
- `TinyStories-valid.txt`
- `TinyStoriesV2-GPT4-train.txt`
- `TinyStoriesV2-GPT4-valid.txt`
- `TinyStories_all_data.tar.gz`
- `data/train-00000-of-00004-2d5a1467fff1081b.parquet`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `text` | `str` |


key: chroma:document
string_value:
# Dataset: `open-thoughts/OpenThoughts-114k`

## 📝 Metadata
- **Author:** open-thoughts
- **Downloads:** 42497
- **Likes:** 766
- **Tags:** license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.04178, region:us, curator, synthetic
- **License:** Not specified

## 📖 Description
```text
[!NOTE]
We have released a paper for OpenThoughts! See our paper here.


 



	
		
	
	
		Open-Thoughts-114k
	

Open synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles!
Inspect the content with rich formatting with Curator Viewer.

	
	
	
		Available Subsets
	

default subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:
ds = load_dataset("open-thoughts/OpenThoughts-114k", split="train")… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/train-00000-of-00006.parquet`
- `data/train-00001-of-00006.parquet`
- `data/train-00002-of-00006.parquet`
- `data/train-00003-of-00006.parquet`
- `data/train-00004-of-00006.parquet`
- `data/train-00005-of-00006.parquet`
- `diagram.png`
- `diagram_dark.png`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `system` | `str` |
| `conversations` | `list` |

### Config: `metadata`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `problem` | `str` |
| `deepseek_reasoning` | `str` |
| `deepseek_solution` | `str` |
| `ground_truth_solution` | `str` |
| `domain` | `str` |
| `source` | `str` |
| `test_cases` | `NoneType` |
| `starter_code` | `NoneType` |


key: chroma:document
string_value:
# Dataset: `teknium/OpenHermes-2.5`

## 📝 Metadata
- **Author:** teknium
- **Downloads:** 2876
- **Likes:** 762
- **Tags:** language:eng, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, GPT-4, Distillation, Compilation
- **License:** Not specified

## 📖 Description
```text
Dataset Card for Dataset Name
	

This is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models.
Support me on GitHub sponsors <3 : https://github.com/sponsors/teknium1

	
		
		Dataset Details
	


	
		
		Dataset Description
	

The Open Hermes 2/2.5 and Nous Hermes 2 models have made significant advancements of SOTA LLM's over recent months, and are underpinned by this exact compilation and curation of many open source datasets and custom created synthetic datasets.… See the full description on the dataset page: https://huggingface.co/datasets/teknium/OpenHermes-2.5....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `openhermes2_5.json`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `custom_instruction` | `NoneType` |
| `topic` | `NoneType` |
| `model_name` | `NoneType` |
| `model` | `NoneType` |
| `skip_prompt_formatting` | `bool` |
| `category` | `str` |
| `conversations` | `list` |
| `views` | `NoneType` |
| `language` | `NoneType` |
| `id` | `NoneType` |
| `title` | `NoneType` |
| `idx` | `NoneType` |
| `hash` | `NoneType` |
| `avatarUrl` | `NoneType` |
| `system_prompt` | `NoneType` |
| `source` | `str` |


key: chroma:document
string_value:
# Dataset: `lmsys/lmsys-chat-1m`

## 📝 Metadata
- **Author:** lmsys
- **Downloads:** 9470
- **Likes:** 749
- **Tags:** size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.11998, region:us
- **License:** Not specified

## 📖 Description
```text
LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset
	

This dataset contains one million real-world conversations with 25 state-of-the-art LLMs.
It is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.
Each sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.
User consent is obtained through the "Terms of use"… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/train-00000-of-00006-4feeb3f83346a0e9.parquet`
- `data/train-00001-of-00006-4030672591c2f478.parquet`
- `data/train-00002-of-00006-1779b7cec9462180.parquet`
- `data/train-00003-of-00006-2fa862bfed56af1f.parquet`
- `data/train-00004-of-00006-18f4bdd50c103e71.parquet`
- `data/train-00005-of-00006-fe1acc5d10a9f0e2.parquet`

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'lmsys/lmsys-chat-1m' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/lmsys/lmsys-chat-1m to ask for access.
```


key: chroma:document
string_value:
# Dataset: `QingyiSi/Alpaca-CoT`

## 📝 Metadata
- **Author:** QingyiSi
- **Downloads:** 7158
- **Likes:** 737
- **Tags:** language:en, language:zh, language:ml, license:apache-2.0, region:us, Instruction, Cot
- **License:** Not specified

## 📖 Description
```text
Instruction-Finetuning Dataset Collection (Alpaca-CoT)
	

This repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.
We also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in https://github.com/PhoebusSi/alpaca-CoT.  
If you think this dataset collection is helpful to you, please like this… See the full description on the dataset page: https://huggingface.co/datasets/QingyiSi/Alpaca-CoT....
```

## 📂 File System Sample
- `.gitattributes`
- `Auto-CoT/Auto-CoT.json`
- `Auto-CoT/Auto.json`
- `COIG/counterfactural_correction_multi_round_chat.json`
- `COIG/counterfactural_correction_multi_round_chat_context.json`
- `COIG/exam.json`
- `COIG/human-value-alignment_common.json`
- `COIG/human-value-alignment_special.json`
- `COIG/leetcode.json`
- `COIG/translate_en.json`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |


key: chroma:document
string_value:
# Dataset: `yahma/alpaca-cleaned`

## 📝 Metadata
- **Author:** yahma
- **Downloads:** 26690
- **Likes:** 736
- **Tags:** task_categories:text-generation, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction-finetuning
- **License:** Not specified

## 📖 Description
```text
Dataset Card for Alpaca-Cleaned
	


Repository: https://github.com/gururise/AlpacaDataCleaned


	
		
		Dataset Description
	

This is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:

Hallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.

"instruction":"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `alpaca_data_cleaned.json`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `output` | `str` |
| `input` | `str` |
| `instruction` | `str` |


key: chroma:document
string_value:
# Dataset: `Congliu/Chinese-DeepSeek-R1-Distill-data-110k`

## 📝 Metadata
- **Author:** Congliu
- **Downloads:** 767
- **Likes:** 703
- **Tags:** task_categories:text-generation, task_categories:question-answering, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us
- **License:** Not specified

## 📖 Description
```text
中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）
	


🤗 Hugging Face   |   🤖 ModelScope    |   🚀 Github    |   📑 Blog


注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。
本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。
为什么开源这个数据？
R1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。
为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：

Math：共计36568个样本，
Exam：共计2432个样本，
STEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `distill_r1_110k.jsonl`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `input` | `str` |
| `content` | `str` |
| `reasoning_content` | `str` |
| `repo_name` | `str` |
| `prompt_tokens_len` | `int` |
| `content_tokens_len` | `int` |
| `reasoning_content_tokens_len` | `int` |
| `score` | `int` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceFW/fineweb-2`

## 📝 Metadata
- **Author:** HuggingFaceFW
- **Downloads:** 114244
- **Likes:** 676
- **Tags:** task_categories:text-generation, language:aai, language:aak, language:aau, language:aaz, language:aba, language:abi, language:abk, language:abn, language:abq, language:abs, language:abt, language:abx, language:aby, language:abz, language:aca, language:acd, language:ace, language:acf, language:ach, language:acm, language:acn, language:acr, language:acu, language:ada, language:ade, language:adh, language:adi, language:adj, language:adl, language:ady, language:adz, language:aeb, language:aer, language:aeu, language:aey, language:afr, language:agd, language:agg, language:agm, language:agn, language:agr, language:agt, language:agu, language:agw, language:agx, language:aha, language:ahk, language:aia, language:aii, language:aim, language:ain, language:ajg, language:aji, language:ajz, language:akb, language:ake, language:akh, language:akp, language:alj, language:aln, language:alp, language:alq, language:als, language:alt, language:aly, language:alz, language:ame, language:amf, language:amh, language:ami, language:amk, language:amm, language:amn, language:amp, language:amr, language:amu, language:amx, language:ang, language:anm, language:ann, language:anp, language:anv, language:any, language:aoi, language:aoj, language:aom, language:aoz, language:apb, language:apc, language:ape, language:apn, language:apr, language:apt, language:apu, language:apw, language:apy, language:apz, language:arb, language:are, language:arg, language:arl, language:arn, language:arp, language:arq, language:ars, language:ary, language:arz, language:asg, language:asm, language:aso, language:ast, language:ata, language:atb, language:atd, language:atg, language:ati, language:atj, language:atq, language:att, language:auc, language:aui, language:auy, language:ava, language:avk, language:avn, language:avt, language:avu, language:awa, language:awb, language:awx, language:ayo, language:ayp, language:ayr, language:azb, language:azg, language:azj, language:azz, language:bak, language:bam, language:ban, language:bao, language:bar, language:bas, language:bav, language:bba, language:bbb, language:bbc, language:bbj, language:bbk, language:bbo, language:bbr, language:bch, language:bci, language:bcl, language:bco, language:bcw, language:bdd, language:bdh, language:bdq, language:bea, language:bef, language:bel, language:bem, language:ben, language:beq, language:bew, language:bex, language:bfd, language:bfo, language:bgr, language:bgs, language:bgt, language:bgz, language:bhg, language:bhl, language:bho, language:bhp, language:bhw, language:bhz, language:bib, language:big, language:bim, language:bin, language:bis, language:biu, language:biv, language:bjn, language:bjp, language:bjr, language:bjv, language:bkd, language:bkl, language:bkq, language:bku, language:bkv, language:bla, language:blh, language:blk, language:blw, language:blz, language:bmh, language:bmk, language:bmq, language:bmr, language:bmu, language:bmv, language:bno, language:bnp, language:boa, language:bod, language:boj, language:bom, language:bon, language:bos, language:bov, language:box, language:bpr, language:bps, language:bpy, language:bqc, language:bqj, language:bqp, language:bre, language:brh, language:bru, language:brx, language:bsc, language:bsn, language:bsp, language:bsq, language:bss, language:btd, language:bth, language:bts, language:btt, language:btx, language:bud, language:bug, language:buk, language:bul, language:bum, language:bus, language:bvc, language:bvd, language:bvr, language:bvz, language:bwd, language:bwi, language:bwq, language:bwu, language:bxh, language:bxr, language:byr, language:byv, language:byx, language:bzd, language:bzh, language:bzi, language:bzj, language:caa, language:cab, language:cac, language:caf, language:cag, language:cak, language:cao, language:cap, language:caq, language:car, language:cas, language:cat, language:cav, language:cax, language:cbc, language:cbi, language:cbk, language:cbr, language:cbs, language:cbt, language:cbu, language:cbv, language:cce, language:cco, language:ccp, language:ceb, language:ceg, language:cek, language:ces, language:cfm, language:cgc, language:cgg, language:cha, language:chd, language:che, language:chf, language:chj, language:chk, language:cho, language:chq, language:chr, language:chu, language:chv, language:chw, language:chz, language:cjk, language:cjo, language:cjp, language:cjs, language:cjv, language:ckb, language:cko, language:ckt, language:cle, language:clu, language:cly, language:cme, language:cmn, language:cmo, language:cmr, language:cnh, language:cni, language:cnk, language:cnl, language:cnt, language:cnw, language:coe, language:cof, language:cok, language:con, language:cop, language:cor, language:cos, language:cot, language:cou, language:cpa, language:cpb, language:cpc, language:cpu, language:cpy, language:crh, language:crj, language:crk, language:crl, language:crm, language:crn, language:crs, language:crt, language:crx, language:csb, language:csk, language:cso, language:csw, language:csy, language:cta, language:ctd, language:cto, language:ctp, language:ctu, language:cub, language:cuc, language:cui, language:cuk, language:cul, language:cut, language:cux, language:cwe, language:cwt, language:cya, language:cym, language:czt, language:daa, language:dad, language:daf, language:dag, language:dah, language:dak, language:dan, language:dar, language:ddg, language:ddn, language:ded, language:des, language:deu, language:dga, language:dgc, language:dgi, language:dgr, language:dgz, language:dhg, language:dhm, language:dhv, language:did, language:dig, language:dik, language:diq, language:dis, language:diu, language:div, language:dje, language:djk, language:djr, language:dks, language:dln, language:dng, language:dnj, language:dnw, language:dob, language:doi, language:dop, language:dos, language:dow, language:drg, language:dru, language:dsb, language:dtb, language:dtp, language:dts, language:dty, language:dua, language:due, language:dug, language:duo, language:dur, language:dwr, language:dww, language:dyi, language:dyo, language:dyu, language:dzo, language:ebk, language:efi, language:eka, language:ekk, language:eko, language:ell, language:emi, language:eml, language:emp, language:enb, language:enl, language:enm, language:enq, language:enx, language:epo, language:eri, language:ese, language:esi, language:esk, language:ess, language:esu, language:eto, language:etr, language:etu, language:eus, language:eve, language:ewe, language:ewo, language:ext, language:eza, language:faa, language:fad, language:fai, language:fal, language:fan, language:fao, language:far, language:fas, language:fat, language:ffm, language:fij, language:fil, language:fin, language:fit, language:fkv, language:fmu, language:fon, language:for, language:fra, language:frd, language:fro, language:frp, language:frr, language:fry, language:fub, language:fud, language:fue, language:fuf, language:fuh, language:fuq, language:fur, language:fuv, language:gaa, language:gag, language:gah, language:gai, language:gam, language:gaw, language:gaz, language:gbi, language:gbo, language:gbr, language:gcf, language:gcr, language:gde, language:gdg, language:gdn, language:gdr, language:geb, language:gej, language:gfk, language:ghs, language:gid, language:gil, language:giz, language:gjn, language:gkn, language:gla, language:gle, language:glg, language:glk, language:glv, language:gmh, language:gmv, language:gna, language:gnb, language:gnd, language:gng, language:gnn, language:gnw, language:goa, language:gof, language:gog, language:goh, language:gom, language:gor, language:gos, language:got, language:gqr, language:grc, language:grt, language:gso, language:gsw, language:gub, language:guc, language:gud, language:gug, language:guh, language:gui, language:guj, language:guk, language:gul, language:gum, language:gun, language:guo, language:guq, language:gur, language:guu, language:guw, language:gux, language:guz, language:gvc, language:gvf, language:gvl, language:gvn, language:gwi, language:gwr, language:gya, language:gym, language:gyr, language:hac, language:hae, language:hag, language:hak, language:hat, language:hav, language:haw, language:hay, language:hbo, language:hch, language:heb, language:heg, language:heh, language:her, language:hif, language:hig, language:hil, language:hin, language:hix, language:hla, language:hmo, language:hmr, language:hne, language:hnj, language:hnn, language:hns, language:hop, language:hot, language:hra, language:hrv, language:hrx, language:hsb, language:hto, language:hub, language:hui, language:hun, language:hus, language:huu, language:huv, language:hvn, language:hwc, language:hye, language:hyw, language:ian, language:iba, language:ibg, language:ibo, language:icr, language:ido, language:idu, language:ifa, language:ifb, language:ife, language:ifk, language:ifu, language:ify, language:ige, language:ign, language:ike, language:ikk, language:ikt, language:ikw, language:ilb, language:ile, language:ilo, language:imo, language:ina, language:inb, language:ind, language:inh, language:ino, language:iou, language:ipi, language:iqw, language:iri, language:irk, language:iry, language:isd, language:ish, language:isl, language:iso, language:ita, language:itv, language:ium, language:ivb, language:ivv, language:iws, language:ixl, language:izr, language:izz, language:jaa, language:jac, language:jae, language:jam, language:jav, language:jbo, language:jbu, language:jic, language:jiv, language:jmc, language:jpn, language:jra, language:jun, language:jvn, language:kaa, language:kab, language:kac, language:kak, language:kal, language:kam, language:kan, language:kao, language:kaq, language:kas, language:kat, language:kaz, language:kbc, language:kbd, language:kbh, language:kbm, language:kbo, language:kbp, language:kbq, language:kbr, language:kby, language:kca, language:kcg, language:kck, language:kdc, language:kde, language:kdh, language:kdi, language:kdj, language:kdl, language:kdr, language:kea, language:kei, language:kek, language:ken, language:keo, language:ker, language:kew, language:kez, language:kff, language:kgf, language:kgk, language:kgp, language:kgr, language:kha, language:khk, language:khm, language:khs, language:khz, language:kia, language:kij, language:kik, language:kin, language:kir, language:kiu, language:kix, language:kjb, language:kje, language:kjh, language:kjs, language:kkc, language:kki, language:kkj, language:kkl, language:kle, language:klt, language:klv, language:kmb, language:kmg, language:kmh, language:kmk, language:kmm, language:kmo, language:kmr, language:kms, language:kmu, language:kmy, language:knc, language:kne, language:knf, language:kng, language:knj, language:knk, language:kno, language:knv, language:knx, language:kny, language:kog, language:koi, language:koo, language:kor, language:kos, language:kpe, language:kpf, language:kpg, language:kpj, language:kpq, language:kpr, language:kpv, language:kpw, language:kpx, language:kpz, language:kqc, language:kqe, language:kqf, language:kql, language:kqn, language:kqo, language:kqp, language:kqs, language:kqw, language:kqy, language:krc, language:kri, language:krj, language:krl, language:kru, language:krx, language:ksb, language:ksc, language:ksd, language:ksf, language:ksh, language:ksj, language:ksp, language:ksr, language:kss, language:ksw, language:ktb, language:ktj, language:ktm, language:kto, language:ktu, language:ktz, language:kua, language:kub, language:kud, language:kue, language:kuj, language:kum, language:kup, language:kus, language:kvg, language:kvj, language:kvn, language:kwd, language:kwf, language:kwi, language:kwj, language:kwn, language:kwy, language:kxc, language:kxm, language:kxw, language:kyc, language:kyf, language:kyg, language:kyq, language:kyu, language:kyz, language:kze, language:kzf, language:kzj, language:lac, language:lad, language:lai, language:laj, language:lam, language:lao, language:lap, language:lat, language:lbb, language:lbe, language:lbj, language:lbk, language:lcm, language:lcp, language:ldi, language:ldn, language:lee, language:lef, language:leh, language:lem, language:leu, language:lew, language:lex, language:lez, language:lfn, language:lgg, language:lgl, language:lgm, language:lhi, language:lhu, language:lia, language:lid, language:lif, language:lij, language:lim, language:lin, language:lip, language:lis, language:lit, language:liv, language:ljp, language:lki, language:llb, language:lld, language:llg, language:lln, language:lmk, language:lmo, language:lmp, language:lnd, language:lob, language:loe, language:log, language:lok, language:lol, language:lom, language:loq, language:loz, language:lrc, language:lsi, language:lsm, language:ltg, language:ltz, language:lua, language:lub, language:luc, language:lud, language:lue, language:lug, language:lun, language:luo, language:lus, language:lvs, language:lwg, language:lwo, language:lww, language:lzh, language:maa, language:mad, language:maf, language:mag, language:mah, language:mai, language:maj, language:mak, language:mal, language:mam, language:maq, language:mar, language:mas, language:mau, language:mav, language:maw, language:maz, language:mbb, language:mbc, language:mbd, language:mbf, language:mbh, language:mbi, language:mbj, language:mbl, language:mbs, language:mbt, language:mca, language:mcb, language:mcd, language:mcf, language:mck, language:mcn, language:mco, language:mcp, language:mcq, language:mcu, language:mda, language:mdf, language:mdy, language:med, language:mee, language:mej, language:mek, language:men, language:meq, language:mer, language:met, language:meu, language:mev, language:mfe, language:mfg, language:mfh, language:mfi, language:mfk, language:mfq, language:mfy, language:mfz, language:mgc, language:mgh, language:mgo, language:mgr, language:mhi, language:mhl, language:mhr, language:mhw, language:mhx, language:mhy, language:mib, language:mic, language:mie, language:mif, language:mig, language:mih, language:mil, language:mim, language:min, language:mio, language:mip, language:miq, language:mir, language:mit, language:miy, language:miz, language:mjc, language:mjw, language:mkd, language:mkl, language:mkn, language:mks, language:mkz, language:mlh, language:mlp, language:mlt, language:mlu, language:mmn, language:mmo, language:mmx, language:mna, language:mnb, language:mnf, language:mni, language:mnk, language:mns, language:mnw, language:mnx, language:mny, language:moa, language:moc, language:mog, language:moh, language:mop, language:mor, language:mos, language:mox, language:mpg, language:mph, language:mpm, language:mpp, language:mps, language:mpt, language:mpx, language:mqb, language:mqj, language:mqy, language:mrg, language:mri, language:mrj, language:mrq, language:mrv, language:mrw, language:msb, language:msc, language:mse, language:msk, language:msy, language:mta, language:mtg, language:mti, language:mto, language:mtp, language:mua, language:mug, language:muh, language:mui, language:mup, language:mur, language:mus, language:mux, language:muy, language:mva, language:mvn, language:mvp, language:mwc, language:mwf, language:mwl, language:mwm, language:mwn, language:mwp, language:mwq, language:mwv, language:mww, language:mxb, language:mxp, language:mxq, language:mxt, language:mxv, language:mya, language:myb, language:myk, language:myu, language:myv, language:myw, language:myx, language:myy, language:mza, language:mzh, language:mzk, language:mzl, language:mzm, language:mzn, language:mzw, language:mzz, language:nab, language:naf, language:nah, language:nak, language:nap, language:naq, language:nas, language:nav, language:naw, language:nba, language:nbc, language:nbe, language:nbl, language:nbq, language:nbu, language:nca, language:nch, language:ncj, language:ncl, language:ncq, language:nct, language:ncu, language:ncx, language:ndc, language:nde, language:ndh, language:ndi, language:ndj, language:ndo, language:nds, language:ndz, language:neb, language:new, language:nfa, language:nfr, language:ngb, language:ngc, language:ngl, language:ngp, language:ngu, language:nhd, language:nhe, language:nhg, language:nhi, language:nhk, language:nho, language:nhr, language:nhu, language:nhw, language:nhx, language:nhy, language:nia, language:nif, language:nii, language:nij, language:nim, language:nin, language:nio, language:niu, language:niy, language:njb, language:njm, language:njn, language:njo, language:njz, language:nkf, language:nko, language:nld, language:nlg, language:nma, language:nmf, language:nmh, language:nmo, language:nmw, language:nmz, language:nnb, language:nng, language:nnh, language:nnl, language:nno, language:nnp, language:nnq, language:nnw, language:noa, language:nob, language:nod, language:nog, language:non, language:nop, language:not, language:nou, language:nov, language:nph, language:npi, language:npl, language:npo, language:npy, language:nqo, language:nre, language:nrf, language:nri, language:nrm, language:nsa, language:nse, language:nsm, language:nsn, language:nso, language:nss, language:nst, language:nsu, language:ntp, language:ntr, language:ntu, language:nuj, language:nus, language:nuy, language:nvm, language:nwb, language:nwi, language:nwx, language:nxd, language:nya, language:nyf, language:nyk, language:nyn, language:nyo, language:nyu, language:nyy, language:nza, language:nzi, language:nzm, language:obo, language:oci, language:ogo, language:ojb, language:oke, language:oku, language:okv, language:old, language:olo, language:omb, language:omw, language:ong, language:ons, language:ood, language:opm, language:orv, language:ory, language:oss, language:ota, language:otd, language:ote, language:otm, language:otn, language:oto, language:otq, language:ots, language:otw, language:oym, language:ozm, language:pab, language:pad, language:pag, language:pah, language:pam, language:pan, language:pao, language:pap, language:pau, language:pbb, language:pbc, language:pbi, language:pbt, language:pcd, language:pck, language:pcm, language:pdc, language:pdt, language:pem, language:pfe, language:pfl, language:phm, language:pib, language:pio, language:pir, language:pis, language:pjt, language:pkb, language:plg, language:pls, language:plt, language:plu, language:plw, language:pma, language:pmf, language:pmq, language:pms, language:pmx, language:pnb, language:pne, language:pnt, language:pny, language:poe, language:poh, language:poi, language:pol, language:pon, language:por, language:pos, language:pot, language:pov, language:poy, language:ppk, language:ppo, language:pps, language:prf, language:prg, language:pri, language:prq, language:pse, language:pss, language:ptp, language:ptu, language:pui, language:pwg, language:pwn, language:pww, language:pxm, language:qub, language:quc, language:quf, language:qug, language:quh, language:qul, language:qup, language:qus, language:quw, language:quy, language:quz, language:qva, language:qvc, language:qve, language:qvh, language:qvi, language:qvm, language:qvn, language:qvo, language:qvs, language:qvw, language:qvz, language:qwh, language:qxh, language:qxl, language:qxn, language:qxo, language:qxr, language:rad, language:rai, language:rap, language:rar, language:rav, language:raw, language:rcf, language:rej, language:rel, language:rgu, language:rhg, language:ria, language:rim, language:rjs, language:rkb, language:rmc, language:rme, language:rml, language:rmn, language:rmo, language:rmq, language:rmy, language:rnd, language:rng, language:rnl, language:roh, language:ron, language:roo, language:rop, language:row, language:rro, language:rtm, language:rub, language:rue, language:ruf, language:rug, language:run, language:rup, language:rus, language:rwo, language:sab, language:sag, language:sah, language:san, language:sas, language:sat, language:sba, language:sbd, language:sbe, language:sbl, language:sbs, language:sby, language:sck, language:scn, language:sco, language:sda, language:sdc, language:sdh, language:sdo, language:sdq, language:seh, language:ses, language:sey, language:sfw, language:sgb, language:sgc, language:sgh, language:sgs, language:sgw, language:sgz, language:shi, language:shk, language:shn, language:shp, language:shu, language:sid, language:sig, language:sil, language:sim, language:sin, language:sja, language:sjo, language:sju, language:skg, language:skr, language:sld, language:slk, language:sll, language:slv, language:sma, language:sme, language:smj, language:smk, language:sml, language:smn, language:smo, language:sms, language:smt, language:sna, language:snc, language:snd, language:snf, language:snn, language:snp, language:snw, language:sny, language:soe, language:som, language:sop, language:soq, language:sot, language:soy, language:spa, language:spl, language:spm, language:spp, language:sps, language:spy, language:srd, language:sri, language:srm, language:srn, language:srp, language:srq, language:srr, language:ssd, language:ssg, language:ssw, language:ssx, language:stn, language:stp, language:stq, language:sua, language:suc, language:sue, language:suk, language:sun, language:sur, language:sus, language:suz, language:swb, language:swc, language:swe, language:swg, language:swh, language:swk, language:swp, language:sxb, language:sxn, language:syb, language:syc, language:syl, language:szl, language:szy, language:tab, language:tac, language:tah, language:taj, language:tam, language:tap, language:taq, language:tar, language:tat, language:tav, language:taw, language:tay, language:tbc, language:tbg, language:tbk, language:tbl, language:tbo, language:tbw, language:tby, language:tbz, language:tca, language:tcc, language:tcf, language:tcs, language:tcy, language:tcz, language:ted, language:tee, language:tel, language:tem, language:teo, language:ter, language:tet, language:tew, language:tfr, language:tgk, language:tgo, language:tgp, language:tha, language:thk, language:thl, language:tif, language:tig, language:tih, language:tik, language:tim, language:tir, language:tiv, language:tiy, language:tke, language:tkl, language:tkr, language:tku, language:tlb, language:tlf, language:tlh, language:tlj, language:tll, language:tly, language:tmc, language:tmd, language:tna, language:tnc, language:tnk, language:tnn, language:tnp, language:tnr, language:tob, language:toc, language:tod, language:tog, language:toh, language:toi, language:toj, language:tok, language:ton, language:too, language:top, language:tos, language:tpa, language:tpi, language:tpm, language:tpp, language:tpt, language:tpw, language:tpz, language:tqo, language:trc, language:trn, language:tro, language:trp, language:trq, language:trs, language:trv, language:tsc, language:tsg, language:tsn, language:tso, language:tsw, language:tsz, language:ttc, language:tte, language:ttj, language:ttq, language:tuc, language:tue, language:tuf, language:tui, language:tuk, language:tul, language:tum, language:tuo, language:tur, language:tuv, language:tvk, language:tvl, language:twi, language:twu, language:twx, language:txq, language:txu, language:tyv, language:tzh, language:tzj, language:tzl, language:tzm, language:tzo, language:ubr, language:ubu, language:udm, language:udu, language:uig, language:ukr, language:umb, language:upv, language:ura, language:urb, language:urd, language:urh, language:uri, language:urk, language:urt, language:urw, language:ury, language:usa, language:usp, language:uth, language:uvh, language:uvl, language:uzn, language:uzs, language:vag, language:vap, language:var, language:vec, language:ven, language:vep, language:vid, language:vie, language:viv, language:vls, language:vmk, language:vmw, language:vmy, language:vol, language:vot, language:vro, language:vun, language:vut, language:waj, language:wal, language:wap, language:war, language:wat, language:way, language:wba, language:wbm, language:wbp, language:wed, language:wer, language:wes, language:wew, language:whg, language:whk, language:wib, language:wim, language:wiu, language:wln, language:wls, language:wlv, language:wlx, language:wmt, language:wmw, language:wnc, language:wnu, language:wob, language:wol, language:wos, language:wrk, language:wrs, language:wsg, language:wsk, language:wuu, language:wuv, language:wwa, language:xal, language:xav, language:xbi, language:xbr, language:xed, language:xho, language:xla, language:xmf, language:xmm, language:xmv, language:xnn, language:xog, language:xon, language:xrb, language:xsb, language:xsi, language:xsm, language:xsr, language:xsu, language:xtd, language:xtm, language:xtn, language:xuo, language:yaa, language:yad, language:yal, language:yam, language:yan, language:yao, language:yap, language:yaq, language:yat, language:yaz, language:ybb, language:yby, language:ycn, language:ydd, language:yim, language:yka, language:yle, language:yli, language:yml, language:yom, language:yon, language:yor, language:yrb, language:yre, language:yrk, language:yrl, language:yss, language:yua, language:yue, language:yuj, language:yup, language:yut, language:yuw, language:yuz, language:yva, language:zaa, language:zab, language:zac, language:zad, language:zae, language:zai, language:zam, language:zao, language:zar, language:zas, language:zat, language:zav, language:zaw, language:zca, language:zdj, language:zea, language:zgh, language:zia, language:ziw, language:zne, language:zom, language:zos, language:zpa, language:zpc, language:zpg, language:zpi, language:zpj, language:zpl, language:zpm, language:zpo, language:zpq, language:zpt, language:zpu, language:zpv, language:zpz, language:zsm, language:zsr, language:ztq, language:zty, language:zul, language:zyb, language:zyp, license:odc-by, size_categories:1B<n<10B, modality:tabular, modality:text, arxiv:2506.20920, arxiv:2109.07445, arxiv:2406.17557, doi:10.57967/hf/3744, region:us
- **License:** Not specified

## 📖 Description
```text
🥂 FineWeb2
	


    



A sparkling update with 1000s of languages


	
		
		What is it?
	

This is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.
The 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.
In particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/aai_Latn/test/000_00000.parquet`
- `data/aai_Latn/train/000_00000.parquet`
- `data/aai_Latn_removed/train/000_00000.parquet`
- `data/aak_Latn/test/000_00000.parquet`
- `data/aak_Latn/train/000_00000.parquet`
- `data/aak_Latn_removed/train/000_00000.parquet`
- `data/aau_Latn/test/000_00000.parquet`
- `data/aau_Latn/train/000_00000.parquet`
- ... and more.

## 📊 Data Structure
### Config: `aai_Latn`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |

### Config: `aak_Latn`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |

### Config: `aau_Latn`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `language` | `str` |
| `language_score` | `float` |
| `language_script` | `str` |
| `minhash_cluster_size` | `int` |
| `top_langs` | `str` |


key: chroma:document
string_value:
# Dataset: `BAAI/Infinity-Instruct`

## 📝 Metadata
- **Author:** BAAI
- **Downloads:** 2673
- **Likes:** 670
- **Tags:** task_categories:text-generation, language:en, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.11116, arxiv:2402.00530, arxiv:2405.19327, arxiv:2409.07045, arxiv:2408.07089, region:us
- **License:** Not specified

## 📖 Description
```text
Infinity Instruct
	





Beijing Academy of Artificial Intelligence (BAAI)
 [Paper][Code][🤗] 


The quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and high-quality instruction… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct....
```

## 📂 File System Sample
- `.gitattributes`
- `0625/train-00000-of-00007.parquet`
- `0625/train-00001-of-00007.parquet`
- `0625/train-00002-of-00007.parquet`
- `0625/train-00003-of-00007.parquet`
- `0625/train-00004-of-00007.parquet`
- `0625/train-00005-of-00007.parquet`
- `0625/train-00006-of-00007.parquet`
- `3M/train-00000-of-00035.parquet`
- `3M/train-00001-of-00035.parquet`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'BAAI/Infinity-Instruct' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/BAAI/Infinity-Instruct to ask for access.
```


key: chroma:document
string_value:
# Dataset: `open-r1/OpenR1-Math-220k`

## 📝 Metadata
- **Author:** open-r1
- **Downloads:** 7623
- **Likes:** 658
- **Tags:** language:en, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us
- **License:** Not specified

## 📖 Description
```text
OpenR1-Math-220k
	


	
		
		Dataset description
	

OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5. 
The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.
The dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `all/default-00000-of-00010.parquet`
- `all/default-00001-of-00010.parquet`
- `all/default-00002-of-00010.parquet`
- `all/default-00003-of-00010.parquet`
- `all/default-00004-of-00010.parquet`
- `all/default-00005-of-00010.parquet`
- `all/default-00006-of-00010.parquet`
- `all/default-00007-of-00010.parquet`
- ... and more.

## 📊 Data Structure
### Config: `all`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `problem` | `str` |
| `solution` | `str` |
| `answer` | `str` |
| `problem_type` | `str` |
| `question_type` | `str` |
| `source` | `str` |
| `uuid` | `str` |
| `is_reasoning_complete` | `list` |
| `generations` | `list` |
| `correctness_math_verify` | `list` |
| `correctness_llama` | `list` |
| `finish_reasons` | `NoneType` |
| `correctness_count` | `int` |
| `messages` | `list` |

### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `problem` | `str` |
| `solution` | `str` |
| `answer` | `str` |
| `problem_type` | `str` |
| `question_type` | `str` |
| `source` | `str` |
| `uuid` | `str` |
| `is_reasoning_complete` | `list` |
| `generations` | `list` |
| `correctness_math_verify` | `list` |
| `correctness_llama` | `NoneType` |
| `finish_reasons` | `NoneType` |
| `correctness_count` | `int` |
| `messages` | `list` |

### Config: `extended`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `problem` | `str` |
| `solution` | `str` |
| `answer` | `str` |
| `problem_type` | `str` |
| `question_type` | `str` |
| `source` | `str` |
| `uuid` | `str` |
| `is_reasoning_complete` | `list` |
| `generations` | `list` |
| `correctness_math_verify` | `list` |
| `correctness_llama` | `NoneType` |
| `finish_reasons` | `NoneType` |
| `correctness_count` | `int` |
| `messages` | `list` |


key: chroma:document
string_value:
# Dataset: `m-a-p/COIG-CQIA`

## 📝 Metadata
- **Author:** m-a-p
- **Downloads:** 4219
- **Likes:** 654
- **Tags:** task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.18058, arxiv:2304.07987, arxiv:2307.09705, region:us
- **License:** Not specified

## 📖 Description
```text
COIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning
	





	
		
		Dataset Details
	


	
		
		Dataset Description
	


欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源的高质量指令微调数据集，旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。COIG-CQIA以中文互联网获取到的问答及文章作为原始数据，经过深度清洗、重构及人工审核构建而成。本项目受LIMA: Less Is More for Alignment等研究启发，使用少量高质量的数据即可让大语言模型学习到人类交互行为，因此在数据构建中我们十分注重数据的来源、质量与多样性，数据集详情请见数据介绍以及我们接下来的论文。
Welcome to the COIG-CQIA… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/COIG-CQIA....
```

## 📂 File System Sample
- `.gitattributes`
- `COIG-CQIA-full.jsonl`
- `README.md`
- `Yi_logo.svg`
- `chinese_traditional/chengyu.jsonl`
- `chinese_traditional/poem.jsonl`
- `chinese_traditional/trad-multi-choice-100-2.jsonl`
- `chinese_traditional/trad-multi-choice-100.jsonl`
- `chinese_traditional/trad-multi-choice-40.jsonl`
- `chinese_traditional/translate_classical_chinese.jsonl`
- ... and more.

## 📊 Data Structure
### Config: `chinese_traditional`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
| `task_type` | `dict` |
| `domain` | `list` |
| `metadata` | `str` |
| `answer_from` | `str` |
| `human_verified` | `bool` |
| `copyright` | `str` |

### Config: `coig_pc`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
| `task_type` | `dict` |
| `domain` | `list` |
| `metadata` | `str` |
| `answer_from` | `str` |
| `human_verified` | `bool` |
| `copyright` | `str` |

### Config: `exam`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `instruction` | `str` |
| `input` | `str` |
| `output` | `str` |
| `task_type` | `dict` |
| `domain` | `list` |
| `metadata` | `str` |
| `answer_from` | `str` |
| `human_verified` | `bool` |
| `copyright` | `str` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceTB/cosmopedia`

## 📝 Metadata
- **Author:** HuggingFaceTB
- **Downloads:** 37803
- **Likes:** 642
- **Tags:** language:en, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.05463, arxiv:2306.11644, region:us, synthetic
- **License:** Not specified

## 📖 Description
```text
Cosmopedia v0.1
	


    
    Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1


Note: Cosmopedia v0.2 is available at smollm-corpus
User: What do you think "Cosmopedia" could mean? Hint: in our case it's not related to cosmology.

Mixtral-8x7B-Instruct-v0.1: A possible meaning for "Cosmopedia" could be an encyclopedia or collection of information about
different cultures, societies, and topics from around the world, emphasizing diversity and global… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/auto_math_text/train-00000-of-00018.parquet`
- `data/auto_math_text/train-00001-of-00018.parquet`
- `data/auto_math_text/train-00002-of-00018.parquet`
- `data/auto_math_text/train-00003-of-00018.parquet`
- `data/auto_math_text/train-00004-of-00018.parquet`
- `data/auto_math_text/train-00005-of-00018.parquet`
- `data/auto_math_text/train-00006-of-00018.parquet`
- `data/auto_math_text/train-00007-of-00018.parquet`
- ... and more.

## 📊 Data Structure
### Config: `auto_math_text`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `text_token_length` | `int` |
| `text` | `str` |
| `seed_data` | `str` |
| `format` | `str` |
| `audience` | `str` |

### Config: `khanacademy`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `text_token_length` | `int` |
| `text` | `str` |
| `seed_data` | `str` |
| `format` | `str` |
| `audience` | `str` |

### Config: `openstax`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text_token_length` | `int` |
| `prompt` | `str` |
| `text` | `str` |
| `seed_data` | `str` |
| `format` | `str` |
| `audience` | `str` |


key: chroma:document
string_value:
# Dataset: `proj-persona/PersonaHub`

## 📝 Metadata
- **Author:** proj-persona
- **Downloads:** 12977
- **Likes:** 642
- **Tags:** task_categories:text-generation, task_categories:text-classification, task_categories:token-classification, task_categories:fill-mask, task_categories:table-question-answering, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.20094, region:us, synthetic, text, math, reasoning, instruction, tool, persona
- **License:** Not specified

## 📖 Description
```text
Scaling Synthetic Data Creation with 1,000,000,000 Personas
	

This repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas:
We propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web data.… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub....
```

## 📂 File System Sample
- `.gitattributes`
- `ElitePersonas/elite_personas.part1.jsonl`
- `ElitePersonas/elite_personas.part10.jsonl`
- `ElitePersonas/elite_personas.part11.jsonl`
- `ElitePersonas/elite_personas.part12.jsonl`
- `ElitePersonas/elite_personas.part13.jsonl`
- `ElitePersonas/elite_personas.part14.jsonl`
- `ElitePersonas/elite_personas.part15.jsonl`
- `ElitePersonas/elite_personas.part16.jsonl`
- `ElitePersonas/elite_personas.part17.jsonl`
- ... and more.

## 📊 Data Structure
### Config: `math`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `input persona` | `str` |
| `synthesized text` | `str` |
| `description` | `str` |

### Config: `instruction`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `input persona` | `str` |
| `synthesized text` | `str` |
| `description` | `str` |

### Config: `reasoning`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `input persona` | `str` |
| `synthesized text` | `str` |
| `description` | `str` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceFW/finepdfs`

## 📝 Metadata
- **Author:** HuggingFaceFW
- **Downloads:** 46934
- **Likes:** 630
- **Tags:** task_categories:text-generation, language:aai, language:aak, language:aau, language:aaz, language:aba, language:abi, language:abk, language:abn, language:abq, language:abs, language:abt, language:abx, language:aby, language:abz, language:aca, language:acd, language:ace, language:acf, language:ach, language:acm, language:acn, language:acr, language:acu, language:ada, language:ade, language:adh, language:adi, language:adj, language:adl, language:ady, language:adz, language:aeb, language:aer, language:aeu, language:aey, language:afr, language:agd, language:agg, language:agm, language:agn, language:agr, language:agt, language:agu, language:agw, language:agx, language:aha, language:ahk, language:aia, language:aii, language:aim, language:ain, language:ajg, language:aji, language:ajz, language:akb, language:ake, language:akh, language:akp, language:alj, language:aln, language:alp, language:alq, language:als, language:alt, language:aly, language:alz, language:ame, language:amf, language:amh, language:ami, language:amk, language:amm, language:amn, language:amp, language:amr, language:amu, language:amx, language:ang, language:anm, language:ann, language:anp, language:anv, language:any, language:aoi, language:aoj, language:aom, language:aoz, language:apb, language:apc, language:ape, language:apn, language:apr, language:apt, language:apu, language:apw, language:apy, language:apz, language:arb, language:are, language:arg, language:arl, language:arn, language:arp, language:arq, language:ars, language:ary, language:arz, language:asg, language:asm, language:aso, language:ast, language:ata, language:atb, language:atd, language:atg, language:ati, language:atj, language:atq, language:att, language:auc, language:aui, language:auy, language:ava, language:avk, language:avn, language:avt, language:avu, language:awa, language:awb, language:awx, language:ayo, language:ayp, language:ayr, language:azb, language:azg, language:azj, language:azz, language:bak, language:bam, language:ban, language:bao, language:bar, language:bas, language:bav, language:bba, language:bbb, language:bbc, language:bbj, language:bbk, language:bbo, language:bbr, language:bch, language:bci, language:bcl, language:bco, language:bcw, language:bdd, language:bdh, language:bdq, language:bea, language:bef, language:bel, language:bem, language:ben, language:beq, language:bew, language:bex, language:bfd, language:bfo, language:bgr, language:bgs, language:bgt, language:bgz, language:bhg, language:bhl, language:bho, language:bhp, language:bhw, language:bhz, language:bib, language:big, language:bim, language:bin, language:bis, language:biu, language:biv, language:bjn, language:bjp, language:bjr, language:bjv, language:bkd, language:bkl, language:bkq, language:bku, language:bkv, language:bla, language:blh, language:blk, language:blw, language:blz, language:bmh, language:bmk, language:bmq, language:bmr, language:bmu, language:bmv, language:bno, language:bnp, language:boa, language:bod, language:boj, language:bom, language:bon, language:bos, language:bov, language:box, language:bpr, language:bps, language:bpy, language:bqc, language:bqj, language:bqp, language:bre, language:brh, language:bru, language:brx, language:bsc, language:bsn, language:bsp, language:bsq, language:bss, language:btd, language:bth, language:bts, language:btt, language:btx, language:bud, language:bug, language:buk, language:bul, language:bum, language:bus, language:bvc, language:bvd, language:bvr, language:bvz, language:bwd, language:bwi, language:bwq, language:bwu, language:bxh, language:bxr, language:byr, language:byv, language:byx, language:bzd, language:bzh, language:bzi, language:bzj, language:caa, language:cab, language:cac, language:caf, language:cag, language:cak, language:cao, language:cap, language:caq, language:car, language:cas, language:cat, language:cav, language:cax, language:cbc, language:cbi, language:cbk, language:cbr, language:cbs, language:cbt, language:cbu, language:cbv, language:cce, language:cco, language:ccp, language:ceb, language:ceg, language:cek, language:ces, language:cfm, language:cgc, language:cgg, language:cha, language:chd, language:che, language:chf, language:chj, language:chk, language:cho, language:chq, language:chr, language:chu, language:chv, language:chw, language:chz, language:cjk, language:cjo, language:cjp, language:cjs, language:cjv, language:ckb, language:cko, language:ckt, language:cle, language:clu, language:cly, language:cme, language:cmn, language:cmo, language:cmr, language:cnh, language:cni, language:cnk, language:cnl, language:cnt, language:cnw, language:coe, language:cof, language:cok, language:con, language:cop, language:cor, language:cos, language:cot, language:cou, language:cpa, language:cpb, language:cpc, language:cpu, language:cpy, language:crh, language:crj, language:crk, language:crl, language:crm, language:crn, language:crs, language:crt, language:crx, language:csb, language:csk, language:cso, language:csw, language:csy, language:cta, language:ctd, language:cto, language:ctp, language:ctu, language:cub, language:cuc, language:cui, language:cuk, language:cul, language:cut, language:cux, language:cwe, language:cwt, language:cya, language:cym, language:czt, language:daa, language:dad, language:daf, language:dag, language:dah, language:dak, language:dan, language:dar, language:ddg, language:ddn, language:ded, language:des, language:deu, language:dga, language:dgc, language:dgi, language:dgr, language:dgz, language:dhg, language:dhm, language:dhv, language:did, language:dig, language:dik, language:diq, language:dis, language:diu, language:div, language:dje, language:djk, language:djr, language:dks, language:dln, language:dng, language:dnj, language:dnw, language:dob, language:doi, language:dop, language:dos, language:dow, language:drg, language:dru, language:dsb, language:dtb, language:dtp, language:dts, language:dty, language:dua, language:due, language:dug, language:duo, language:dur, language:dwr, language:dww, language:dyi, language:dyo, language:dyu, language:dzo, language:ebk, language:efi, language:eka, language:ekk, language:eko, language:ell, language:emi, language:eml, language:emp, language:enb, language:enl, language:enm, language:enq, language:enx, language:epo, language:eri, language:ese, language:esi, language:esk, language:ess, language:esu, language:eto, language:etr, language:etu, language:eus, language:eve, language:ewe, language:ewo, language:ext, language:eza, language:faa, language:fad, language:fai, language:fal, language:fan, language:fao, language:far, language:fas, language:fat, language:ffm, language:fij, language:fil, language:fin, language:fit, language:fkv, language:fmu, language:fon, language:for, language:fra, language:frd, language:fro, language:frp, language:frr, language:fry, language:fub, language:fud, language:fue, language:fuf, language:fuh, language:fuq, language:fur, language:fuv, language:gaa, language:gag, language:gah, language:gai, language:gam, language:gaw, language:gaz, language:gbi, language:gbo, language:gbr, language:gcf, language:gcr, language:gde, language:gdg, language:gdn, language:gdr, language:geb, language:gej, language:gfk, language:ghs, language:gid, language:gil, language:giz, language:gjn, language:gkn, language:gla, language:gle, language:glg, language:glk, language:glv, language:gmh, language:gmv, language:gna, language:gnb, language:gnd, language:gng, language:gnn, language:gnw, language:goa, language:gof, language:gog, language:goh, language:gom, language:gor, language:gos, language:got, language:gqr, language:grc, language:grt, language:gso, language:gsw, language:gub, language:guc, language:gud, language:gug, language:guh, language:gui, language:guj, language:guk, language:gul, language:gum, language:gun, language:guo, language:guq, language:gur, language:guu, language:guw, language:gux, language:guz, language:gvc, language:gvf, language:gvl, language:gvn, language:gwi, language:gwr, language:gya, language:gym, language:gyr, language:hac, language:hae, language:hag, language:hak, language:hat, language:hav, language:haw, language:hay, language:hbo, language:hch, language:heb, language:heg, language:heh, language:her, language:hif, language:hig, language:hil, language:hin, language:hix, language:hla, language:hmo, language:hmr, language:hne, language:hnj, language:hnn, language:hns, language:hop, language:hot, language:hra, language:hrv, language:hrx, language:hsb, language:hto, language:hub, language:hui, language:hun, language:hus, language:huu, language:huv, language:hvn, language:hwc, language:hye, language:hyw, language:ian, language:iba, language:ibg, language:ibo, language:icr, language:ido, language:idu, language:ifa, language:ifb, language:ife, language:ifk, language:ifu, language:ify, language:ige, language:ign, language:ike, language:ikk, language:ikt, language:ikw, language:ilb, language:ile, language:ilo, language:imo, language:ina, language:inb, language:ind, language:inh, language:ino, language:iou, language:ipi, language:iqw, language:iri, language:irk, language:iry, language:isd, language:ish, language:isl, language:iso, language:ita, language:itv, language:ium, language:ivb, language:ivv, language:iws, language:ixl, language:izr, language:izz, language:jaa, language:jac, language:jae, language:jam, language:jav, language:jbo, language:jbu, language:jic, language:jiv, language:jmc, language:jpn, language:jra, language:jun, language:jvn, language:kaa, language:kab, language:kac, language:kak, language:kal, language:kam, language:kan, language:kao, language:kaq, language:kas, language:kat, language:kaz, language:kbc, language:kbd, language:kbh, language:kbm, language:kbo, language:kbp, language:kbq, language:kbr, language:kby, language:kca, language:kcg, language:kck, language:kdc, language:kde, language:kdh, language:kdi, language:kdj, language:kdl, language:kdr, language:kea, language:kei, language:kek, language:ken, language:keo, language:ker, language:kew, language:kez, language:kff, language:kgf, language:kgk, language:kgp, language:kgr, language:kha, language:khk, language:khm, language:khs, language:khz, language:kia, language:kij, language:kik, language:kin, language:kir, language:kiu, language:kix, language:kjb, language:kje, language:kjh, language:kjs, language:kkc, language:kki, language:kkj, language:kkl, language:kle, language:klt, language:klv, language:kmb, language:kmg, language:kmh, language:kmk, language:kmm, language:kmo, language:kmr, language:kms, language:kmu, language:kmy, language:knc, language:kne, language:knf, language:kng, language:knj, language:knk, language:kno, language:knv, language:knx, language:kny, language:kog, language:koi, language:koo, language:kor, language:kos, language:kpe, language:kpf, language:kpg, language:kpj, language:kpq, language:kpr, language:kpv, language:kpw, language:kpx, language:kpz, language:kqc, language:kqe, language:kqf, language:kql, language:kqn, language:kqo, language:kqp, language:kqs, language:kqw, language:kqy, language:krc, language:kri, language:krj, language:krl, language:kru, language:krx, language:ksb, language:ksc, language:ksd, language:ksf, language:ksh, language:ksj, language:ksp, language:ksr, language:kss, language:ksw, language:ktb, language:ktj, language:ktm, language:kto, language:ktu, language:ktz, language:kua, language:kub, language:kud, language:kue, language:kuj, language:kum, language:kup, language:kus, language:kvg, language:kvj, language:kvn, language:kwd, language:kwf, language:kwi, language:kwj, language:kwn, language:kwy, language:kxc, language:kxm, language:kxw, language:kyc, language:kyf, language:kyg, language:kyq, language:kyu, language:kyz, language:kze, language:kzf, language:kzj, language:lac, language:lad, language:lai, language:laj, language:lam, language:lao, language:lap, language:lat, language:lbb, language:lbe, language:lbj, language:lbk, language:lcm, language:lcp, language:ldi, language:ldn, language:lee, language:lef, language:leh, language:lem, language:leu, language:lew, language:lex, language:lez, language:lfn, language:lgg, language:lgl, language:lgm, language:lhi, language:lhu, language:lia, language:lid, language:lif, language:lij, language:lim, language:lin, language:lip, language:lis, language:lit, language:liv, language:ljp, language:lki, language:llb, language:lld, language:llg, language:lln, language:lmk, language:lmo, language:lmp, language:lnd, language:lob, language:loe, language:log, language:lok, language:lol, language:lom, language:loq, language:loz, language:lrc, language:lsi, language:lsm, language:ltg, language:ltz, language:lua, language:lub, language:luc, language:lud, language:lue, language:lug, language:lun, language:luo, language:lus, language:lvs, language:lwg, language:lwo, language:lww, language:lzh, language:maa, language:mad, language:maf, language:mag, language:mah, language:mai, language:maj, language:mak, language:mal, language:mam, language:maq, language:mar, language:mas, language:mau, language:mav, language:maw, language:maz, language:mbb, language:mbc, language:mbd, language:mbf, language:mbh, language:mbi, language:mbj, language:mbl, language:mbs, language:mbt, language:mca, language:mcb, language:mcd, language:mcf, language:mck, language:mcn, language:mco, language:mcp, language:mcq, language:mcu, language:mda, language:mdf, language:mdy, language:med, language:mee, language:mej, language:mek, language:men, language:meq, language:mer, language:met, language:meu, language:mev, language:mfe, language:mfg, language:mfh, language:mfi, language:mfk, language:mfq, language:mfy, language:mfz, language:mgc, language:mgh, language:mgo, language:mgr, language:mhi, language:mhl, language:mhr, language:mhw, language:mhx, language:mhy, language:mib, language:mic, language:mie, language:mif, language:mig, language:mih, language:mil, language:mim, language:min, language:mio, language:mip, language:miq, language:mir, language:mit, language:miy, language:miz, language:mjc, language:mjw, language:mkd, language:mkl, language:mkn, language:mks, language:mkz, language:mlh, language:mlp, language:mlt, language:mlu, language:mmn, language:mmo, language:mmx, language:mna, language:mnb, language:mnf, language:mni, language:mnk, language:mns, language:mnw, language:mnx, language:mny, language:moa, language:moc, language:mog, language:moh, language:mop, language:mor, language:mos, language:mox, language:mpg, language:mph, language:mpm, language:mpp, language:mps, language:mpt, language:mpx, language:mqb, language:mqj, language:mqy, language:mrg, language:mri, language:mrj, language:mrq, language:mrv, language:mrw, language:msb, language:msc, language:mse, language:msk, language:msy, language:mta, language:mtg, language:mti, language:mto, language:mtp, language:mua, language:mug, language:muh, language:mui, language:mup, language:mur, language:mus, language:mux, language:muy, language:mva, language:mvn, language:mvp, language:mwc, language:mwf, language:mwl, language:mwm, language:mwn, language:mwp, language:mwq, language:mwv, language:mww, language:mxb, language:mxp, language:mxq, language:mxt, language:mxv, language:mya, language:myb, language:myk, language:myu, language:myv, language:myw, language:myx, language:myy, language:mza, language:mzh, language:mzk, language:mzl, language:mzm, language:mzn, language:mzw, language:mzz, language:nab, language:naf, language:nah, language:nak, language:nap, language:naq, language:nas, language:nav, language:naw, language:nba, language:nbc, language:nbe, language:nbl, language:nbq, language:nbu, language:nca, language:nch, language:ncj, language:ncl, language:ncq, language:nct, language:ncu, language:ncx, language:ndc, language:nde, language:ndh, language:ndi, language:ndj, language:ndo, language:nds, language:ndz, language:neb, language:new, language:nfa, language:nfr, language:ngb, language:ngc, language:ngl, language:ngp, language:ngu, language:nhd, language:nhe, language:nhg, language:nhi, language:nhk, language:nho, language:nhr, language:nhu, language:nhw, language:nhx, language:nhy, language:nia, language:nif, language:nii, language:nij, language:nim, language:nin, language:nio, language:niu, language:niy, language:njb, language:njm, language:njn, language:njo, language:njz, language:nkf, language:nko, language:nld, language:nlg, language:nma, language:nmf, language:nmh, language:nmo, language:nmw, language:nmz, language:nnb, language:nng, language:nnh, language:nnl, language:nno, language:nnp, language:nnq, language:nnw, language:noa, language:nob, language:nod, language:nog, language:non, language:nop, language:not, language:nou, language:nov, language:nph, language:npi, language:npl, language:npo, language:npy, language:nqo, language:nre, language:nrf, language:nri, language:nrm, language:nsa, language:nse, language:nsm, language:nsn, language:nso, language:nss, language:nst, language:nsu, language:ntp, language:ntr, language:ntu, language:nuj, language:nus, language:nuy, language:nvm, language:nwb, language:nwi, language:nwx, language:nxd, language:nya, language:nyf, language:nyk, language:nyn, language:nyo, language:nyu, language:nyy, language:nza, language:nzi, language:nzm, language:obo, language:oci, language:ogo, language:ojb, language:oke, language:oku, language:okv, language:old, language:olo, language:omb, language:omw, language:ong, language:ons, language:ood, language:opm, language:orv, language:ory, language:oss, language:ota, language:otd, language:ote, language:otm, language:otn, language:oto, language:otq, language:ots, language:otw, language:oym, language:ozm, language:pab, language:pad, language:pag, language:pah, language:pam, language:pan, language:pao, language:pap, language:pau, language:pbb, language:pbc, language:pbi, language:pbt, language:pcd, language:pck, language:pcm, language:pdc, language:pdt, language:pem, language:pfe, language:pfl, language:phm, language:pib, language:pio, language:pir, language:pis, language:pjt, language:pkb, language:plg, language:pls, language:plt, language:plu, language:plw, language:pma, language:pmf, language:pmq, language:pms, language:pmx, language:pnb, language:pne, language:pnt, language:pny, language:poe, language:poh, language:poi, language:pol, language:pon, language:por, language:pos, language:pot, language:pov, language:poy, language:ppk, language:ppo, language:pps, language:prf, language:prg, language:pri, language:prq, language:pse, language:pss, language:ptp, language:ptu, language:pui, language:pwg, language:pwn, language:pww, language:pxm, language:qub, language:quc, language:quf, language:qug, language:quh, language:qul, language:qup, language:qus, language:quw, language:quy, language:quz, language:qva, language:qvc, language:qve, language:qvh, language:qvi, language:qvm, language:qvn, language:qvo, language:qvs, language:qvw, language:qvz, language:qwh, language:qxh, language:qxl, language:qxn, language:qxo, language:qxr, language:rad, language:rai, language:rap, language:rar, language:rav, language:raw, language:rcf, language:rej, language:rel, language:rgu, language:rhg, language:ria, language:rim, language:rjs, language:rkb, language:rmc, language:rme, language:rml, language:rmn, language:rmo, language:rmq, language:rmy, language:rnd, language:rng, language:rnl, language:roh, language:ron, language:roo, language:rop, language:row, language:rro, language:rtm, language:rub, language:rue, language:ruf, language:rug, language:run, language:rup, language:rus, language:rwo, language:sab, language:sag, language:sah, language:san, language:sas, language:sat, language:sba, language:sbd, language:sbe, language:sbl, language:sbs, language:sby, language:sck, language:scn, language:sco, language:sda, language:sdc, language:sdh, language:sdo, language:sdq, language:seh, language:ses, language:sey, language:sfw, language:sgb, language:sgc, language:sgh, language:sgs, language:sgw, language:sgz, language:shi, language:shk, language:shn, language:shp, language:shu, language:sid, language:sig, language:sil, language:sim, language:sin, language:sja, language:sjo, language:sju, language:skg, language:skr, language:sld, language:slk, language:sll, language:slv, language:sma, language:sme, language:smj, language:smk, language:sml, language:smn, language:smo, language:sms, language:smt, language:sna, language:snc, language:snd, language:snf, language:snn, language:snp, language:snw, language:sny, language:soe, language:som, language:sop, language:soq, language:sot, language:soy, language:spa, language:spl, language:spm, language:spp, language:sps, language:spy, language:srd, language:sri, language:srm, language:srn, language:srp, language:srq, language:srr, language:ssd, language:ssg, language:ssw, language:ssx, language:stn, language:stp, language:stq, language:sua, language:suc, language:sue, language:suk, language:sun, language:sur, language:sus, language:suz, language:swb, language:swc, language:swe, language:swg, language:swh, language:swk, language:swp, language:sxb, language:sxn, language:syb, language:syc, language:syl, language:szl, language:szy, language:tab, language:tac, language:tah, language:taj, language:tam, language:tap, language:taq, language:tar, language:tat, language:tav, language:taw, language:tay, language:tbc, language:tbg, language:tbk, language:tbl, language:tbo, language:tbw, language:tby, language:tbz, language:tca, language:tcc, language:tcf, language:tcs, language:tcy, language:tcz, language:ted, language:tee, language:tel, language:tem, language:teo, language:ter, language:tet, language:tew, language:tfr, language:tgk, language:tgo, language:tgp, language:tha, language:thk, language:thl, language:tif, language:tig, language:tih, language:tik, language:tim, language:tir, language:tiv, language:tiy, language:tke, language:tkl, language:tkr, language:tku, language:tlb, language:tlf, language:tlh, language:tlj, language:tll, language:tly, language:tmc, language:tmd, language:tna, language:tnc, language:tnk, language:tnn, language:tnp, language:tnr, language:tob, language:toc, language:tod, language:tog, language:toh, language:toi, language:toj, language:tok, language:ton, language:too, language:top, language:tos, language:tpa, language:tpi, language:tpm, language:tpp, language:tpt, language:tpw, language:tpz, language:tqo, language:trc, language:trn, language:tro, language:trp, language:trq, language:trs, language:trv, language:tsc, language:tsg, language:tsn, language:tso, language:tsw, language:tsz, language:ttc, language:tte, language:ttj, language:ttq, language:tuc, language:tue, language:tuf, language:tui, language:tuk, language:tul, language:tum, language:tuo, language:tur, language:tuv, language:tvk, language:tvl, language:twi, language:twu, language:twx, language:txq, language:txu, language:tyv, language:tzh, language:tzj, language:tzl, language:tzm, language:tzo, language:ubr, language:ubu, language:udm, language:udu, language:uig, language:ukr, language:umb, language:upv, language:ura, language:urb, language:urd, language:urh, language:uri, language:urk, language:urt, language:urw, language:ury, language:usa, language:usp, language:uth, language:uvh, language:uvl, language:uzn, language:uzs, language:vag, language:vap, language:var, language:vec, language:ven, language:vep, language:vid, language:vie, language:viv, language:vls, language:vmk, language:vmw, language:vmy, language:vol, language:vot, language:vro, language:vun, language:vut, language:waj, language:wal, language:wap, language:war, language:wat, language:way, language:wba, language:wbm, language:wbp, language:wed, language:wer, language:wes, language:wew, language:whg, language:whk, language:wib, language:wim, language:wiu, language:wln, language:wls, language:wlv, language:wlx, language:wmt, language:wmw, language:wnc, language:wnu, language:wob, language:wol, language:wos, language:wrk, language:wrs, language:wsg, language:wsk, language:wuu, language:wuv, language:wwa, language:xal, language:xav, language:xbi, language:xbr, language:xed, language:xho, language:xla, language:xmf, language:xmm, language:xmv, language:xnn, language:xog, language:xon, language:xrb, language:xsb, language:xsi, language:xsm, language:xsr, language:xsu, language:xtd, language:xtm, language:xtn, language:xuo, language:yaa, language:yad, language:yal, language:yam, language:yan, language:yao, language:yap, language:yaq, language:yat, language:yaz, language:ybb, language:yby, language:ycn, language:ydd, language:yim, language:yka, language:yle, language:yli, language:yml, language:yom, language:yon, language:yor, language:yrb, language:yre, language:yrk, language:yrl, language:yss, language:yua, language:yue, language:yuj, language:yup, language:yut, language:yuw, language:yuz, language:yva, language:zaa, language:zab, language:zac, language:zad, language:zae, language:zai, language:zam, language:zao, language:zar, language:zas, language:zat, language:zav, language:zaw, language:zca, language:zdj, language:zea, language:zgh, language:zia, language:ziw, language:zne, language:zom, language:zos, language:zpa, language:zpc, language:zpg, language:zpi, language:zpj, language:zpl, language:zpm, language:zpo, language:zpq, language:zpt, language:zpu, language:zpv, language:zpz, language:zsm, language:zsr, language:ztq, language:zty, language:zul, language:zyb, language:zyp, license:odc-by, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.18421, arxiv:2109.07445, region:us
- **License:** Not specified

## 📖 Description
```text
Liberating 3T of the finest tokens from PDFs


	
		
		What is this?
	

As we run out of web pages to process, the natural question has always been: what to do next? Only a few knew about a data source that everyone avoided for ages, due to its incredible extraction cost and complexity: PDFs.
📄 FinePDFs is exactly that. It is the largest publicly available corpus sourced exclusively from PDFs, containing about 3 trillion tokens across 475 million documents in 1733 languages.
Compared to HTML… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finepdfs....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/aai_Latn/train/000_00000.parquet`
- `data/aak_Latn/train/000_00000.parquet`
- `data/aau_Latn/train/000_00000.parquet`
- `data/aaz_Latn/train/000_00000.parquet`
- `data/aba_Latn/train/000_00000.parquet`
- `data/abk_Cyrl/test/000_00000.parquet`
- `data/abk_Cyrl/train/000_00000.parquet`
- `data/abq_Cyrl/train/000_00000.parquet`
- ... and more.

## 📊 Data Structure
### Config: `aai_Latn`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `offset` | `int` |
| `token_count` | `int` |
| `language` | `str` |
| `page_average_lid` | `str` |
| `page_average_lid_score` | `float` |
| `full_doc_lid` | `str` |
| `full_doc_lid_score` | `float` |
| `per_page_languages` | `list` |
| `is_truncated` | `bool` |
| `extractor` | `str` |
| `page_ends` | `list` |

### Config: `aak_Latn`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `offset` | `int` |
| `token_count` | `int` |
| `language` | `str` |
| `page_average_lid` | `str` |
| `page_average_lid_score` | `float` |
| `full_doc_lid` | `str` |
| `full_doc_lid_score` | `float` |
| `per_page_languages` | `list` |
| `is_truncated` | `bool` |
| `extractor` | `str` |
| `page_ends` | `list` |

### Config: `aau_Latn`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
| `id` | `str` |
| `dump` | `str` |
| `url` | `str` |
| `date` | `str` |
| `file_path` | `str` |
| `offset` | `int` |
| `token_count` | `int` |
| `language` | `str` |
| `page_average_lid` | `str` |
| `page_average_lid_score` | `float` |
| `full_doc_lid` | `str` |
| `full_doc_lid_score` | `float` |
| `per_page_languages` | `list` |
| `is_truncated` | `bool` |
| `extractor` | `str` |
| `page_ends` | `list` |


key: chroma:document
string_value:
# Dataset: `legacy-datasets/wikipedia`

## 📝 Metadata
- **Author:** legacy-datasets
- **Downloads:** 25891
- **Likes:** 604
- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:aa, language:ab, language:ace, language:af, language:ak, language:als, language:am, language:an, language:ang, language:ar, language:arc, language:arz, language:as, language:ast, language:atj, language:av, language:ay, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bh, language:bi, language:bjn, language:bm, language:bn, language:bo, language:bpy, language:br, language:bs, language:bug, language:bxr, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:ch, language:cho, language:chr, language:chy, language:ckb, language:co, language:cr, language:crh, language:cs, language:csb, language:cu, language:cv, language:cy, language:da, language:de, language:din, language:diq, language:dsb, language:dty, language:dv, language:dz, language:ee, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:ff, language:fi, language:fj, language:fo, language:fr, language:frp, language:frr, language:fur, language:fy, language:ga, language:gag, language:gan, language:gd, language:gl, language:glk, language:gn, language:gom, language:gor, language:got, language:gu, language:gv, language:ha, language:hak, language:haw, language:he, language:hi, language:hif, language:ho, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ig, language:ii, language:ik, language:ilo, language:inh, language:io, language:is, language:it, language:iu, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kbp, language:kg, language:ki, language:kj, language:kk, language:kl, language:km, language:kn, language:ko, language:koi, language:krc, language:ks, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lbe, language:lez, language:lfn, language:lg, language:li, language:lij, language:lmo, language:ln, language:lo, language:lrc, language:lt, language:ltg, language:lv, language:lzh, language:mai, language:mdf, language:mg, language:mh, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mr, language:mrj, language:ms, language:mt, language:mus, language:mwl, language:my, language:myv, language:mzn, language:na, language:nah, language:nan, language:nap, language:nds, language:ne, language:new, language:ng, language:nl, language:nn, language:no, language:nov, language:nrf, language:nso, language:nv, language:ny, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pdc, language:pfl, language:pi, language:pih, language:pl, language:pms, language:pnb, language:pnt, language:ps, language:pt, language:qu, language:rm, language:rmy, language:rn, language:ro, language:ru, language:rue, language:rup, language:rw, language:sa, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:se, language:sg, language:sgs, language:sh, language:si, language:sk, language:sl, language:sm, language:sn, language:so, language:sq, language:sr, language:srn, language:ss, language:st, language:stq, language:su, language:sv, language:sw, language:szl, language:ta, language:tcy, language:tdt, language:te, language:tg, language:th, language:ti, language:tk, language:tl, language:tn, language:to, language:tpi, language:tr, language:ts, language:tt, language:tum, language:tw, language:ty, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wo, language:wuu, language:xal, language:xh, language:xmf, language:yi, language:yo, language:yue, language:za, language:zea, language:zh, language:zu, license:cc-by-sa-3.0, license:gfdl, size_categories:n<1K, region:us
- **License:** Not specified

## 📖 Description
```text
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.)....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/20220301.de/train-00000-of-00018.parquet`
- `data/20220301.de/train-00001-of-00018.parquet`
- `data/20220301.de/train-00002-of-00018.parquet`
- `data/20220301.de/train-00003-of-00018.parquet`
- `data/20220301.de/train-00004-of-00018.parquet`
- `data/20220301.de/train-00005-of-00018.parquet`
- `data/20220301.de/train-00006-of-00018.parquet`
- `data/20220301.de/train-00007-of-00018.parquet`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset scripts are no longer supported, but found wikipedia.py
```


key: chroma:document
string_value:
# Dataset: `gretelai/synthetic_text_to_sql`

## 📝 Metadata
- **Author:** gretelai
- **Downloads:** 2514
- **Likes:** 601
- **Tags:** task_categories:question-answering, task_categories:table-question-answering, task_categories:text-generation, language:en, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.05685, region:us, synthetic, SQL, text-to-SQL, code
- **License:** Not specified

## 📖 Description
```text
Image generated by DALL-E. See prompt for more details



	
		
		synthetic_text_to_sql
	


gretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, 
designed and generated using Gretel Navigator, and released under Apache 2.0.
Please see our release blogpost for more details.
The dataset includes:

  105,851 records partitioned into 100,000 train and 5,851 test records
  ~23M total tokens, including ~12M SQL tokens
  Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `bmc2_llm_judge_example_1.txt`
- `bmc2_llm_judge_example_2.txt`
- `dalle_prompt.txt`
- `llm_as_a_judge_rubric.txt`
- `synthetic_text_to_sql_test.snappy.parquet`
- `synthetic_text_to_sql_train.snappy.parquet`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `int` |
| `domain` | `str` |
| `domain_description` | `str` |
| `sql_complexity` | `str` |
| `sql_complexity_description` | `str` |
| `sql_task_type` | `str` |
| `sql_task_type_description` | `str` |
| `sql_prompt` | `str` |
| `sql_context` | `str` |
| `sql` | `str` |
| `sql_explanation` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `id` | `int` |
| `domain` | `str` |
| `domain_description` | `str` |
| `sql_complexity` | `str` |
| `sql_complexity_description` | `str` |
| `sql_task_type` | `str` |
| `sql_task_type_description` | `str` |
| `sql_prompt` | `str` |
| `sql_context` | `str` |
| `sql` | `str` |
| `sql_explanation` | `str` |


key: chroma:document
string_value:
# Dataset: `ILSVRC/imagenet-1k`

## 📝 Metadata
- **Author:** ILSVRC
- **Downloads:** 47651
- **Likes:** 591
- **Tags:** task_categories:image-classification, task_ids:multi-class-image-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:other, size_categories:1M<n<10M, format:parquet, modality:image, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1409.0575, arxiv:1912.07726, arxiv:1811.12231, arxiv:2109.13228, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for ImageNet
	


	
		
		Dataset Summary
	

ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are… See the full description on the dataset page: https://huggingface.co/datasets/ILSVRC/imagenet-1k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `classes.py`
- `data/test-00000-of-00028.parquet`
- `data/test-00001-of-00028.parquet`
- `data/test-00002-of-00028.parquet`
- `data/test-00003-of-00028.parquet`
- `data/test-00004-of-00028.parquet`
- `data/test-00005-of-00028.parquet`
- `data/test-00006-of-00028.parquet`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'ILSVRC/imagenet-1k' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/ILSVRC/imagenet-1k to ask for access.
```


key: chroma:document
string_value:
# Dataset: `HuggingFaceH4/ultrachat_200k`

## 📝 Metadata
- **Author:** HuggingFaceH4
- **Downloads:** 22093
- **Likes:** 590
- **Tags:** task_categories:text-generation, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.14233, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for UltraChat 200k
	


	
		
		Dataset Description
	

This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model.
The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:

Selection of a subset of data for faster supervised fine tuning.
Truecasing of the dataset, as we observed around 5% of the data… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/test_gen-00000-of-00001-3d4cd8309148a71f.parquet`
- `data/test_sft-00000-of-00001-f7dfac4afe5b93f4.parquet`
- `data/train_gen-00000-of-00003-a6c9fb894be3e50b.parquet`
- `data/train_gen-00001-of-00003-d6a0402e417f35ca.parquet`
- `data/train_gen-00002-of-00003-c0db75b92a2f48fd.parquet`
- `data/train_sft-00000-of-00003-a3ecf92756993583.parquet`
- `data/train_sft-00001-of-00003-0a1804bcb6ae68c6.parquet`
- `data/train_sft-00002-of-00003-ee46ed25cfae92c6.parquet`

## 📊 Data Structure
### Config: `default`
#### Split: `train_sft`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `prompt_id` | `str` |
| `messages` | `list` |
#### Split: `test_sft`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `prompt_id` | `str` |
| `messages` | `list` |
#### Split: `train_gen`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `prompt_id` | `str` |
| `messages` | `list` |


key: chroma:document
string_value:
# Dataset: `nvidia/Llama-Nemotron-Post-Training-Dataset`

## 📝 Metadata
- **Author:** nvidia
- **Downloads:** 3669
- **Likes:** 587
- **Tags:** license:cc-by-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2505.00949, region:us
- **License:** Not specified

## 📖 Description
```text
Llama-Nemotron-Post-Training-Dataset-v1.1 Release
	

Update [4/8/2025]: 
v1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉 

	
		
		Data Overview
	

This dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `RL/instruction_following/instruction_following.jsonl`
- `SFT/chat/chat.jsonl`
- `SFT/code/code_v1.1.jsonl`
- `SFT/code/code_v1.jsonl`
- `SFT/math/math_v1.1.jsonl`
- `SFT/math/math_v1.jsonl`
- `SFT/safety/safety.jsonl`
- `SFT/science/science.jsonl`
- ... and more.

## 📊 Data Structure
### Config: `SFT`
#### Split: `code`
| Column Name | Data Type |
|---|---|
| `input` | `list` |
| `output` | `str` |
| `category` | `str` |
| `license` | `str` |
| `reasoning` | `str` |
| `generator` | `str` |
| `used_in_training` | `str` |
| `version` | `str` |
| `system_prompt` | `str` |
#### Split: `math`
| Column Name | Data Type |
|---|---|
| `input` | `list` |
| `output` | `str` |
| `category` | `str` |
| `license` | `str` |
| `reasoning` | `str` |
| `generator` | `str` |
| `used_in_training` | `str` |
| `version` | `str` |
| `system_prompt` | `str` |
#### Split: `science`
| Column Name | Data Type |
|---|---|
| `input` | `list` |
| `output` | `str` |
| `category` | `str` |
| `license` | `str` |
| `reasoning` | `str` |
| `generator` | `str` |
| `used_in_training` | `str` |
| `version` | `str` |
| `system_prompt` | `str` |

### Config: `RL`
#### Split: `instruction_following`
| Column Name | Data Type |
|---|---|
| `input` | `list` |
| `args` | `dict` |
| `category` | `str` |
| `license` | `str` |
| `reasoning` | `str` |
| `used_in_training` | `str` |
| `version` | `str` |
| `system_prompt` | `str` |


key: chroma:document
string_value:
# Dataset: `cais/mmlu`

## 📝 Metadata
- **Author:** cais
- **Downloads:** 256311
- **Likes:** 569
- **Tags:** task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:no-annotation, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2009.03300, arxiv:2005.00700, arxiv:2005.14165, arxiv:2008.02275, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for MMLU
	


	
		
		Dataset Summary
	

Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `abstract_algebra/dev-00000-of-00001.parquet`
- `abstract_algebra/test-00000-of-00001.parquet`
- `abstract_algebra/validation-00000-of-00001.parquet`
- `all/auxiliary_train-00000-of-00001.parquet`
- `all/dev-00000-of-00001.parquet`
- `all/test-00000-of-00001.parquet`
- `all/validation-00000-of-00001.parquet`
- `anatomy/dev-00000-of-00001.parquet`
- ... and more.

## 📊 Data Structure
### Config: `abstract_algebra`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `dev`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |

### Config: `all`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `dev`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |

### Config: `anatomy`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |
#### Split: `dev`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `subject` | `str` |
| `choices` | `list` |
| `answer` | `int` |


key: chroma:document
string_value:
# Dataset: `liwu/MNBVC`

## 📝 Metadata
- **Author:** liwu
- **Downloads:** 55042
- **Likes:** 560
- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:other, language_creators:other, multilinguality:monolingual, source_datasets:original, language:zh, license:mit, region:us
- **License:** Not specified

## 📖 Description
```text
MNBVC: Massive Never-ending BT Vast Chinese corpus...
```

## 📂 File System Sample
- `.gitattributes`
- `MNBVC.py`
- `README.md`
- `academic_paper/arxiv/20230241/arxivCode.0.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.1.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.10.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.11.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.12.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.13.jsonl.gz`
- `academic_paper/arxiv/20230241/arxivCode.14.jsonl.gz`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset scripts are no longer supported, but found MNBVC.py
```


key: chroma:document
string_value:
# Dataset: `liuhaotian/LLaVA-Instruct-150K`

## 📝 Metadata
- **Author:** liuhaotian
- **Downloads:** 3016
- **Likes:** 550
- **Tags:** task_categories:visual-question-answering, task_categories:question-answering, language:en, license:cc-by-4.0, size_categories:100K<n<1M, region:us
- **License:** Not specified

## 📖 Description
```text
LLaVA Visual Instruct 150K Dataset Card
	


	
		
		Dataset details
	

Dataset type:
LLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data.
It is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability.
Dataset date:
LLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API.
Paper or resources for more information:
https://llava-vl.github.io/
License:
Creative… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `complex_reasoning_77k.json`
- `conversation_58k.json`
- `detail_23k.json`
- `llava_instruct_150k.json`
- `llava_instruct_80k.json`
- `llava_v1_5_mix665k.json`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `id` | `str` |
| `image` | `str` |
| `conversations` | `list` |


key: chroma:document
string_value:
# Dataset: `uonlp/CulturaX`

## 📝 Metadata
- **Author:** uonlp
- **Downloads:** 46423
- **Likes:** 547
- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:als, language:am, language:an, language:ar, language:arz, language:as, language:ast, language:av, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bh, language:bn, language:bo, language:bpy, language:br, language:bs, language:bxr, language:ca, language:cbk, language:ce, language:ceb, language:ckb, language:cs, language:cv, language:cy, language:da, language:de, language:dsb, language:dv, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:frr, language:fy, language:ga, language:gd, language:gl, language:gn, language:gom, language:gu, language:he, language:hi, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ilo, language:io, language:is, language:it, language:ja, language:jbo, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:krc, language:ku, language:kv, language:kw, language:ky, language:la, language:lb, language:lez, language:li, language:lmo, language:lo, language:lrc, language:lt, language:lv, language:mai, language:mg, language:mhr, language:min, language:mk, language:ml, language:mn, language:mr, language:mrj, language:ms, language:mt, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nap, language:nds, language:ne, language:new, language:nl, language:nn, language:no, language:oc, language:or, language:os, language:pa, language:pam, language:pl, language:pms, language:pnb, language:ps, language:pt, language:qu, language:rm, language:ro, language:ru, language:rue, language:sa, language:sah, language:scn, language:sd, language:sh, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:te, language:tg, language:th, language:tk, language:tl, language:tr, language:tt, language:tyv, language:ug, language:uk, language:ur, language:uz, language:vec, language:vi, language:vls, language:vo, language:wa, language:war, language:wuu, language:xal, language:xmf, language:yi, language:yo, language:yue, language:zh, size_categories:1B<n<10B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.09400, region:us
- **License:** Not specified

## 📖 Description
```text
CulturaX 
     Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages 






	
		
		Dataset Summary
	

We present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language… See the full description on the dataset page: https://huggingface.co/datasets/uonlp/CulturaX....
```

## 📂 File System Sample
- `.gitattributes`
- `CulturaX_loading_script.py`
- `README.md`
- `af/af_part_00000.parquet`
- `af/checksum.sha256`
- `als/als_part_00000.parquet`
- `als/checksum.sha256`
- `am/am_part_00000.parquet`
- `am/checksum.sha256`
- `an/an_part_00000.parquet`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'uonlp/CulturaX' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/uonlp/CulturaX to ask for access.
```


key: chroma:document
string_value:
# Dataset: `Salesforce/xlam-function-calling-60k`

## 📝 Metadata
- **Author:** Salesforce
- **Downloads:** 6017
- **Likes:** 538
- **Tags:** task_categories:question-answering, task_categories:text-generation, task_categories:reinforcement-learning, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.18518, region:us, function-calling, LLM Agent, code, synthetic
- **License:** Not specified

## 📖 Description
```text
APIGen Function-Calling Datasets
	

Paper | Website | Models
This repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. 
We conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `figures/dataset_pie_chart.png`
- `figures/function-call-overview.png`
- `figures/overview.jpg`
- `xlam_function_calling_60k.json`

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'Salesforce/xlam-function-calling-60k' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k to ask for access.
```


key: chroma:document
string_value:
# Dataset: `facebook/natural_reasoning`

## 📝 Metadata
- **Author:** facebook
- **Downloads:** 3349
- **Likes:** 538
- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2502.13124, region:us
- **License:** Not specified

## 📖 Description
```text
NaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning....
```

## 📂 File System Sample
- `.gitattributes`
- `NaturalReasoning.pdf`
- `README.md`
- `full.jsonl`
- `scaling_plot`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `question` | `str` |
| `reference_answer` | `str` |
| `responses` | `list` |


key: chroma:document
string_value:
# Dataset: `poloclub/diffusiondb`

## 📝 Metadata
- **Author:** poloclub
- **Downloads:** 34306
- **Likes:** 519
- **Tags:** task_categories:text-to-image, task_categories:image-to-text, task_ids:image-captioning, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:en, license:cc0-1.0, size_categories:n>1T, arxiv:2210.14896, region:us, stable diffusion, prompt engineering, prompts, research paper
- **License:** Not specified

## 📖 Description
```text
DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2
million images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the interplay between prompts and generative models, detecting deepfakes, and
designing human-AI interaction tools to help users more easily use these models....
```

## 📂 File System Sample
- `.gitattributes`
- `.gitignore`
- `README.md`
- `diffusiondb-large-part-1/part-000001.zip`
- `diffusiondb-large-part-1/part-000002.zip`
- `diffusiondb-large-part-1/part-000003.zip`
- `diffusiondb-large-part-1/part-000004.zip`
- `diffusiondb-large-part-1/part-000005.zip`
- `diffusiondb-large-part-1/part-000006.zip`
- `diffusiondb-large-part-1/part-000007.zip`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset scripts are no longer supported, but found diffusiondb.py
```


key: chroma:document
string_value:
# Dataset: `JosephusCheung/GuanacoDataset`

## 📝 Metadata
- **Author:** JosephusCheung
- **Downloads:** 269
- **Likes:** 515
- **Tags:** task_categories:text-generation, task_categories:question-answering, language:zh, language:en, language:ja, language:de, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, doi:10.57967/hf/1423, region:us, alpaca, llama, guanaco
- **License:** Not specified

## 📖 Description
```text
Sorry, it's no longer available on Hugging Face. Please reach out to those who have already downloaded it. If you have a copy, please refrain from re-uploading it to Hugging Face. The people here don't deserve it. See also: https://twitter.com/RealJosephus/status/1779913520529707387

	
		
		GuanacoDataset
	

News: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.
Still under construction: GuanacoVQA weight & GuanacoVQA Dataset 
Notice: Effective… See the full description on the dataset page: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `additional/general_ans-utf8.json`
- `additional/general_ans.json`
- `additional/general_questions-utf8.json`
- `additional/general_questions.json`
- `additional/paper_answers-utf8.json`
- `additional/paper_answers.json`
- `additional/paper_questions-utf8.json`
- `additional/paper_questions.json`
- ... and more.

## 📊 Data Structure
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Dataset 'JosephusCheung/GuanacoDataset' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/JosephusCheung/GuanacoDataset to ask for access.
```


key: chroma:document
string_value:
# Dataset: `Salesforce/wikitext`

## 📝 Metadata
- **Author:** Salesforce
- **Downloads:** 883534
- **Likes:** 511
- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1609.07843, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for "wikitext"
	


	
		
		Dataset Summary
	

 The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified
 Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.
Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over
110 times larger. The WikiText dataset also features a far larger… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `wikitext-103-raw-v1/test-00000-of-00001.parquet`
- `wikitext-103-raw-v1/train-00000-of-00002.parquet`
- `wikitext-103-raw-v1/train-00001-of-00002.parquet`
- `wikitext-103-raw-v1/validation-00000-of-00001.parquet`
- `wikitext-103-v1/test-00000-of-00001.parquet`
- `wikitext-103-v1/train-00000-of-00002.parquet`
- `wikitext-103-v1/train-00001-of-00002.parquet`
- `wikitext-103-v1/validation-00000-of-00001.parquet`
- ... and more.

## 📊 Data Structure
### Config: `wikitext-103-raw-v1`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `text` | `str` |

### Config: `wikitext-103-v1`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `text` | `str` |

### Config: `wikitext-2-raw-v1`
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `text` | `str` |
#### Split: `validation`
| Column Name | Data Type |
|---|---|
| `text` | `str` |


key: chroma:document
string_value:
# Dataset: `HuggingFaceH4/no_robots`

## 📝 Metadata
- **Author:** HuggingFaceH4
- **Downloads:** 2713
- **Likes:** 505
- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.02155, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for No Robots 🙅‍♂️🤖
	

Look Ma, an instruction dataset that wasn't generated by GPTs!

	
		
		Dataset Summary
	

No Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/no_robots....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/test-00000-of-00001.parquet`
- `data/test_sft-00000-of-00001.parquet`
- `data/train-00000-of-00001.parquet`
- `data/train_sft-00000-of-00001.parquet`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `prompt_id` | `str` |
| `messages` | `list` |
| `category` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `prompt` | `str` |
| `prompt_id` | `str` |
| `messages` | `list` |
| `category` | `str` |


key: chroma:document
string_value:
# Dataset: `Gustavosta/Stable-Diffusion-Prompts`

## 📝 Metadata
- **Author:** Gustavosta
- **Downloads:** 8696
- **Likes:** 503
- **Tags:** annotations_creators:no-annotation, language_creators:found, source_datasets:original, language:en, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us
- **License:** Not specified

## 📖 Description
```text
Stable Diffusion Dataset
	

This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "Lexica.art". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
If you want to test the model with a demo, you can go to: "spaces/Gustavosta/MagicPrompt-Stable-Diffusion".
If you want to see the model, go to: "Gustavosta/MagicPrompt-Stable-Diffusion"....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `data/eval.parquet`
- `data/train.parquet`

## 📊 Data Structure
### Config: `default`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `Prompt` | `str` |
#### Split: `test`
| Column Name | Data Type |
|---|---|
| `Prompt` | `str` |


key: chroma:document
string_value:
# Dataset: `cerebras/SlimPajama-627B`

## 📝 Metadata
- **Author:** cerebras
- **Downloads:** 44705
- **Likes:** 502
- **Tags:** task_categories:text-generation, language:en, arxiv:2306.01116, arxiv:2302.13971, region:us
- **License:** Not specified

## 📖 Description
```text
The dataset consists of 59166 jsonl files and is ~895GB compressed. It is a cleaned and deduplicated version of Together's RedPajama. 
Check out our blog post explaining our methods, our code on GitHub, and join the discussion on the Cerebras Discord.

	
		
	
	
		Getting Started
	

You can download the dataset using Hugging Face datasets:
from datasets import load_dataset
ds = load_dataset("cerebras/SlimPajama-627B")


	
	
	
		Background
	

Today we are releasing SlimPajama – the largest… See the full description on the dataset page: https://huggingface.co/datasets/cerebras/SlimPajama-627B....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `test/chunk1/example_holdout_0.jsonl.zst`
- `test/chunk1/example_holdout_1.jsonl.zst`
- `test/chunk1/example_holdout_10.jsonl.zst`
- `test/chunk1/example_holdout_100.jsonl.zst`
- `test/chunk1/example_holdout_1000.jsonl.zst`
- `test/chunk1/example_holdout_1001.jsonl.zst`
- `test/chunk1/example_holdout_1002.jsonl.zst`
- `test/chunk1/example_holdout_1003.jsonl.zst`
- ... and more.

## 📊 Data Structure
### Config: `default`
#### Split: `train`
**Anomaly/Error during data loading:**
```
Could not inspect dataset structure.
Details: Compression type zstd not supported
```


key: chroma:document
string_value:
# Dataset: `HuggingFaceM4/the_cauldron`

## 📝 Metadata
- **Author:** HuggingFaceM4
- **Downloads:** 58735
- **Likes:** 502
- **Tags:** size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1603.07396, arxiv:2206.01718, arxiv:2208.05358, arxiv:1612.06890, arxiv:2310.00367, arxiv:1710.07300, arxiv:2312.12241, arxiv:1912.03098, arxiv:2211.08545, arxiv:2306.05425, arxiv:1709.00103, arxiv:2003.12462, arxiv:1612.00837, arxiv:2205.00363, arxiv:2403.09029, arxiv:2405.02246, region:us
- **License:** Not specified

## 📖 Description
```text
Dataset Card for The Cauldron
	



	
		
		Dataset description
	

The Cauldron is part of the Idefics2 release.
It is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.

	
		
		Load the dataset
	

To load the dataset, install the library datasets with pip install datasets. Then,
from datasets import load_dataset
ds = load_dataset("HuggingFaceM4/the_cauldron", "ai2d")

to download and load the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron....
```

## 📂 File System Sample
- `.gitattributes`
- `README.md`
- `ai2d/train-00000-of-00001-2ce340398c113b79.parquet`
- `aokvqa/train-00000-of-00002-88e828b9a932c295.parquet`
- `aokvqa/train-00001-of-00002-ceb27cfe85e08680.parquet`
- `chart2text/train-00000-of-00003-3a2ec464eb1cfc9b.parquet`
- `chart2text/train-00001-of-00003-a65d11892445678c.parquet`
- `chart2text/train-00002-of-00003-8626ac7f2c225705.parquet`
- `chartqa/train-00000-of-00002-7733d4ca73ccd12e.parquet`
- `chartqa/train-00001-of-00002-03251c406186eabb.parquet`
- ... and more.

## 📊 Data Structure
### Config: `ai2d`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `images` | `list` |
| `texts` | `list` |

### Config: `aokvqa`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `images` | `list` |
| `texts` | `list` |

### Config: `chart2text`
#### Split: `train`
| Column Name | Data Type |
|---|---|
| `images` | `list` |
| `texts` | `list` |


# embeddings_queue

created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: fka/awesome-chatgpt-prompts
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `fka/awesome-chatgpt-prompts`\n\n## 📝 Metadata\n- **Author:** fka\n- **Downloads:** 34418\n- **Likes:** 9309\n- **Tags:** task_categories:question-answering, license:cc0-1.0, size_categories:n<1K, format:csv, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, ChatGPT\n- **License:** Not specified\n\n## 📖 Description\n```text\n🧠 Awesome ChatGPT Prompts [CSV dataset]\n\nThis is a Dataset Repository of Awesome ChatGPT Prompts\nView All Prompts on GitHub\n\n\t\n\t\t\n\t\tLicense\n\t\n\nCC-0...\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `prompts.csv`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `act` | `str` |\n| `prompt` | `str` |\n\n","id":"fka/awesome-chatgpt-prompts"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceFW/fineweb
encoding: FLOAT32
metadata:
{"id":"HuggingFaceFW/fineweb","chroma:document":"# Dataset: `HuggingFaceFW/fineweb`\n\n## 📝 Metadata\n- **Author:** HuggingFaceFW\n- **Downloads:** 277712\n- **Likes:** 2405\n- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:10B<n<100B, modality:tabular, modality:text, arxiv:2306.01116, arxiv:2109.07445, arxiv:2406.17557, doi:10.57967/hf/2493, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n🍷 FineWeb\n\t\n\n\n    \n\n\n\n15 trillion tokens of the finest data the 🌐 web has to offer\n\n\n\t\n\t\t\n\t\tWhat is it?\n\t\n\nThe 🍷 FineWeb dataset consists of more than 18.5T tokens (originally 15T tokens) of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library. \n🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/CC-MAIN-2013-20/000_00000.parquet`\n- `data/CC-MAIN-2013-20/000_00001.parquet`\n- `data/CC-MAIN-2013-20/000_00002.parquet`\n- `data/CC-MAIN-2013-20/000_00003.parquet`\n- `data/CC-MAIN-2013-20/000_00004.parquet`\n- `data/CC-MAIN-2013-20/000_00005.parquet`\n- `data/CC-MAIN-2013-20/000_00006.parquet`\n- `data/CC-MAIN-2013-20/000_00007.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n\n### Config: `sample-10BT`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n\n### Config: `sample-100BT`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Anthropic/hh-rlhf
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `Anthropic/hh-rlhf`\n\n## 📝 Metadata\n- **Author:** Anthropic\n- **Downloads:** 33017\n- **Likes:** 1464\n- **Tags:** license:mit, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2204.05862, region:us, human-feedback\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for HH-RLHF\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nThis repository provides access to two different kinds of data:\n\nHuman preference data about helpfulness and harmlessness from Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback. These data are meant to train preference (or reward) models for subsequent RLHF training. These data are not meant for supervised training of dialogue agents. Training dialogue agents on these data is likely to lead… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/hh-rlhf....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `harmless-base/test.jsonl.gz`\n- `harmless-base/train.jsonl.gz`\n- `helpful-base/test.jsonl.gz`\n- `helpful-base/train.jsonl.gz`\n- `helpful-online/test.jsonl.gz`\n- `helpful-online/train.jsonl.gz`\n- `helpful-rejection-sampled/test.jsonl.gz`\n- `helpful-rejection-sampled/train.jsonl.gz`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `chosen` | `str` |\n| `rejected` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `chosen` | `str` |\n| `rejected` | `str` |\n\n","id":"Anthropic/hh-rlhf"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Open-Orca/OpenOrca
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `Open-Orca/OpenOrca`\n\n## 📝 Metadata\n- **Author:** Open-Orca\n- **Downloads:** 8268\n- **Likes:** 1457\n- **Tags:** task_categories:text-classification, task_categories:token-classification, task_categories:table-question-answering, task_categories:question-answering, task_categories:zero-shot-classification, task_categories:summarization, task_categories:feature-extraction, task_categories:text-generation, language:en, license:mit, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.02707, arxiv:2301.13688, arxiv:2302.13971, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n🐋 The OpenOrca Dataset! 🐋\n\n\n\nWe are thrilled to announce the release of the OpenOrca dataset!\nThis rich collection of augmented FLAN data aligns, as best as possible, with the distributions outlined in the Orca paper.\nIt has been instrumental in generating high-performing model checkpoints and serves as a valuable resource for all NLP researchers and developers!\n\n\t\n\t\t\n\t\n\t\n\t\tOfficial Models\n\t\n\n\n\t\n\t\n\t\n\t\tMistral-7B-OpenOrca\n\t\n\nOur latest model, the first 7B to score better overall than all… See the full description on the dataset page: https://huggingface.co/datasets/Open-Orca/OpenOrca....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `1M-GPT4-Augmented.parquet`\n- `3_5M-GPT3_5-Augmented.parquet`\n- `OpenOrcaLogo.png`\n- `README.md`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `str` |\n| `system_prompt` | `str` |\n| `question` | `str` |\n| `response` | `str` |\n\n","id":"Open-Orca/OpenOrca"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: OpenAssistant/oasst1
encoding: FLOAT32
metadata:
{"id":"OpenAssistant/oasst1","chroma:document":"# Dataset: `OpenAssistant/oasst1`\n\n## 📝 Metadata\n- **Author:** OpenAssistant\n- **Downloads:** 8123\n- **Likes:** 1440\n- **Tags:** language:en, language:es, language:ru, language:de, language:pl, language:th, language:vi, language:sv, language:bn, language:da, language:he, language:it, language:fa, language:sk, language:id, language:nb, language:el, language:nl, language:hu, language:eu, language:zh, language:eo, language:ja, language:ca, language:cs, language:bg, language:fi, language:pt, language:tr, language:ro, language:ar, language:uk, language:gl, language:fr, language:ko, license:apache-2.0, size_categories:10K<n<100K, format:parquet, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2304.07327, region:us, human-feedback\n- **License:** Not specified\n\n## 📖 Description\n```text\nOpenAssistant Conversations Dataset (OASST1)\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nIn an effort to democratize research on large-scale alignment, we release OpenAssistant \nConversations (OASST1), a human-generated, human-annotated assistant-style conversation \ncorpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 \nquality ratings, resulting in over 10,000 fully annotated conversation trees. The corpus \nis a product of a worldwide crowd-sourcing effort… See the full description on the dataset page: https://huggingface.co/datasets/OpenAssistant/oasst1....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `2023-04-12_oasst_all.messages.jsonl.gz`\n- `2023-04-12_oasst_all.trees.jsonl.gz`\n- `2023-04-12_oasst_prompts.messages.jsonl.gz`\n- `2023-04-12_oasst_ready.messages.jsonl.gz`\n- `2023-04-12_oasst_ready.trees.jsonl.gz`\n- `2023-04-12_oasst_spam.messages.jsonl.gz`\n- `LICENSE`\n- `README.md`\n- `data/train-00000-of-00001-b42a775f407cee45.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `message_id` | `str` |\n| `parent_id` | `NoneType` |\n| `user_id` | `str` |\n| `created_date` | `str` |\n| `text` | `str` |\n| `role` | `str` |\n| `lang` | `str` |\n| `review_count` | `int` |\n| `review_result` | `bool` |\n| `deleted` | `bool` |\n| `rank` | `NoneType` |\n| `synthetic` | `bool` |\n| `model_name` | `NoneType` |\n| `detoxify` | `dict` |\n| `message_tree_id` | `str` |\n| `tree_state` | `str` |\n| `emojis` | `dict` |\n| `labels` | `dict` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `message_id` | `str` |\n| `parent_id` | `NoneType` |\n| `user_id` | `str` |\n| `created_date` | `str` |\n| `text` | `str` |\n| `role` | `str` |\n| `lang` | `str` |\n| `review_count` | `int` |\n| `review_result` | `bool` |\n| `deleted` | `bool` |\n| `rank` | `NoneType` |\n| `synthetic` | `bool` |\n| `model_name` | `NoneType` |\n| `detoxify` | `dict` |\n| `message_tree_id` | `str` |\n| `tree_state` | `str` |\n| `emojis` | `dict` |\n| `labels` | `dict` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: gsdf/EasyNegative
encoding: FLOAT32
metadata:
{"id":"gsdf/EasyNegative","chroma:document":"# Dataset: `gsdf/EasyNegative`\n\n## 📝 Metadata\n- **Author:** gsdf\n- **Downloads:** 63081\n- **Likes:** 1158\n- **Tags:** license:other, size_categories:n<1K, format:imagefolder, modality:image, library:datasets, library:mlcroissant, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nNegative Embedding\n\t\n\nThis is a Negative Embedding trained with Counterfeit. Please use it in the \"\\stable-diffusion-webui\\embeddings\" folder.It can be used with other models, but the effectiveness is not certain.  \n\n\t\n\t\t\n\t\tCounterfeit-V2.0.safetensors\n\t\n\n\n\n\t\n\t\t\n\t\tAbyssOrangeMix2_sfw.safetensors\n\t\n\n\n\n\t\n\t\t\n\t\tanything-v4.0-pruned.safetensors...\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `EasyNegative.pt`\n- `EasyNegative.safetensors`\n- `README.md`\n- `sample01.png`\n- `sample02.png`\n- `sample03.png`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `image` | `PngImageFile` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: togethercomputer/RedPajama-Data-1T
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `togethercomputer/RedPajama-Data-1T`\n\n## 📝 Metadata\n- **Author:** togethercomputer\n- **Downloads:** 1009\n- **Likes:** 1105\n- **Tags:** task_categories:text-generation, language:en, size_categories:1M<n<10M, modality:text, library:datasets, library:mlcroissant, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nRedPajama is a clean-room, fully open-source implementation of the LLaMa dataset....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `RedPajama-Data-1T.py`\n- `urls/arxiv.txt`\n- `urls/c4.txt`\n- `urls/common_crawl.txt`\n- `urls/github.txt`\n- `urls/stackexchange.txt`\n- `urls/wikipedia.txt`\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset scripts are no longer supported, but found RedPajama-Data-1T.py\n```\n","id":"togethercomputer/RedPajama-Data-1T"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: wikimedia/wikipedia
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `wikimedia/wikipedia`\n\n## 📝 Metadata\n- **Author:** wikimedia\n- **Downloads:** 58900\n- **Likes:** 955\n- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, language:ab, language:ace, language:ady, language:af, language:alt, language:am, language:ami, language:an, language:ang, language:anp, language:ar, language:arc, language:ary, language:arz, language:as, language:ast, language:atj, language:av, language:avk, language:awa, language:ay, language:az, language:azb, language:ba, language:ban, language:bar, language:bbc, language:bcl, language:be, language:bg, language:bh, language:bi, language:bjn, language:blk, language:bm, language:bn, language:bo, language:bpy, language:br, language:bs, language:bug, language:bxr, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:ch, language:chr, language:chy, language:ckb, language:co, language:cr, language:crh, language:cs, language:csb, language:cu, language:cv, language:cy, language:da, language:dag, language:de, language:dga, language:din, language:diq, language:dsb, language:dty, language:dv, language:dz, language:ee, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:fat, language:ff, language:fi, language:fj, language:fo, language:fon, language:fr, language:frp, language:frr, language:fur, language:fy, language:ga, language:gag, language:gan, language:gcr, language:gd, language:gl, language:glk, language:gn, language:gom, language:gor, language:got, language:gpe, language:gsw, language:gu, language:guc, language:gur, language:guw, language:gv, language:ha, language:hak, language:haw, language:hbs, language:he, language:hi, language:hif, language:hr, language:hsb, language:ht, language:hu, language:hy, language:hyw, language:ia, language:id, language:ie, language:ig, language:ik, language:ilo, language:inh, language:io, language:is, language:it, language:iu, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kbp, language:kcg, language:kg, language:ki, language:kk, language:kl, language:km, language:kn, language:ko, language:koi, language:krc, language:ks, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lbe, language:lez, language:lfn, language:lg, language:li, language:lij, language:lld, language:lmo, language:ln, language:lo, language:lt, language:ltg, language:lv, language:lzh, language:mad, language:mai, language:map, language:mdf, language:mg, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mni, language:mnw, language:mr, language:mrj, language:ms, language:mt, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nan, language:nap, language:nds, language:ne, language:new, language:nia, language:nl, language:nn, language:no, language:nov, language:nqo, language:nrf, language:nso, language:nv, language:ny, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pcm, language:pdc, language:pfl, language:pi, language:pih, language:pl, language:pms, language:pnb, language:pnt, language:ps, language:pt, language:pwn, language:qu, language:rm, language:rmy, language:rn, language:ro, language:ru, language:rue, language:rup, language:rw, language:sa, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:se, language:sg, language:sgs, language:shi, language:shn, language:si, language:sk, language:skr, language:sl, language:sm, language:smn, language:sn, language:so, language:sq, language:sr, language:srn, language:ss, language:st, language:stq, language:su, language:sv, language:sw, language:szl, language:szy, language:ta, language:tay, language:tcy, language:te, language:tet, language:tg, language:th, language:ti, language:tk, language:tl, language:tly, language:tn, language:to, language:tpi, language:tr, language:trv, language:ts, language:tt, language:tum, language:tw, language:ty, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wo, language:wuu, language:xal, language:xh, language:xmf, language:yi, language:yo, language:yue, language:za, language:zea, language:zgh, language:zh, language:zu, license:cc-by-sa-3.0, license:gfdl, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for Wikimedia Wikipedia\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nWikipedia dataset containing cleaned articles of all languages.\nThe dataset is built from the Wikipedia dumps (https://dumps.wikimedia.org/)\nwith one subset per language, each containing a single train split.\nEach example contains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.).\nAll language subsets have already been processed for recent dump, and you… See the full description on the dataset page: https://huggingface.co/datasets/wikimedia/wikipedia....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `20231101.ab/train-00000-of-00001.parquet`\n- `20231101.ace/train-00000-of-00001.parquet`\n- `20231101.ady/train-00000-of-00001.parquet`\n- `20231101.af/train-00000-of-00001.parquet`\n- `20231101.als/train-00000-of-00001.parquet`\n- `20231101.alt/train-00000-of-00001.parquet`\n- `20231101.am/train-00000-of-00001.parquet`\n- `20231101.ami/train-00000-of-00001.parquet`\n- `20231101.an/train-00000-of-00001.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `20231101.ab`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `str` |\n| `url` | `str` |\n| `title` | `str` |\n| `text` | `str` |\n\n### Config: `20231101.ace`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `str` |\n| `url` | `str` |\n| `title` | `str` |\n| `text` | `str` |\n\n### Config: `20231101.ady`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `str` |\n| `url` | `str` |\n| `title` | `str` |\n| `text` | `str` |\n\n","id":"wikimedia/wikipedia"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: allenai/dolma
encoding: FLOAT32
metadata:
{"id":"allenai/dolma","chroma:document":"# Dataset: `allenai/dolma`\n\n## 📝 Metadata\n- **Author:** allenai\n- **Downloads:** 1045\n- **Likes:** 950\n- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:n>1T, arxiv:2402.00159, arxiv:2301.13688, region:us, language-modeling, casual-lm, llm\n- **License:** Not specified\n\n## 📖 Description\n```text\nDolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research...\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `.gitignore`\n- `LICENSE.md`\n- `README.md`\n- `dolma.py`\n- `urls/v1.txt`\n- `urls/v1_5-sample.txt`\n- `urls/v1_5.txt`\n- `urls/v1_6-sample.txt`\n- `urls/v1_6.txt`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset scripts are no longer supported, but found dolma.py\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Nerfgun3/bad_prompt
encoding: FLOAT32
metadata:
{"id":"Nerfgun3/bad_prompt","chroma:document":"# Dataset: `Nerfgun3/bad_prompt`\n\n## 📝 Metadata\n- **Author:** Nerfgun3\n- **Downloads:** 3360\n- **Likes:** 926\n- **Tags:** language:en, license:creativeml-openrail-m, size_categories:n<1K, format:imagefolder, modality:image, library:datasets, library:mlcroissant, region:us, stable-diffusion, text-to-image, image-to-image\n- **License:** Not specified\n\n## 📖 Description\n```text\nNegative Embedding / Textual Inversion\n\t\n\n\n\n\n\t\n\t\t\n\t\tIdea\n\t\n\nThe idea behind this embedding was to somehow train the negative prompt as an embedding, thus unifying the basis of the negative prompt into one word or embedding. \nSide note: Embedding has proven to be very helpful for the generation of hands! :)\n\n\t\n\t\t\n\t\tUsage\n\t\n\nTo use this embedding you have to download the file aswell as drop it into the \"\\stable-diffusion-webui\\embeddings\" folder.\nPlease put the embedding in the negative… See the full description on the dataset page: https://huggingface.co/datasets/Nerfgun3/bad_prompt....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `bad_prompt.pt`\n- `bad_prompt_showcase.jpg`\n- `bad_prompt_version2.pt`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `image` | `JpegImageFile` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: openai/gsm8k
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `openai/gsm8k`\n\n## 📝 Metadata\n- **Author:** openai\n- **Downloads:** 386964\n- **Likes:** 915\n- **Tags:** annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2110.14168, region:us, math-word-problems\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for GSM8K\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nGSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.\n\nThese problems take between 2 and 8 steps to solve.\nSolutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − ×÷) to reach the… See the full description on the dataset page: https://huggingface.co/datasets/openai/gsm8k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `main/test-00000-of-00001.parquet`\n- `main/train-00000-of-00001.parquet`\n- `socratic/test-00000-of-00001.parquet`\n- `socratic/train-00000-of-00001.parquet`\n\n## 📊 Data Structure\n### Config: `main`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `answer` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `answer` | `str` |\n\n### Config: `socratic`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `answer` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `answer` | `str` |\n\n","id":"openai/gsm8k"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: FreedomIntelligence/medical-o1-reasoning-SFT
encoding: FLOAT32
metadata:
{"id":"FreedomIntelligence/medical-o1-reasoning-SFT","chroma:document":"# Dataset: `FreedomIntelligence/medical-o1-reasoning-SFT`\n\n## 📝 Metadata\n- **Author:** FreedomIntelligence\n- **Downloads:** 8138\n- **Likes:** 913\n- **Tags:** task_categories:question-answering, task_categories:text-generation, language:en, language:zh, license:apache-2.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2412.18925, region:us, medical, biology\n- **License:** Not specified\n\n## 📖 Description\n```text\nNews\n\t\n\n[2025/04/22] We split the data and kept only the medical SFT dataset (medical_o1_sft.json). The file medical_o1_sft_mix.json contains a mix of medical and general instruction data.\n[2025/02/22] We released the distilled dataset from Deepseek-R1 based on medical verifiable problems. You can use it to initialize your models with the reasoning chain from Deepseek-R1.\n[2024/12/25] We open-sourced the medical reasoning dataset for SFT, built on medical verifiable problems and an LLM… See the full description on the dataset page: https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `medical_o1_sft.json`\n- `medical_o1_sft_Chinese.json`\n- `medical_o1_sft_mix.json`\n- `medical_o1_sft_mix_Chinese.json`\n\n## 📊 Data Structure\n### Config: `en`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `Question` | `str` |\n| `Complex_CoT` | `str` |\n| `Response` | `str` |\n\n### Config: `zh`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `Question` | `str` |\n| `Complex_CoT` | `str` |\n| `Response` | `str` |\n\n### Config: `en_mix`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `Question` | `str` |\n| `Complex_CoT` | `str` |\n| `Response` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: tiiuae/falcon-refinedweb
encoding: FLOAT32
metadata:
{"id":"tiiuae/falcon-refinedweb","chroma:document":"# Dataset: `tiiuae/falcon-refinedweb`\n\n## 📝 Metadata\n- **Author:** tiiuae\n- **Downloads:** 18435\n- **Likes:** 874\n- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:100M<n<1B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2306.01116, arxiv:2203.15556, arxiv:2107.06499, arxiv:2104.08758, arxiv:2109.07445, arxiv:1911.00359, arxiv:2112.11446, doi:10.57967/hf/0737, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n📀 Falcon RefinedWeb\n\t\n\nFalcon RefinedWeb is a massive English web dataset built by TII and released under an ODC-By 1.0 license.\nSee the 📓 paper on arXiv for more details. \nRefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data. \nRefinedWeb is also \"multimodal-friendly\": it contains links and alt… See the full description on the dataset page: https://huggingface.co/datasets/tiiuae/falcon-refinedweb....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/train-00000-of-05534-b8fc5348cbe605a5.parquet`\n- `data/train-00001-of-05534-9bca3ce859516338.parquet`\n- `data/train-00002-of-05534-01680948bd81de83.parquet`\n- `data/train-00003-of-05534-b7806bb8ca893c23.parquet`\n- `data/train-00004-of-05534-cbe1137f523084a7.parquet`\n- `data/train-00005-of-05534-e4a5eae6c1419c9b.parquet`\n- `data/train-00006-of-05534-5bc49be138fd315b.parquet`\n- `data/train-00007-of-05534-c185da59aece723d.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `content` | `str` |\n| `url` | `str` |\n| `timestamp` | `datetime` |\n| `dump` | `str` |\n| `segment` | `str` |\n| `image_urls` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: databricks/databricks-dolly-15k
encoding: FLOAT32
metadata:
{"id":"databricks/databricks-dolly-15k","chroma:document":"# Dataset: `databricks/databricks-dolly-15k`\n\n## 📝 Metadata\n- **Author:** databricks\n- **Downloads:** 17397\n- **Likes:** 873\n- **Tags:** task_categories:question-answering, task_categories:summarization, language:en, license:cc-by-sa-3.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.02155, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nSummary\n\t\n\ndatabricks-dolly-15k is an open source dataset of instruction-following records generated by thousands of Databricks employees in several \nof the behavioral categories outlined in the InstructGPT paper, including brainstorming, classification, \nclosed QA, generation, information extraction, open QA, and summarization.\nThis dataset can be used for any purpose, whether academic or commercial,  under the terms of the \nCreative Commons Attribution-ShareAlike 3.0 Unported… See the full description on the dataset page: https://huggingface.co/datasets/databricks/databricks-dolly-15k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `databricks-dolly-15k.jsonl`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `context` | `str` |\n| `response` | `str` |\n| `category` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: bigcode/the-stack
encoding: FLOAT32
metadata:
{"id":"bigcode/the-stack","chroma:document":"# Dataset: `bigcode/the-stack`\n\n## 📝 Metadata\n- **Author:** bigcode\n- **Downloads:** 35469\n- **Likes:** 865\n- **Tags:** task_categories:text-generation, language_creators:crowdsourced, language_creators:expert-generated, multilinguality:multilingual, language:code, license:other, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2211.15533, arxiv:2107.03374, arxiv:2207.14157, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for The Stack\n\t\n\n\n\n\t\n\t\t\n\t\tChangelog\n\t\n\n\n\t\n\t\t\nRelease\nDescription\n\n\n\t\t\nv1.0\nInitial release of the Stack. Included 30 programming languages and 18 permissive licenses. Note: Three included licenses (MPL/EPL/LGPL) are considered weak copyleft licenses. The resulting near-deduplicated dataset is 3TB in size.\n\n\nv1.1\nThe three copyleft licenses ((MPL/EPL/LGPL) were excluded and the list of permissive licenses extended to 193 licenses in total. The list of programming languages… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/the-stack....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/abap/train-00000-of-00001.parquet`\n- `data/actionscript/train-00000-of-00002.parquet`\n- `data/actionscript/train-00001-of-00002.parquet`\n- `data/ada/train-00000-of-00001.parquet`\n- `data/agda/train-00000-of-00001.parquet`\n- `data/ags-script/train-00000-of-00001.parquet`\n- `data/alloy/train-00000-of-00001.parquet`\n- `data/ampl/train-00000-of-00001.parquet`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'bigcode/the-stack' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/bigcode/the-stack to ask for access.\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: anon8231489123/ShareGPT_Vicuna_unfiltered
encoding: FLOAT32
metadata:
{"id":"anon8231489123/ShareGPT_Vicuna_unfiltered","chroma:document":"# Dataset: `anon8231489123/ShareGPT_Vicuna_unfiltered`\n\n## 📝 Metadata\n- **Author:** anon8231489123\n- **Downloads:** 43328\n- **Likes:** 822\n- **Tags:** language:en, license:apache-2.0, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nFurther cleaning done. Please look through the dataset and ensure that I didn't miss anything.\nUpdate: Confirmed working method for training the model: https://huggingface.co/AlekseyKorshuk/vicuna-7b/discussions/4#64346c08ef6d5abefe42c12c\nTwo choices:\n\nRemoves instances of \"I'm sorry, but\": https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/blob/main/ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json\nHas instances of \"I'm sorry, but\":… See the full description on the dataset page: https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `HTML_cleaned_raw_dataset/sg_90k_part1.json`\n- `HTML_cleaned_raw_dataset/sg_90k_part1_html_cleaned.json`\n- `HTML_cleaned_raw_dataset/sg_90k_part2.json`\n- `HTML_cleaned_raw_dataset/sg_90k_part2_html_cleaned.json`\n- `README.md`\n- `ShareGPT_V3_unfiltered_cleaned_split.json`\n- `ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json`\n- `Vicuna_unfiltered_train.ipynb`\n- `clean_sharegpt.py`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: No (supported) data files found in anon8231489123/ShareGPT_Vicuna_unfiltered\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: tatsu-lab/alpaca
encoding: FLOAT32
metadata:
{"id":"tatsu-lab/alpaca","chroma:document":"# Dataset: `tatsu-lab/alpaca`\n\n## 📝 Metadata\n- **Author:** tatsu-lab\n- **Downloads:** 38066\n- **Likes:** 812\n- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction-finetuning\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for Alpaca\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's text-davinci-003 engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\nThe text-davinci-003 engine to generate the instruction data instead… See the full description on the dataset page: https://huggingface.co/datasets/tatsu-lab/alpaca....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/train-00000-of-00001-a09b74b3ef9c3b56.parquet`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n| `text` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceFW/fineweb-edu
encoding: FLOAT32
metadata:
{"id":"HuggingFaceFW/fineweb-edu","chroma:document":"# Dataset: `HuggingFaceFW/fineweb-edu`\n\n## 📝 Metadata\n- **Author:** HuggingFaceFW\n- **Downloads:** 373200\n- **Likes:** 784\n- **Tags:** task_categories:text-generation, language:en, license:odc-by, size_categories:1B<n<10B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.17557, arxiv:2404.14219, arxiv:2401.10020, arxiv:2109.07445, doi:10.57967/hf/2497, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n📚 FineWeb-Edu\n\t\n\n\n    \n\n\n\n1.3 trillion tokens of the finest educational data the 🌐 web has to offer\n\nPaper: https://arxiv.org/abs/2406.17557\n\n\t\n\t\t\n\t\n\t\n\t\tWhat is it?\n\t\n\n📚 FineWeb-Edu  dataset consists of 1.3T tokens  and  5.4T tokens (FineWeb-Edu-score-2) of educational web pages filtered from 🍷 FineWeb dataset. This is the 1.3 trillion version.\nTo enhance FineWeb's quality, we developed an educational quality classifier using annotations generated by LLama3-70B-Instruct. We then… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/CC-MAIN-2013-20/train-00000-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00001-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00002-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00003-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00004-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00005-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00006-of-00014.parquet`\n- `data/CC-MAIN-2013-20/train-00007-of-00014.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `NoneType` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n| `score` | `float` |\n| `int_score` | `int` |\n\n### Config: `sample-10BT`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n| `score` | `float` |\n| `int_score` | `int` |\n\n### Config: `sample-100BT`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `token_count` | `int` |\n| `score` | `float` |\n| `int_score` | `int` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: roneneldan/TinyStories
encoding: FLOAT32
metadata:
{"id":"roneneldan/TinyStories","chroma:document":"# Dataset: `roneneldan/TinyStories`\n\n## 📝 Metadata\n- **Author:** roneneldan\n- **Downloads:** 43353\n- **Likes:** 767\n- **Tags:** task_categories:text-generation, language:en, license:cdla-sharing-1.0, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.07759, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset containing synthetically generated (by GPT-3.5 and GPT-4) short stories that only use a small vocabulary.\nDescribed in the following paper: https://arxiv.org/abs/2305.07759. \nThe models referred to in the paper were trained on TinyStories-train.txt  (the file tinystories-valid.txt can be used for validation loss). These models can be found on Huggingface, at roneneldan/TinyStories-1M/3M/8M/28M/33M/1Layer-21M.\nAdditional resources:\ntinystories_all_data.tar.gz - contains a superset of… See the full description on the dataset page: https://huggingface.co/datasets/roneneldan/TinyStories....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `Evaluation prompts.yaml`\n- `MLP_input_output.npy`\n- `README.md`\n- `TinyStories-train.txt`\n- `TinyStories-valid.txt`\n- `TinyStoriesV2-GPT4-train.txt`\n- `TinyStoriesV2-GPT4-valid.txt`\n- `TinyStories_all_data.tar.gz`\n- `data/train-00000-of-00004-2d5a1467fff1081b.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: open-thoughts/OpenThoughts-114k
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `open-thoughts/OpenThoughts-114k`\n\n## 📝 Metadata\n- **Author:** open-thoughts\n- **Downloads:** 42497\n- **Likes:** 766\n- **Tags:** license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.04178, region:us, curator, synthetic\n- **License:** Not specified\n\n## 📖 Description\n```text\n[!NOTE]\nWe have released a paper for OpenThoughts! See our paper here.\n\n\n \n\n\n\n\t\n\t\t\n\t\n\t\n\t\tOpen-Thoughts-114k\n\t\n\nOpen synthetic reasoning dataset with 114k high-quality examples covering math, science, code, and puzzles!\nInspect the content with rich formatting with Curator Viewer.\n\n\t\n\t\n\t\n\t\tAvailable Subsets\n\t\n\ndefault subset containing ready-to-train data used to finetune the OpenThinker-7B and OpenThinker-32B models:\nds = load_dataset(\"open-thoughts/OpenThoughts-114k\", split=\"train\")… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/OpenThoughts-114k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/train-00000-of-00006.parquet`\n- `data/train-00001-of-00006.parquet`\n- `data/train-00002-of-00006.parquet`\n- `data/train-00003-of-00006.parquet`\n- `data/train-00004-of-00006.parquet`\n- `data/train-00005-of-00006.parquet`\n- `diagram.png`\n- `diagram_dark.png`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `system` | `str` |\n| `conversations` | `list` |\n\n### Config: `metadata`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `problem` | `str` |\n| `deepseek_reasoning` | `str` |\n| `deepseek_solution` | `str` |\n| `ground_truth_solution` | `str` |\n| `domain` | `str` |\n| `source` | `str` |\n| `test_cases` | `NoneType` |\n| `starter_code` | `NoneType` |\n\n","id":"open-thoughts/OpenThoughts-114k"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: teknium/OpenHermes-2.5
encoding: FLOAT32
metadata:
{"id":"teknium/OpenHermes-2.5","chroma:document":"# Dataset: `teknium/OpenHermes-2.5`\n\n## 📝 Metadata\n- **Author:** teknium\n- **Downloads:** 2876\n- **Likes:** 762\n- **Tags:** language:eng, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, synthetic, GPT-4, Distillation, Compilation\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for Dataset Name\n\t\n\nThis is the dataset that made OpenHermes 2.5 and Nous Hermes 2 series of models.\nSupport me on GitHub sponsors <3 : https://github.com/sponsors/teknium1\n\n\t\n\t\t\n\t\tDataset Details\n\t\n\n\n\t\n\t\t\n\t\tDataset Description\n\t\n\nThe Open Hermes 2/2.5 and Nous Hermes 2 models have made significant advancements of SOTA LLM's over recent months, and are underpinned by this exact compilation and curation of many open source datasets and custom created synthetic datasets.… See the full description on the dataset page: https://huggingface.co/datasets/teknium/OpenHermes-2.5....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `openhermes2_5.json`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `custom_instruction` | `NoneType` |\n| `topic` | `NoneType` |\n| `model_name` | `NoneType` |\n| `model` | `NoneType` |\n| `skip_prompt_formatting` | `bool` |\n| `category` | `str` |\n| `conversations` | `list` |\n| `views` | `NoneType` |\n| `language` | `NoneType` |\n| `id` | `NoneType` |\n| `title` | `NoneType` |\n| `idx` | `NoneType` |\n| `hash` | `NoneType` |\n| `avatarUrl` | `NoneType` |\n| `system_prompt` | `NoneType` |\n| `source` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: lmsys/lmsys-chat-1m
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `lmsys/lmsys-chat-1m`\n\n## 📝 Metadata\n- **Author:** lmsys\n- **Downloads:** 9470\n- **Likes:** 749\n- **Tags:** size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.11998, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nLMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset\n\t\n\nThis dataset contains one million real-world conversations with 25 state-of-the-art LLMs.\nIt is collected from 210K unique IP addresses in the wild on the Vicuna demo and Chatbot Arena website from April to August 2023.\nEach sample includes a conversation ID, model name, conversation text in OpenAI API JSON format, detected language tag, and OpenAI moderation API tag.\nUser consent is obtained through the \"Terms of use\"… See the full description on the dataset page: https://huggingface.co/datasets/lmsys/lmsys-chat-1m....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/train-00000-of-00006-4feeb3f83346a0e9.parquet`\n- `data/train-00001-of-00006-4030672591c2f478.parquet`\n- `data/train-00002-of-00006-1779b7cec9462180.parquet`\n- `data/train-00003-of-00006-2fa862bfed56af1f.parquet`\n- `data/train-00004-of-00006-18f4bdd50c103e71.parquet`\n- `data/train-00005-of-00006-fe1acc5d10a9f0e2.parquet`\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'lmsys/lmsys-chat-1m' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/lmsys/lmsys-chat-1m to ask for access.\n```\n","id":"lmsys/lmsys-chat-1m"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: QingyiSi/Alpaca-CoT
encoding: FLOAT32
metadata:
{"id":"QingyiSi/Alpaca-CoT","chroma:document":"# Dataset: `QingyiSi/Alpaca-CoT`\n\n## 📝 Metadata\n- **Author:** QingyiSi\n- **Downloads:** 7158\n- **Likes:** 737\n- **Tags:** language:en, language:zh, language:ml, license:apache-2.0, region:us, Instruction, Cot\n- **License:** Not specified\n\n## 📖 Description\n```text\nInstruction-Finetuning Dataset Collection (Alpaca-CoT)\n\t\n\nThis repository will continuously collect various instruction tuning datasets. And we standardize different datasets into the same format, which can be directly loaded by the code of Alpaca model.\nWe also have conducted empirical study on various instruction-tuning datasets based on the Alpaca model, as shown in https://github.com/PhoebusSi/alpaca-CoT.  \nIf you think this dataset collection is helpful to you, please like this… See the full description on the dataset page: https://huggingface.co/datasets/QingyiSi/Alpaca-CoT....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `Auto-CoT/Auto-CoT.json`\n- `Auto-CoT/Auto.json`\n- `COIG/counterfactural_correction_multi_round_chat.json`\n- `COIG/counterfactural_correction_multi_round_chat_context.json`\n- `COIG/exam.json`\n- `COIG/human-value-alignment_common.json`\n- `COIG/human-value-alignment_special.json`\n- `COIG/leetcode.json`\n- `COIG/translate_en.json`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: yahma/alpaca-cleaned
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `yahma/alpaca-cleaned`\n\n## 📝 Metadata\n- **Author:** yahma\n- **Downloads:** 26690\n- **Likes:** 736\n- **Tags:** task_categories:text-generation, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us, instruction-finetuning\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for Alpaca-Cleaned\n\t\n\n\nRepository: https://github.com/gururise/AlpacaDataCleaned\n\n\n\t\n\t\t\n\t\tDataset Description\n\t\n\nThis is a cleaned version of the original Alpaca Dataset released by Stanford. The following issues have been identified in the original release and fixed in this dataset:\n\nHallucinations: Many instructions in the original dataset had instructions referencing data on the internet, which just caused GPT3 to hallucinate an answer.\n\n\"instruction\":\"Summarize the… See the full description on the dataset page: https://huggingface.co/datasets/yahma/alpaca-cleaned....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `alpaca_data_cleaned.json`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `output` | `str` |\n| `input` | `str` |\n| `instruction` | `str` |\n\n","id":"yahma/alpaca-cleaned"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Congliu/Chinese-DeepSeek-R1-Distill-data-110k
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `Congliu/Chinese-DeepSeek-R1-Distill-data-110k`\n\n## 📝 Metadata\n- **Author:** Congliu\n- **Downloads:** 767\n- **Likes:** 703\n- **Tags:** task_categories:text-generation, task_categories:question-answering, language:zh, license:apache-2.0, size_categories:100K<n<1M, format:json, modality:tabular, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n中文基于满血DeepSeek-R1蒸馏数据集（Chinese-Data-Distill-From-R1）\n\t\n\n\n🤗 Hugging Face   |   🤖 ModelScope    |   🚀 Github    |   📑 Blog\n\n\n注意：提供了直接SFT使用的版本，点击下载。将数据中的思考和答案整合成output字段，大部分SFT代码框架均可直接直接加载训练。\n本数据集为中文开源蒸馏满血R1的数据集，数据集中不仅包含math数据，还包括大量的通用类型数据，总数量为110K。\n为什么开源这个数据？\nR1的效果十分强大，并且基于R1蒸馏数据SFT的小模型也展现出了强大的效果，但检索发现，大部分开源的R1蒸馏数据集均为英文数据集。 同时，R1的报告中展示，蒸馏模型中同时也使用了部分通用场景数据集。\n为了帮助大家更好地复现R1蒸馏模型的效果，特此开源中文数据集。该中文数据集中的数据分布如下：\n\nMath：共计36568个样本，\nExam：共计2432个样本，\nSTEM：共计12648个样本，… See the full description on the dataset page: https://huggingface.co/datasets/Congliu/Chinese-DeepSeek-R1-Distill-data-110k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `distill_r1_110k.jsonl`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `input` | `str` |\n| `content` | `str` |\n| `reasoning_content` | `str` |\n| `repo_name` | `str` |\n| `prompt_tokens_len` | `int` |\n| `content_tokens_len` | `int` |\n| `reasoning_content_tokens_len` | `int` |\n| `score` | `int` |\n\n","id":"Congliu/Chinese-DeepSeek-R1-Distill-data-110k"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceFW/fineweb-2
encoding: FLOAT32
metadata:
{"id":"HuggingFaceFW/fineweb-2","chroma:document":"# Dataset: `HuggingFaceFW/fineweb-2`\n\n## 📝 Metadata\n- **Author:** HuggingFaceFW\n- **Downloads:** 114244\n- **Likes:** 676\n- **Tags:** task_categories:text-generation, language:aai, language:aak, language:aau, language:aaz, language:aba, language:abi, language:abk, language:abn, language:abq, language:abs, language:abt, language:abx, language:aby, language:abz, language:aca, language:acd, language:ace, language:acf, language:ach, language:acm, language:acn, language:acr, language:acu, language:ada, language:ade, language:adh, language:adi, language:adj, language:adl, language:ady, language:adz, language:aeb, language:aer, language:aeu, language:aey, language:afr, language:agd, language:agg, language:agm, language:agn, language:agr, language:agt, language:agu, language:agw, language:agx, language:aha, language:ahk, language:aia, language:aii, language:aim, language:ain, language:ajg, language:aji, language:ajz, language:akb, 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language:quy, language:quz, language:qva, language:qvc, language:qve, language:qvh, language:qvi, language:qvm, language:qvn, language:qvo, language:qvs, language:qvw, language:qvz, language:qwh, language:qxh, language:qxl, language:qxn, language:qxo, language:qxr, language:rad, language:rai, language:rap, language:rar, language:rav, language:raw, language:rcf, language:rej, language:rel, language:rgu, language:rhg, language:ria, language:rim, language:rjs, language:rkb, language:rmc, language:rme, language:rml, language:rmn, language:rmo, language:rmq, language:rmy, language:rnd, language:rng, language:rnl, language:roh, language:ron, language:roo, language:rop, language:row, language:rro, language:rtm, language:rub, language:rue, language:ruf, language:rug, language:run, language:rup, language:rus, language:rwo, language:sab, language:sag, language:sah, language:san, language:sas, language:sat, language:sba, language:sbd, language:sbe, language:sbl, language:sbs, language:sby, language:sck, language:scn, language:sco, language:sda, language:sdc, language:sdh, language:sdo, language:sdq, language:seh, language:ses, language:sey, language:sfw, language:sgb, language:sgc, language:sgh, language:sgs, language:sgw, language:sgz, language:shi, language:shk, language:shn, language:shp, language:shu, language:sid, language:sig, language:sil, language:sim, language:sin, language:sja, language:sjo, language:sju, language:skg, language:skr, language:sld, language:slk, language:sll, language:slv, language:sma, language:sme, language:smj, language:smk, language:sml, language:smn, language:smo, language:sms, language:smt, language:sna, language:snc, language:snd, language:snf, language:snn, language:snp, language:snw, language:sny, language:soe, language:som, language:sop, language:soq, language:sot, language:soy, language:spa, language:spl, language:spm, language:spp, language:sps, language:spy, language:srd, language:sri, language:srm, language:srn, language:srp, language:srq, language:srr, language:ssd, language:ssg, language:ssw, language:ssx, language:stn, language:stp, language:stq, language:sua, language:suc, language:sue, language:suk, language:sun, language:sur, language:sus, language:suz, language:swb, language:swc, language:swe, language:swg, language:swh, language:swk, language:swp, language:sxb, language:sxn, language:syb, language:syc, language:syl, language:szl, language:szy, language:tab, language:tac, language:tah, language:taj, language:tam, language:tap, language:taq, language:tar, language:tat, language:tav, language:taw, language:tay, language:tbc, language:tbg, language:tbk, language:tbl, language:tbo, language:tbw, language:tby, language:tbz, language:tca, language:tcc, language:tcf, language:tcs, language:tcy, language:tcz, language:ted, language:tee, language:tel, language:tem, language:teo, language:ter, language:tet, language:tew, language:tfr, language:tgk, language:tgo, language:tgp, language:tha, language:thk, language:thl, language:tif, language:tig, language:tih, language:tik, language:tim, language:tir, language:tiv, language:tiy, language:tke, language:tkl, language:tkr, language:tku, language:tlb, language:tlf, language:tlh, language:tlj, language:tll, language:tly, language:tmc, language:tmd, language:tna, language:tnc, language:tnk, language:tnn, language:tnp, language:tnr, language:tob, language:toc, language:tod, language:tog, language:toh, language:toi, language:toj, language:tok, language:ton, language:too, language:top, language:tos, language:tpa, language:tpi, language:tpm, language:tpp, language:tpt, language:tpw, language:tpz, language:tqo, language:trc, language:trn, language:tro, language:trp, language:trq, language:trs, language:trv, language:tsc, language:tsg, language:tsn, language:tso, language:tsw, language:tsz, language:ttc, language:tte, language:ttj, language:ttq, language:tuc, language:tue, language:tuf, language:tui, language:tuk, language:tul, language:tum, language:tuo, language:tur, language:tuv, language:tvk, language:tvl, language:twi, language:twu, language:twx, language:txq, language:txu, language:tyv, language:tzh, language:tzj, language:tzl, language:tzm, language:tzo, language:ubr, language:ubu, language:udm, language:udu, language:uig, language:ukr, language:umb, language:upv, language:ura, language:urb, language:urd, language:urh, language:uri, language:urk, language:urt, language:urw, language:ury, language:usa, language:usp, language:uth, language:uvh, language:uvl, language:uzn, language:uzs, language:vag, language:vap, language:var, language:vec, language:ven, language:vep, language:vid, language:vie, language:viv, language:vls, language:vmk, language:vmw, language:vmy, language:vol, language:vot, language:vro, language:vun, language:vut, language:waj, language:wal, language:wap, language:war, language:wat, language:way, language:wba, language:wbm, language:wbp, language:wed, language:wer, language:wes, language:wew, language:whg, language:whk, language:wib, language:wim, language:wiu, language:wln, language:wls, language:wlv, language:wlx, language:wmt, language:wmw, language:wnc, language:wnu, language:wob, language:wol, language:wos, language:wrk, language:wrs, language:wsg, language:wsk, language:wuu, language:wuv, language:wwa, language:xal, language:xav, language:xbi, language:xbr, language:xed, language:xho, language:xla, language:xmf, language:xmm, language:xmv, language:xnn, language:xog, language:xon, language:xrb, language:xsb, language:xsi, language:xsm, language:xsr, language:xsu, language:xtd, language:xtm, language:xtn, language:xuo, language:yaa, language:yad, language:yal, language:yam, language:yan, language:yao, language:yap, language:yaq, language:yat, language:yaz, language:ybb, language:yby, language:ycn, language:ydd, language:yim, language:yka, language:yle, language:yli, language:yml, language:yom, language:yon, language:yor, language:yrb, language:yre, language:yrk, language:yrl, language:yss, language:yua, language:yue, language:yuj, language:yup, language:yut, language:yuw, language:yuz, language:yva, language:zaa, language:zab, language:zac, language:zad, language:zae, language:zai, language:zam, language:zao, language:zar, language:zas, language:zat, language:zav, language:zaw, language:zca, language:zdj, language:zea, language:zgh, language:zia, language:ziw, language:zne, language:zom, language:zos, language:zpa, language:zpc, language:zpg, language:zpi, language:zpj, language:zpl, language:zpm, language:zpo, language:zpq, language:zpt, language:zpu, language:zpv, language:zpz, language:zsm, language:zsr, language:ztq, language:zty, language:zul, language:zyb, language:zyp, license:odc-by, size_categories:1B<n<10B, modality:tabular, modality:text, arxiv:2506.20920, arxiv:2109.07445, arxiv:2406.17557, doi:10.57967/hf/3744, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\n🥂 FineWeb2\n\t\n\n\n    \n\n\n\nA sparkling update with 1000s of languages\n\n\n\t\n\t\t\n\t\tWhat is it?\n\t\n\nThis is the second iteration of the popular 🍷 FineWeb dataset, bringing high quality pretraining data to over 1000 🗣️ languages.\nThe 🥂 FineWeb2 dataset is fully reproducible, available under the permissive ODC-By 1.0 license and extensively validated through hundreds of ablation experiments.\nIn particular, on the set of 9 diverse languages we used to guide our processing decisions, 🥂 FineWeb2… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/aai_Latn/test/000_00000.parquet`\n- `data/aai_Latn/train/000_00000.parquet`\n- `data/aai_Latn_removed/train/000_00000.parquet`\n- `data/aak_Latn/test/000_00000.parquet`\n- `data/aak_Latn/train/000_00000.parquet`\n- `data/aak_Latn_removed/train/000_00000.parquet`\n- `data/aau_Latn/test/000_00000.parquet`\n- `data/aau_Latn/train/000_00000.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `aai_Latn`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n\n### Config: `aak_Latn`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n\n### Config: `aau_Latn`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `language` | `str` |\n| `language_score` | `float` |\n| `language_script` | `str` |\n| `minhash_cluster_size` | `int` |\n| `top_langs` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: BAAI/Infinity-Instruct
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `BAAI/Infinity-Instruct`\n\n## 📝 Metadata\n- **Author:** BAAI\n- **Downloads:** 2673\n- **Likes:** 670\n- **Tags:** task_categories:text-generation, language:en, language:zh, license:cc-by-sa-4.0, size_categories:10M<n<100M, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.11116, arxiv:2402.00530, arxiv:2405.19327, arxiv:2409.07045, arxiv:2408.07089, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nInfinity Instruct\n\t\n\n\n\n\n\nBeijing Academy of Artificial Intelligence (BAAI)\n [Paper][Code][🤗] \n\n\nThe quality and scale of instruction data are crucial for model performance. Recently, open-source models have increasingly relied on fine-tuning datasets comprising millions of instances, necessitating both high quality and large scale. However, the open-source community has long been constrained by the high costs associated with building such extensive and high-quality instruction… See the full description on the dataset page: https://huggingface.co/datasets/BAAI/Infinity-Instruct....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `0625/train-00000-of-00007.parquet`\n- `0625/train-00001-of-00007.parquet`\n- `0625/train-00002-of-00007.parquet`\n- `0625/train-00003-of-00007.parquet`\n- `0625/train-00004-of-00007.parquet`\n- `0625/train-00005-of-00007.parquet`\n- `0625/train-00006-of-00007.parquet`\n- `3M/train-00000-of-00035.parquet`\n- `3M/train-00001-of-00035.parquet`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'BAAI/Infinity-Instruct' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/BAAI/Infinity-Instruct to ask for access.\n```\n","id":"BAAI/Infinity-Instruct"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: open-r1/OpenR1-Math-220k
encoding: FLOAT32
metadata:
{"id":"open-r1/OpenR1-Math-220k","chroma:document":"# Dataset: `open-r1/OpenR1-Math-220k`\n\n## 📝 Metadata\n- **Author:** open-r1\n- **Downloads:** 7623\n- **Likes:** 658\n- **Tags:** language:en, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nOpenR1-Math-220k\n\t\n\n\n\t\n\t\t\n\t\tDataset description\n\t\n\nOpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5. \nThe traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.\nThe dataset consists of two splits:… See the full description on the dataset page: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `all/default-00000-of-00010.parquet`\n- `all/default-00001-of-00010.parquet`\n- `all/default-00002-of-00010.parquet`\n- `all/default-00003-of-00010.parquet`\n- `all/default-00004-of-00010.parquet`\n- `all/default-00005-of-00010.parquet`\n- `all/default-00006-of-00010.parquet`\n- `all/default-00007-of-00010.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `all`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `problem` | `str` |\n| `solution` | `str` |\n| `answer` | `str` |\n| `problem_type` | `str` |\n| `question_type` | `str` |\n| `source` | `str` |\n| `uuid` | `str` |\n| `is_reasoning_complete` | `list` |\n| `generations` | `list` |\n| `correctness_math_verify` | `list` |\n| `correctness_llama` | `list` |\n| `finish_reasons` | `NoneType` |\n| `correctness_count` | `int` |\n| `messages` | `list` |\n\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `problem` | `str` |\n| `solution` | `str` |\n| `answer` | `str` |\n| `problem_type` | `str` |\n| `question_type` | `str` |\n| `source` | `str` |\n| `uuid` | `str` |\n| `is_reasoning_complete` | `list` |\n| `generations` | `list` |\n| `correctness_math_verify` | `list` |\n| `correctness_llama` | `NoneType` |\n| `finish_reasons` | `NoneType` |\n| `correctness_count` | `int` |\n| `messages` | `list` |\n\n### Config: `extended`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `problem` | `str` |\n| `solution` | `str` |\n| `answer` | `str` |\n| `problem_type` | `str` |\n| `question_type` | `str` |\n| `source` | `str` |\n| `uuid` | `str` |\n| `is_reasoning_complete` | `list` |\n| `generations` | `list` |\n| `correctness_math_verify` | `list` |\n| `correctness_llama` | `NoneType` |\n| `finish_reasons` | `NoneType` |\n| `correctness_count` | `int` |\n| `messages` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: m-a-p/COIG-CQIA
encoding: FLOAT32
metadata:
{"id":"m-a-p/COIG-CQIA","chroma:document":"# Dataset: `m-a-p/COIG-CQIA`\n\n## 📝 Metadata\n- **Author:** m-a-p\n- **Downloads:** 4219\n- **Likes:** 654\n- **Tags:** task_categories:question-answering, task_categories:text-classification, task_categories:text-generation, language:zh, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2403.18058, arxiv:2304.07987, arxiv:2307.09705, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nCOIG-CQIA：Quality is All you need for Chinese Instruction Fine-tuning\n\t\n\n\n\n\n\n\t\n\t\t\n\t\tDataset Details\n\t\n\n\n\t\n\t\t\n\t\tDataset Description\n\t\n\n\n欢迎来到COIG-CQIA，COIG-CQIA全称为Chinese Open Instruction Generalist - Quality is All You Need， 是一个开源的高质量指令微调数据集，旨在为中文NLP社区提供高质量且符合人类交互行为的指令微调数据。COIG-CQIA以中文互联网获取到的问答及文章作为原始数据，经过深度清洗、重构及人工审核构建而成。本项目受LIMA: Less Is More for Alignment等研究启发，使用少量高质量的数据即可让大语言模型学习到人类交互行为，因此在数据构建中我们十分注重数据的来源、质量与多样性，数据集详情请见数据介绍以及我们接下来的论文。\nWelcome to the COIG-CQIA… See the full description on the dataset page: https://huggingface.co/datasets/m-a-p/COIG-CQIA....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `COIG-CQIA-full.jsonl`\n- `README.md`\n- `Yi_logo.svg`\n- `chinese_traditional/chengyu.jsonl`\n- `chinese_traditional/poem.jsonl`\n- `chinese_traditional/trad-multi-choice-100-2.jsonl`\n- `chinese_traditional/trad-multi-choice-100.jsonl`\n- `chinese_traditional/trad-multi-choice-40.jsonl`\n- `chinese_traditional/translate_classical_chinese.jsonl`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `chinese_traditional`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n| `task_type` | `dict` |\n| `domain` | `list` |\n| `metadata` | `str` |\n| `answer_from` | `str` |\n| `human_verified` | `bool` |\n| `copyright` | `str` |\n\n### Config: `coig_pc`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n| `task_type` | `dict` |\n| `domain` | `list` |\n| `metadata` | `str` |\n| `answer_from` | `str` |\n| `human_verified` | `bool` |\n| `copyright` | `str` |\n\n### Config: `exam`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `instruction` | `str` |\n| `input` | `str` |\n| `output` | `str` |\n| `task_type` | `dict` |\n| `domain` | `list` |\n| `metadata` | `str` |\n| `answer_from` | `str` |\n| `human_verified` | `bool` |\n| `copyright` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceTB/cosmopedia
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `HuggingFaceTB/cosmopedia`\n\n## 📝 Metadata\n- **Author:** HuggingFaceTB\n- **Downloads:** 37803\n- **Likes:** 642\n- **Tags:** language:en, license:apache-2.0, size_categories:10M<n<100M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.05463, arxiv:2306.11644, region:us, synthetic\n- **License:** Not specified\n\n## 📖 Description\n```text\nCosmopedia v0.1\n\t\n\n\n    \n    Image generated by DALL-E, the prompt was generated by Mixtral-8x7B-Instruct-v0.1\n\n\nNote: Cosmopedia v0.2 is available at smollm-corpus\nUser: What do you think \"Cosmopedia\" could mean? Hint: in our case it's not related to cosmology.\n\nMixtral-8x7B-Instruct-v0.1: A possible meaning for \"Cosmopedia\" could be an encyclopedia or collection of information about\ndifferent cultures, societies, and topics from around the world, emphasizing diversity and global… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/cosmopedia....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/auto_math_text/train-00000-of-00018.parquet`\n- `data/auto_math_text/train-00001-of-00018.parquet`\n- `data/auto_math_text/train-00002-of-00018.parquet`\n- `data/auto_math_text/train-00003-of-00018.parquet`\n- `data/auto_math_text/train-00004-of-00018.parquet`\n- `data/auto_math_text/train-00005-of-00018.parquet`\n- `data/auto_math_text/train-00006-of-00018.parquet`\n- `data/auto_math_text/train-00007-of-00018.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `auto_math_text`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `text_token_length` | `int` |\n| `text` | `str` |\n| `seed_data` | `str` |\n| `format` | `str` |\n| `audience` | `str` |\n\n### Config: `khanacademy`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `text_token_length` | `int` |\n| `text` | `str` |\n| `seed_data` | `str` |\n| `format` | `str` |\n| `audience` | `str` |\n\n### Config: `openstax`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text_token_length` | `int` |\n| `prompt` | `str` |\n| `text` | `str` |\n| `seed_data` | `str` |\n| `format` | `str` |\n| `audience` | `str` |\n\n","id":"HuggingFaceTB/cosmopedia"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: proj-persona/PersonaHub
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `proj-persona/PersonaHub`\n\n## 📝 Metadata\n- **Author:** proj-persona\n- **Downloads:** 12977\n- **Likes:** 642\n- **Tags:** task_categories:text-generation, task_categories:text-classification, task_categories:token-classification, task_categories:fill-mask, task_categories:table-question-answering, language:en, language:zh, license:cc-by-nc-sa-4.0, size_categories:100K<n<1M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2406.20094, region:us, synthetic, text, math, reasoning, instruction, tool, persona\n- **License:** Not specified\n\n## 📖 Description\n```text\nScaling Synthetic Data Creation with 1,000,000,000 Personas\n\t\n\nThis repo releases data introduced in our paper Scaling Synthetic Data Creation with 1,000,000,000 Personas:\nWe propose a novel persona-driven data synthesis methodology that leverages various perspectives within a large language model (LLM) to create diverse synthetic data. To fully exploit this methodology at scale, we introduce PERSONA HUB – a collection of 1 billion diverse personas automatically curated from web data.… See the full description on the dataset page: https://huggingface.co/datasets/proj-persona/PersonaHub....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `ElitePersonas/elite_personas.part1.jsonl`\n- `ElitePersonas/elite_personas.part10.jsonl`\n- `ElitePersonas/elite_personas.part11.jsonl`\n- `ElitePersonas/elite_personas.part12.jsonl`\n- `ElitePersonas/elite_personas.part13.jsonl`\n- `ElitePersonas/elite_personas.part14.jsonl`\n- `ElitePersonas/elite_personas.part15.jsonl`\n- `ElitePersonas/elite_personas.part16.jsonl`\n- `ElitePersonas/elite_personas.part17.jsonl`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `math`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `input persona` | `str` |\n| `synthesized text` | `str` |\n| `description` | `str` |\n\n### Config: `instruction`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `input persona` | `str` |\n| `synthesized text` | `str` |\n| `description` | `str` |\n\n### Config: `reasoning`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `input persona` | `str` |\n| `synthesized text` | `str` |\n| `description` | `str` |\n\n","id":"proj-persona/PersonaHub"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceFW/finepdfs
encoding: FLOAT32
metadata:
{"id":"HuggingFaceFW/finepdfs","chroma:document":"# Dataset: `HuggingFaceFW/finepdfs`\n\n## 📝 Metadata\n- **Author:** HuggingFaceFW\n- **Downloads:** 46934\n- **Likes:** 630\n- **Tags:** task_categories:text-generation, language:aai, language:aak, language:aau, language:aaz, language:aba, language:abi, language:abk, language:abn, language:abq, language:abs, language:abt, language:abx, language:aby, language:abz, language:aca, language:acd, language:ace, language:acf, language:ach, language:acm, language:acn, language:acr, language:acu, language:ada, language:ade, language:adh, language:adi, language:adj, language:adl, language:ady, language:adz, language:aeb, language:aer, language:aeu, language:aey, language:afr, language:agd, language:agg, language:agm, language:agn, language:agr, language:agt, language:agu, language:agw, language:agx, language:aha, language:ahk, language:aia, language:aii, language:aim, language:ain, language:ajg, language:aji, language:ajz, language:akb, 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language:quy, language:quz, language:qva, language:qvc, language:qve, language:qvh, language:qvi, language:qvm, language:qvn, language:qvo, language:qvs, language:qvw, language:qvz, language:qwh, language:qxh, language:qxl, language:qxn, language:qxo, language:qxr, language:rad, language:rai, language:rap, language:rar, language:rav, language:raw, language:rcf, language:rej, language:rel, language:rgu, language:rhg, language:ria, language:rim, language:rjs, language:rkb, language:rmc, language:rme, language:rml, language:rmn, language:rmo, language:rmq, language:rmy, language:rnd, language:rng, language:rnl, language:roh, language:ron, language:roo, language:rop, language:row, language:rro, language:rtm, language:rub, language:rue, language:ruf, language:rug, language:run, language:rup, language:rus, language:rwo, language:sab, language:sag, language:sah, language:san, language:sas, language:sat, language:sba, language:sbd, language:sbe, language:sbl, language:sbs, language:sby, language:sck, language:scn, language:sco, language:sda, language:sdc, language:sdh, language:sdo, language:sdq, language:seh, language:ses, language:sey, language:sfw, language:sgb, language:sgc, language:sgh, language:sgs, language:sgw, language:sgz, language:shi, language:shk, language:shn, language:shp, language:shu, language:sid, language:sig, language:sil, language:sim, language:sin, language:sja, language:sjo, language:sju, language:skg, language:skr, language:sld, language:slk, language:sll, language:slv, language:sma, language:sme, language:smj, language:smk, language:sml, language:smn, language:smo, language:sms, language:smt, language:sna, language:snc, language:snd, language:snf, language:snn, language:snp, language:snw, language:sny, language:soe, language:som, language:sop, language:soq, language:sot, language:soy, language:spa, language:spl, language:spm, language:spp, language:sps, language:spy, language:srd, language:sri, language:srm, language:srn, language:srp, language:srq, language:srr, language:ssd, language:ssg, language:ssw, language:ssx, language:stn, language:stp, language:stq, language:sua, language:suc, language:sue, language:suk, language:sun, language:sur, language:sus, language:suz, language:swb, language:swc, language:swe, language:swg, language:swh, language:swk, language:swp, language:sxb, language:sxn, language:syb, language:syc, language:syl, language:szl, language:szy, language:tab, language:tac, language:tah, language:taj, language:tam, language:tap, language:taq, language:tar, language:tat, language:tav, language:taw, language:tay, language:tbc, language:tbg, language:tbk, language:tbl, language:tbo, language:tbw, language:tby, language:tbz, language:tca, language:tcc, language:tcf, language:tcs, language:tcy, language:tcz, language:ted, language:tee, language:tel, language:tem, language:teo, language:ter, language:tet, language:tew, language:tfr, language:tgk, language:tgo, language:tgp, language:tha, language:thk, language:thl, language:tif, language:tig, language:tih, language:tik, language:tim, language:tir, language:tiv, language:tiy, language:tke, language:tkl, language:tkr, language:tku, language:tlb, language:tlf, language:tlh, language:tlj, language:tll, language:tly, language:tmc, language:tmd, language:tna, language:tnc, language:tnk, language:tnn, language:tnp, language:tnr, language:tob, language:toc, language:tod, language:tog, language:toh, language:toi, language:toj, language:tok, language:ton, language:too, language:top, language:tos, language:tpa, language:tpi, language:tpm, language:tpp, language:tpt, language:tpw, language:tpz, language:tqo, language:trc, language:trn, language:tro, language:trp, language:trq, language:trs, language:trv, language:tsc, language:tsg, language:tsn, language:tso, language:tsw, language:tsz, language:ttc, language:tte, language:ttj, language:ttq, language:tuc, language:tue, language:tuf, language:tui, language:tuk, language:tul, language:tum, language:tuo, language:tur, language:tuv, language:tvk, language:tvl, language:twi, language:twu, language:twx, language:txq, language:txu, language:tyv, language:tzh, language:tzj, language:tzl, language:tzm, language:tzo, language:ubr, language:ubu, language:udm, language:udu, language:uig, language:ukr, language:umb, language:upv, language:ura, language:urb, language:urd, language:urh, language:uri, language:urk, language:urt, language:urw, language:ury, language:usa, language:usp, language:uth, language:uvh, language:uvl, language:uzn, language:uzs, language:vag, language:vap, language:var, language:vec, language:ven, language:vep, language:vid, language:vie, language:viv, language:vls, language:vmk, language:vmw, language:vmy, language:vol, language:vot, language:vro, language:vun, language:vut, language:waj, language:wal, language:wap, language:war, language:wat, language:way, language:wba, language:wbm, language:wbp, language:wed, language:wer, language:wes, language:wew, language:whg, language:whk, language:wib, language:wim, language:wiu, language:wln, language:wls, language:wlv, language:wlx, language:wmt, language:wmw, language:wnc, language:wnu, language:wob, language:wol, language:wos, language:wrk, language:wrs, language:wsg, language:wsk, language:wuu, language:wuv, language:wwa, language:xal, language:xav, language:xbi, language:xbr, language:xed, language:xho, language:xla, language:xmf, language:xmm, language:xmv, language:xnn, language:xog, language:xon, language:xrb, language:xsb, language:xsi, language:xsm, language:xsr, language:xsu, language:xtd, language:xtm, language:xtn, language:xuo, language:yaa, language:yad, language:yal, language:yam, language:yan, language:yao, language:yap, language:yaq, language:yat, language:yaz, language:ybb, language:yby, language:ycn, language:ydd, language:yim, language:yka, language:yle, language:yli, language:yml, language:yom, language:yon, language:yor, language:yrb, language:yre, language:yrk, language:yrl, language:yss, language:yua, language:yue, language:yuj, language:yup, language:yut, language:yuw, language:yuz, language:yva, language:zaa, language:zab, language:zac, language:zad, language:zae, language:zai, language:zam, language:zao, language:zar, language:zas, language:zat, language:zav, language:zaw, language:zca, language:zdj, language:zea, language:zgh, language:zia, language:ziw, language:zne, language:zom, language:zos, language:zpa, language:zpc, language:zpg, language:zpi, language:zpj, language:zpl, language:zpm, language:zpo, language:zpq, language:zpt, language:zpu, language:zpv, language:zpz, language:zsm, language:zsr, language:ztq, language:zty, language:zul, language:zyb, language:zyp, license:odc-by, size_categories:100M<n<1B, format:parquet, modality:tabular, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2506.18421, arxiv:2109.07445, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nLiberating 3T of the finest tokens from PDFs\n\n\n\t\n\t\t\n\t\tWhat is this?\n\t\n\nAs we run out of web pages to process, the natural question has always been: what to do next? Only a few knew about a data source that everyone avoided for ages, due to its incredible extraction cost and complexity: PDFs.\n📄 FinePDFs is exactly that. It is the largest publicly available corpus sourced exclusively from PDFs, containing about 3 trillion tokens across 475 million documents in 1733 languages.\nCompared to HTML… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceFW/finepdfs....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/aai_Latn/train/000_00000.parquet`\n- `data/aak_Latn/train/000_00000.parquet`\n- `data/aau_Latn/train/000_00000.parquet`\n- `data/aaz_Latn/train/000_00000.parquet`\n- `data/aba_Latn/train/000_00000.parquet`\n- `data/abk_Cyrl/test/000_00000.parquet`\n- `data/abk_Cyrl/train/000_00000.parquet`\n- `data/abq_Cyrl/train/000_00000.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `aai_Latn`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `offset` | `int` |\n| `token_count` | `int` |\n| `language` | `str` |\n| `page_average_lid` | `str` |\n| `page_average_lid_score` | `float` |\n| `full_doc_lid` | `str` |\n| `full_doc_lid_score` | `float` |\n| `per_page_languages` | `list` |\n| `is_truncated` | `bool` |\n| `extractor` | `str` |\n| `page_ends` | `list` |\n\n### Config: `aak_Latn`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `offset` | `int` |\n| `token_count` | `int` |\n| `language` | `str` |\n| `page_average_lid` | `str` |\n| `page_average_lid_score` | `float` |\n| `full_doc_lid` | `str` |\n| `full_doc_lid_score` | `float` |\n| `per_page_languages` | `list` |\n| `is_truncated` | `bool` |\n| `extractor` | `str` |\n| `page_ends` | `list` |\n\n### Config: `aau_Latn`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n| `id` | `str` |\n| `dump` | `str` |\n| `url` | `str` |\n| `date` | `str` |\n| `file_path` | `str` |\n| `offset` | `int` |\n| `token_count` | `int` |\n| `language` | `str` |\n| `page_average_lid` | `str` |\n| `page_average_lid_score` | `float` |\n| `full_doc_lid` | `str` |\n| `full_doc_lid_score` | `float` |\n| `per_page_languages` | `list` |\n| `is_truncated` | `bool` |\n| `extractor` | `str` |\n| `page_ends` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: legacy-datasets/wikipedia
encoding: FLOAT32
metadata:
{"id":"legacy-datasets/wikipedia","chroma:document":"# Dataset: `legacy-datasets/wikipedia`\n\n## 📝 Metadata\n- **Author:** legacy-datasets\n- **Downloads:** 25891\n- **Likes:** 604\n- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:multilingual, source_datasets:original, language:aa, language:ab, language:ace, language:af, language:ak, language:als, language:am, language:an, language:ang, language:ar, language:arc, language:arz, language:as, language:ast, language:atj, language:av, language:ay, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bh, language:bi, language:bjn, language:bm, language:bn, language:bo, language:bpy, language:br, language:bs, language:bug, language:bxr, language:ca, language:cbk, language:cdo, language:ce, language:ceb, language:ch, language:cho, language:chr, language:chy, language:ckb, language:co, language:cr, language:crh, language:cs, language:csb, language:cu, language:cv, language:cy, language:da, language:de, language:din, language:diq, language:dsb, language:dty, language:dv, language:dz, language:ee, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:ext, language:fa, language:ff, language:fi, language:fj, language:fo, language:fr, language:frp, language:frr, language:fur, language:fy, language:ga, language:gag, language:gan, language:gd, language:gl, language:glk, language:gn, language:gom, language:gor, language:got, language:gu, language:gv, language:ha, language:hak, language:haw, language:he, language:hi, language:hif, language:ho, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ig, language:ii, language:ik, language:ilo, language:inh, language:io, language:is, language:it, language:iu, language:ja, language:jam, language:jbo, language:jv, language:ka, language:kaa, language:kab, language:kbd, language:kbp, language:kg, language:ki, language:kj, language:kk, language:kl, language:km, language:kn, language:ko, language:koi, language:krc, language:ks, language:ksh, language:ku, language:kv, language:kw, language:ky, language:la, language:lad, language:lb, language:lbe, language:lez, language:lfn, language:lg, language:li, language:lij, language:lmo, language:ln, language:lo, language:lrc, language:lt, language:ltg, language:lv, language:lzh, language:mai, language:mdf, language:mg, language:mh, language:mhr, language:mi, language:min, language:mk, language:ml, language:mn, language:mr, language:mrj, language:ms, language:mt, language:mus, language:mwl, language:my, language:myv, language:mzn, language:na, language:nah, language:nan, language:nap, language:nds, language:ne, language:new, language:ng, language:nl, language:nn, language:no, language:nov, language:nrf, language:nso, language:nv, language:ny, language:oc, language:olo, language:om, language:or, language:os, language:pa, language:pag, language:pam, language:pap, language:pcd, language:pdc, language:pfl, language:pi, language:pih, language:pl, language:pms, language:pnb, language:pnt, language:ps, language:pt, language:qu, language:rm, language:rmy, language:rn, language:ro, language:ru, language:rue, language:rup, language:rw, language:sa, language:sah, language:sat, language:sc, language:scn, language:sco, language:sd, language:se, language:sg, language:sgs, language:sh, language:si, language:sk, language:sl, language:sm, language:sn, language:so, language:sq, language:sr, language:srn, language:ss, language:st, language:stq, language:su, language:sv, language:sw, language:szl, language:ta, language:tcy, language:tdt, language:te, language:tg, language:th, language:ti, language:tk, language:tl, language:tn, language:to, language:tpi, language:tr, language:ts, language:tt, language:tum, language:tw, language:ty, language:tyv, language:udm, language:ug, language:uk, language:ur, language:uz, language:ve, language:vec, language:vep, language:vi, language:vls, language:vo, language:vro, language:wa, language:war, language:wo, language:wuu, language:xal, language:xh, language:xmf, language:yi, language:yo, language:yue, language:za, language:zea, language:zh, language:zu, license:cc-by-sa-3.0, license:gfdl, size_categories:n<1K, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nWikipedia dataset containing cleaned articles of all languages.\nThe datasets are built from the Wikipedia dump\n(https://dumps.wikimedia.org/) with one split per language. Each example\ncontains the content of one full Wikipedia article with cleaning to strip\nmarkdown and unwanted sections (references, etc.)....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/20220301.de/train-00000-of-00018.parquet`\n- `data/20220301.de/train-00001-of-00018.parquet`\n- `data/20220301.de/train-00002-of-00018.parquet`\n- `data/20220301.de/train-00003-of-00018.parquet`\n- `data/20220301.de/train-00004-of-00018.parquet`\n- `data/20220301.de/train-00005-of-00018.parquet`\n- `data/20220301.de/train-00006-of-00018.parquet`\n- `data/20220301.de/train-00007-of-00018.parquet`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset scripts are no longer supported, but found wikipedia.py\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: gretelai/synthetic_text_to_sql
encoding: FLOAT32
metadata:
{"id":"gretelai/synthetic_text_to_sql","chroma:document":"# Dataset: `gretelai/synthetic_text_to_sql`\n\n## 📝 Metadata\n- **Author:** gretelai\n- **Downloads:** 2514\n- **Likes:** 601\n- **Tags:** task_categories:question-answering, task_categories:table-question-answering, task_categories:text-generation, language:en, license:apache-2.0, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2306.05685, region:us, synthetic, SQL, text-to-SQL, code\n- **License:** Not specified\n\n## 📖 Description\n```text\nImage generated by DALL-E. See prompt for more details\n\n\n\n\t\n\t\t\n\t\tsynthetic_text_to_sql\n\t\n\n\ngretelai/synthetic_text_to_sql is a rich dataset of high quality synthetic Text-to-SQL samples, \ndesigned and generated using Gretel Navigator, and released under Apache 2.0.\nPlease see our release blogpost for more details.\nThe dataset includes:\n\n  105,851 records partitioned into 100,000 train and 5,851 test records\n  ~23M total tokens, including ~12M SQL tokens\n  Coverage across 100 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic_text_to_sql....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `bmc2_llm_judge_example_1.txt`\n- `bmc2_llm_judge_example_2.txt`\n- `dalle_prompt.txt`\n- `llm_as_a_judge_rubric.txt`\n- `synthetic_text_to_sql_test.snappy.parquet`\n- `synthetic_text_to_sql_train.snappy.parquet`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `int` |\n| `domain` | `str` |\n| `domain_description` | `str` |\n| `sql_complexity` | `str` |\n| `sql_complexity_description` | `str` |\n| `sql_task_type` | `str` |\n| `sql_task_type_description` | `str` |\n| `sql_prompt` | `str` |\n| `sql_context` | `str` |\n| `sql` | `str` |\n| `sql_explanation` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `id` | `int` |\n| `domain` | `str` |\n| `domain_description` | `str` |\n| `sql_complexity` | `str` |\n| `sql_complexity_description` | `str` |\n| `sql_task_type` | `str` |\n| `sql_task_type_description` | `str` |\n| `sql_prompt` | `str` |\n| `sql_context` | `str` |\n| `sql` | `str` |\n| `sql_explanation` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: ILSVRC/imagenet-1k
encoding: FLOAT32
metadata:
{"id":"ILSVRC/imagenet-1k","chroma:document":"# Dataset: `ILSVRC/imagenet-1k`\n\n## 📝 Metadata\n- **Author:** ILSVRC\n- **Downloads:** 47651\n- **Likes:** 591\n- **Tags:** task_categories:image-classification, task_ids:multi-class-image-classification, annotations_creators:crowdsourced, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:other, size_categories:1M<n<10M, format:parquet, modality:image, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1409.0575, arxiv:1912.07726, arxiv:1811.12231, arxiv:2109.13228, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for ImageNet\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a \"synonym set\" or \"synset\". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are… See the full description on the dataset page: https://huggingface.co/datasets/ILSVRC/imagenet-1k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `classes.py`\n- `data/test-00000-of-00028.parquet`\n- `data/test-00001-of-00028.parquet`\n- `data/test-00002-of-00028.parquet`\n- `data/test-00003-of-00028.parquet`\n- `data/test-00004-of-00028.parquet`\n- `data/test-00005-of-00028.parquet`\n- `data/test-00006-of-00028.parquet`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'ILSVRC/imagenet-1k' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/ILSVRC/imagenet-1k to ask for access.\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceH4/ultrachat_200k
encoding: FLOAT32
metadata:
{"id":"HuggingFaceH4/ultrachat_200k","chroma:document":"# Dataset: `HuggingFaceH4/ultrachat_200k`\n\n## 📝 Metadata\n- **Author:** HuggingFaceH4\n- **Downloads:** 22093\n- **Likes:** 590\n- **Tags:** task_categories:text-generation, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2305.14233, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for UltraChat 200k\n\t\n\n\n\t\n\t\t\n\t\tDataset Description\n\t\n\nThis is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model.\nThe original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:\n\nSelection of a subset of data for faster supervised fine tuning.\nTruecasing of the dataset, as we observed around 5% of the data… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/test_gen-00000-of-00001-3d4cd8309148a71f.parquet`\n- `data/test_sft-00000-of-00001-f7dfac4afe5b93f4.parquet`\n- `data/train_gen-00000-of-00003-a6c9fb894be3e50b.parquet`\n- `data/train_gen-00001-of-00003-d6a0402e417f35ca.parquet`\n- `data/train_gen-00002-of-00003-c0db75b92a2f48fd.parquet`\n- `data/train_sft-00000-of-00003-a3ecf92756993583.parquet`\n- `data/train_sft-00001-of-00003-0a1804bcb6ae68c6.parquet`\n- `data/train_sft-00002-of-00003-ee46ed25cfae92c6.parquet`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train_sft`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `prompt_id` | `str` |\n| `messages` | `list` |\n#### Split: `test_sft`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `prompt_id` | `str` |\n| `messages` | `list` |\n#### Split: `train_gen`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `prompt_id` | `str` |\n| `messages` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: nvidia/Llama-Nemotron-Post-Training-Dataset
encoding: FLOAT32
metadata:
{"id":"nvidia/Llama-Nemotron-Post-Training-Dataset","chroma:document":"# Dataset: `nvidia/Llama-Nemotron-Post-Training-Dataset`\n\n## 📝 Metadata\n- **Author:** nvidia\n- **Downloads:** 3669\n- **Likes:** 587\n- **Tags:** license:cc-by-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, arxiv:2505.00949, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nLlama-Nemotron-Post-Training-Dataset-v1.1 Release\n\t\n\nUpdate [4/8/2025]: \nv1.1: We are releasing an additional 2.2M Math and 500K Code Reasoning Data in support of our release of Llama-3.1-Nemotron-Ultra-253B-v1. 🎉 \n\n\t\n\t\t\n\t\tData Overview\n\t\n\nThis dataset is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model, in support of NVIDIA’s release of… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `RL/instruction_following/instruction_following.jsonl`\n- `SFT/chat/chat.jsonl`\n- `SFT/code/code_v1.1.jsonl`\n- `SFT/code/code_v1.jsonl`\n- `SFT/math/math_v1.1.jsonl`\n- `SFT/math/math_v1.jsonl`\n- `SFT/safety/safety.jsonl`\n- `SFT/science/science.jsonl`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `SFT`\n#### Split: `code`\n| Column Name | Data Type |\n|---|---|\n| `input` | `list` |\n| `output` | `str` |\n| `category` | `str` |\n| `license` | `str` |\n| `reasoning` | `str` |\n| `generator` | `str` |\n| `used_in_training` | `str` |\n| `version` | `str` |\n| `system_prompt` | `str` |\n#### Split: `math`\n| Column Name | Data Type |\n|---|---|\n| `input` | `list` |\n| `output` | `str` |\n| `category` | `str` |\n| `license` | `str` |\n| `reasoning` | `str` |\n| `generator` | `str` |\n| `used_in_training` | `str` |\n| `version` | `str` |\n| `system_prompt` | `str` |\n#### Split: `science`\n| Column Name | Data Type |\n|---|---|\n| `input` | `list` |\n| `output` | `str` |\n| `category` | `str` |\n| `license` | `str` |\n| `reasoning` | `str` |\n| `generator` | `str` |\n| `used_in_training` | `str` |\n| `version` | `str` |\n| `system_prompt` | `str` |\n\n### Config: `RL`\n#### Split: `instruction_following`\n| Column Name | Data Type |\n|---|---|\n| `input` | `list` |\n| `args` | `dict` |\n| `category` | `str` |\n| `license` | `str` |\n| `reasoning` | `str` |\n| `used_in_training` | `str` |\n| `version` | `str` |\n| `system_prompt` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: cais/mmlu
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `cais/mmlu`\n\n## 📝 Metadata\n- **Author:** cais\n- **Downloads:** 256311\n- **Likes:** 569\n- **Tags:** task_categories:question-answering, task_ids:multiple-choice-qa, annotations_creators:no-annotation, language_creators:expert-generated, multilinguality:monolingual, source_datasets:original, language:en, license:mit, size_categories:100K<n<1M, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2009.03300, arxiv:2005.00700, arxiv:2005.14165, arxiv:2008.02275, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for MMLU\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nMeasuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).\nThis is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks… See the full description on the dataset page: https://huggingface.co/datasets/cais/mmlu....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `abstract_algebra/dev-00000-of-00001.parquet`\n- `abstract_algebra/test-00000-of-00001.parquet`\n- `abstract_algebra/validation-00000-of-00001.parquet`\n- `all/auxiliary_train-00000-of-00001.parquet`\n- `all/dev-00000-of-00001.parquet`\n- `all/test-00000-of-00001.parquet`\n- `all/validation-00000-of-00001.parquet`\n- `anatomy/dev-00000-of-00001.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `abstract_algebra`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `dev`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n\n### Config: `all`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `dev`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n\n### Config: `anatomy`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n#### Split: `dev`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `subject` | `str` |\n| `choices` | `list` |\n| `answer` | `int` |\n\n","id":"cais/mmlu"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: liwu/MNBVC
encoding: FLOAT32
metadata:
{"id":"liwu/MNBVC","chroma:document":"# Dataset: `liwu/MNBVC`\n\n## 📝 Metadata\n- **Author:** liwu\n- **Downloads:** 55042\n- **Likes:** 560\n- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:other, language_creators:other, multilinguality:monolingual, source_datasets:original, language:zh, license:mit, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nMNBVC: Massive Never-ending BT Vast Chinese corpus...\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `MNBVC.py`\n- `README.md`\n- `academic_paper/arxiv/20230241/arxivCode.0.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.1.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.10.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.11.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.12.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.13.jsonl.gz`\n- `academic_paper/arxiv/20230241/arxivCode.14.jsonl.gz`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset scripts are no longer supported, but found MNBVC.py\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: liuhaotian/LLaVA-Instruct-150K
encoding: FLOAT32
metadata:
{"id":"liuhaotian/LLaVA-Instruct-150K","chroma:document":"# Dataset: `liuhaotian/LLaVA-Instruct-150K`\n\n## 📝 Metadata\n- **Author:** liuhaotian\n- **Downloads:** 3016\n- **Likes:** 550\n- **Tags:** task_categories:visual-question-answering, task_categories:question-answering, language:en, license:cc-by-4.0, size_categories:100K<n<1M, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nLLaVA Visual Instruct 150K Dataset Card\n\t\n\n\n\t\n\t\t\n\t\tDataset details\n\t\n\nDataset type:\nLLaVA Visual Instruct 150K is a set of GPT-generated multimodal instruction-following data.\nIt is constructed for visual instruction tuning and for building large multimodal towards GPT-4 vision/language capability.\nDataset date:\nLLaVA Visual Instruct 150K was collected in April 2023, by prompting GPT-4-0314 API.\nPaper or resources for more information:\nhttps://llava-vl.github.io/\nLicense:\nCreative… See the full description on the dataset page: https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `complex_reasoning_77k.json`\n- `conversation_58k.json`\n- `detail_23k.json`\n- `llava_instruct_150k.json`\n- `llava_instruct_80k.json`\n- `llava_v1_5_mix665k.json`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `id` | `str` |\n| `image` | `str` |\n| `conversations` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: uonlp/CulturaX
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `uonlp/CulturaX`\n\n## 📝 Metadata\n- **Author:** uonlp\n- **Downloads:** 46423\n- **Likes:** 547\n- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:af, language:als, language:am, language:an, language:ar, language:arz, language:as, language:ast, language:av, language:az, language:azb, language:ba, language:bar, language:bcl, language:be, language:bg, language:bh, language:bn, language:bo, language:bpy, language:br, language:bs, language:bxr, language:ca, language:cbk, language:ce, language:ceb, language:ckb, language:cs, language:cv, language:cy, language:da, language:de, language:dsb, language:dv, language:el, language:eml, language:en, language:eo, language:es, language:et, language:eu, language:fa, language:fi, language:fr, language:frr, language:fy, language:ga, language:gd, language:gl, language:gn, language:gom, language:gu, language:he, language:hi, language:hr, language:hsb, language:ht, language:hu, language:hy, language:ia, language:id, language:ie, language:ilo, language:io, language:is, language:it, language:ja, language:jbo, language:jv, language:ka, language:kk, language:km, language:kn, language:ko, language:krc, language:ku, language:kv, language:kw, language:ky, language:la, language:lb, language:lez, language:li, language:lmo, language:lo, language:lrc, language:lt, language:lv, language:mai, language:mg, language:mhr, language:min, language:mk, language:ml, language:mn, language:mr, language:mrj, language:ms, language:mt, language:mwl, language:my, language:myv, language:mzn, language:nah, language:nap, language:nds, language:ne, language:new, language:nl, language:nn, language:no, language:oc, language:or, language:os, language:pa, language:pam, language:pl, language:pms, language:pnb, language:ps, language:pt, language:qu, language:rm, language:ro, language:ru, language:rue, language:sa, language:sah, language:scn, language:sd, language:sh, language:si, language:sk, language:sl, language:so, language:sq, language:sr, language:su, language:sv, language:sw, language:ta, language:te, language:tg, language:th, language:tk, language:tl, language:tr, language:tt, language:tyv, language:ug, language:uk, language:ur, language:uz, language:vec, language:vi, language:vls, language:vo, language:wa, language:war, language:wuu, language:xal, language:xmf, language:yi, language:yo, language:yue, language:zh, size_categories:1B<n<10B, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:2309.09400, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nCulturaX \n     Cleaned, Enormous, and Public: The Multilingual Fuel to Democratize Large Language Models for 167 Languages \n\n\n\n\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nWe present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for large language model (LLM) development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language… See the full description on the dataset page: https://huggingface.co/datasets/uonlp/CulturaX....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `CulturaX_loading_script.py`\n- `README.md`\n- `af/af_part_00000.parquet`\n- `af/checksum.sha256`\n- `als/als_part_00000.parquet`\n- `als/checksum.sha256`\n- `am/am_part_00000.parquet`\n- `am/checksum.sha256`\n- `an/an_part_00000.parquet`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'uonlp/CulturaX' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/uonlp/CulturaX to ask for access.\n```\n","id":"uonlp/CulturaX"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Salesforce/xlam-function-calling-60k
encoding: FLOAT32
metadata:
{"id":"Salesforce/xlam-function-calling-60k","chroma:document":"# Dataset: `Salesforce/xlam-function-calling-60k`\n\n## 📝 Metadata\n- **Author:** Salesforce\n- **Downloads:** 6017\n- **Likes:** 538\n- **Tags:** task_categories:question-answering, task_categories:text-generation, task_categories:reinforcement-learning, language:en, license:cc-by-4.0, size_categories:10K<n<100K, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2406.18518, region:us, function-calling, LLM Agent, code, synthetic\n- **License:** Not specified\n\n## 📖 Description\n```text\nAPIGen Function-Calling Datasets\n\t\n\nPaper | Website | Models\nThis repo contains 60,000 data collected by APIGen, an automated data generation pipeline designed to produce verifiable high-quality datasets for function-calling applications. Each data in our dataset is verified through three hierarchical stages: format checking, actual function executions, and semantic verification, ensuring its reliability and correctness. \nWe conducted human evaluation over 600 sampled data points, and… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `figures/dataset_pie_chart.png`\n- `figures/function-call-overview.png`\n- `figures/overview.jpg`\n- `xlam_function_calling_60k.json`\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'Salesforce/xlam-function-calling-60k' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k to ask for access.\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: facebook/natural_reasoning
encoding: FLOAT32
metadata:
{"id":"facebook/natural_reasoning","chroma:document":"# Dataset: `facebook/natural_reasoning`\n\n## 📝 Metadata\n- **Author:** facebook\n- **Downloads:** 3349\n- **Likes:** 538\n- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2502.13124, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nNaturalReasoning is a large-scale dataset for general reasoning tasks. It consists of high-quality challenging reasoning questions backtranslated from pretraining corpora DCLM and FineMath. The questions have been deduplicated and decontaminated from popular reasoning benchmarks including MATH, GPQA, MMLU-Pro, MMLU-STEM. For each question, we extract the reference final answer from the original document from the pretraining corpora if possible. We also provide a model-generated response from… See the full description on the dataset page: https://huggingface.co/datasets/facebook/natural_reasoning....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `NaturalReasoning.pdf`\n- `README.md`\n- `full.jsonl`\n- `scaling_plot`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `question` | `str` |\n| `reference_answer` | `str` |\n| `responses` | `list` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: poloclub/diffusiondb
encoding: FLOAT32
metadata:
{"id":"poloclub/diffusiondb","chroma:document":"# Dataset: `poloclub/diffusiondb`\n\n## 📝 Metadata\n- **Author:** poloclub\n- **Downloads:** 34306\n- **Likes:** 519\n- **Tags:** task_categories:text-to-image, task_categories:image-to-text, task_ids:image-captioning, annotations_creators:no-annotation, language_creators:found, multilinguality:multilingual, source_datasets:original, language:en, license:cc0-1.0, size_categories:n>1T, arxiv:2210.14896, region:us, stable diffusion, prompt engineering, prompts, research paper\n- **License:** Not specified\n\n## 📖 Description\n```text\nDiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2\nmillion images generated by Stable Diffusion using prompts and hyperparameters\nspecified by real users. The unprecedented scale and diversity of this\nhuman-actuated dataset provide exciting research opportunities in understanding\nthe interplay between prompts and generative models, detecting deepfakes, and\ndesigning human-AI interaction tools to help users more easily use these models....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `.gitignore`\n- `README.md`\n- `diffusiondb-large-part-1/part-000001.zip`\n- `diffusiondb-large-part-1/part-000002.zip`\n- `diffusiondb-large-part-1/part-000003.zip`\n- `diffusiondb-large-part-1/part-000004.zip`\n- `diffusiondb-large-part-1/part-000005.zip`\n- `diffusiondb-large-part-1/part-000006.zip`\n- `diffusiondb-large-part-1/part-000007.zip`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset scripts are no longer supported, but found diffusiondb.py\n```\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: JosephusCheung/GuanacoDataset
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `JosephusCheung/GuanacoDataset`\n\n## 📝 Metadata\n- **Author:** JosephusCheung\n- **Downloads:** 269\n- **Likes:** 515\n- **Tags:** task_categories:text-generation, task_categories:question-answering, language:zh, language:en, language:ja, language:de, license:gpl-3.0, size_categories:1M<n<10M, format:json, modality:text, library:datasets, library:dask, library:mlcroissant, doi:10.57967/hf/1423, region:us, alpaca, llama, guanaco\n- **License:** Not specified\n\n## 📖 Description\n```text\nSorry, it's no longer available on Hugging Face. Please reach out to those who have already downloaded it. If you have a copy, please refrain from re-uploading it to Hugging Face. The people here don't deserve it. See also: https://twitter.com/RealJosephus/status/1779913520529707387\n\n\t\n\t\t\n\t\tGuanacoDataset\n\t\n\nNews: We're heading towards multimodal VQA, with blip2-flan-t5-xxl Alignment to Guannaco 7B LLM.\nStill under construction: GuanacoVQA weight & GuanacoVQA Dataset \nNotice: Effective… See the full description on the dataset page: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `additional/general_ans-utf8.json`\n- `additional/general_ans.json`\n- `additional/general_questions-utf8.json`\n- `additional/general_questions.json`\n- `additional/paper_answers-utf8.json`\n- `additional/paper_answers.json`\n- `additional/paper_questions-utf8.json`\n- `additional/paper_questions.json`\n- ... and more.\n\n## 📊 Data Structure\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Dataset 'JosephusCheung/GuanacoDataset' is a gated dataset on the Hub. Visit the dataset page at https://huggingface.co/datasets/JosephusCheung/GuanacoDataset to ask for access.\n```\n","id":"JosephusCheung/GuanacoDataset"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Salesforce/wikitext
encoding: FLOAT32
metadata:
{"id":"Salesforce/wikitext","chroma:document":"# Dataset: `Salesforce/wikitext`\n\n## 📝 Metadata\n- **Author:** Salesforce\n- **Downloads:** 883534\n- **Likes:** 511\n- **Tags:** task_categories:text-generation, task_categories:fill-mask, task_ids:language-modeling, task_ids:masked-language-modeling, annotations_creators:no-annotation, language_creators:crowdsourced, multilinguality:monolingual, source_datasets:original, language:en, license:cc-by-sa-3.0, license:gfdl, size_categories:1M<n<10M, format:parquet, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1609.07843, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for \"wikitext\"\n\t\n\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\n The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified\n Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License.\nCompared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over\n110 times larger. The WikiText dataset also features a far larger… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `wikitext-103-raw-v1/test-00000-of-00001.parquet`\n- `wikitext-103-raw-v1/train-00000-of-00002.parquet`\n- `wikitext-103-raw-v1/train-00001-of-00002.parquet`\n- `wikitext-103-raw-v1/validation-00000-of-00001.parquet`\n- `wikitext-103-v1/test-00000-of-00001.parquet`\n- `wikitext-103-v1/train-00000-of-00002.parquet`\n- `wikitext-103-v1/train-00001-of-00002.parquet`\n- `wikitext-103-v1/validation-00000-of-00001.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `wikitext-103-raw-v1`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n\n### Config: `wikitext-103-v1`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n\n### Config: `wikitext-2-raw-v1`\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n#### Split: `validation`\n| Column Name | Data Type |\n|---|---|\n| `text` | `str` |\n\n"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceH4/no_robots
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `HuggingFaceH4/no_robots`\n\n## 📝 Metadata\n- **Author:** HuggingFaceH4\n- **Downloads:** 2713\n- **Likes:** 505\n- **Tags:** task_categories:text-generation, language:en, license:cc-by-nc-4.0, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, arxiv:2203.02155, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for No Robots 🙅‍♂️🤖\n\t\n\nLook Ma, an instruction dataset that wasn't generated by GPTs!\n\n\t\n\t\t\n\t\tDataset Summary\n\t\n\nNo Robots is a high-quality dataset of 10,000 instructions and demonstrations created by skilled human annotators. This data can be used for supervised fine-tuning (SFT) to make language models follow instructions better. No Robots was modelled after the instruction dataset described in OpenAI's InstructGPT paper, and is comprised mostly of single-turn… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/no_robots....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/test-00000-of-00001.parquet`\n- `data/test_sft-00000-of-00001.parquet`\n- `data/train-00000-of-00001.parquet`\n- `data/train_sft-00000-of-00001.parquet`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `prompt_id` | `str` |\n| `messages` | `list` |\n| `category` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `prompt` | `str` |\n| `prompt_id` | `str` |\n| `messages` | `list` |\n| `category` | `str` |\n\n","id":"HuggingFaceH4/no_robots"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: Gustavosta/Stable-Diffusion-Prompts
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `Gustavosta/Stable-Diffusion-Prompts`\n\n## 📝 Metadata\n- **Author:** Gustavosta\n- **Downloads:** 8696\n- **Likes:** 503\n- **Tags:** annotations_creators:no-annotation, language_creators:found, source_datasets:original, language:en, license:unknown, size_categories:10K<n<100K, format:parquet, modality:text, library:datasets, library:pandas, library:mlcroissant, library:polars, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nStable Diffusion Dataset\n\t\n\nThis is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: \"Lexica.art\". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.\nIf you want to test the model with a demo, you can go to: \"spaces/Gustavosta/MagicPrompt-Stable-Diffusion\".\nIf you want to see the model, go to: \"Gustavosta/MagicPrompt-Stable-Diffusion\"....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `data/eval.parquet`\n- `data/train.parquet`\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `Prompt` | `str` |\n#### Split: `test`\n| Column Name | Data Type |\n|---|---|\n| `Prompt` | `str` |\n\n","id":"Gustavosta/Stable-Diffusion-Prompts"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: cerebras/SlimPajama-627B
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `cerebras/SlimPajama-627B`\n\n## 📝 Metadata\n- **Author:** cerebras\n- **Downloads:** 44705\n- **Likes:** 502\n- **Tags:** task_categories:text-generation, language:en, arxiv:2306.01116, arxiv:2302.13971, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nThe dataset consists of 59166 jsonl files and is ~895GB compressed. It is a cleaned and deduplicated version of Together's RedPajama. \nCheck out our blog post explaining our methods, our code on GitHub, and join the discussion on the Cerebras Discord.\n\n\t\n\t\t\n\t\n\t\n\t\tGetting Started\n\t\n\nYou can download the dataset using Hugging Face datasets:\nfrom datasets import load_dataset\nds = load_dataset(\"cerebras/SlimPajama-627B\")\n\n\n\t\n\t\n\t\n\t\tBackground\n\t\n\nToday we are releasing SlimPajama – the largest… See the full description on the dataset page: https://huggingface.co/datasets/cerebras/SlimPajama-627B....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `test/chunk1/example_holdout_0.jsonl.zst`\n- `test/chunk1/example_holdout_1.jsonl.zst`\n- `test/chunk1/example_holdout_10.jsonl.zst`\n- `test/chunk1/example_holdout_100.jsonl.zst`\n- `test/chunk1/example_holdout_1000.jsonl.zst`\n- `test/chunk1/example_holdout_1001.jsonl.zst`\n- `test/chunk1/example_holdout_1002.jsonl.zst`\n- `test/chunk1/example_holdout_1003.jsonl.zst`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `default`\n#### Split: `train`\n**Anomaly/Error during data loading:**\n```\nCould not inspect dataset structure.\nDetails: Compression type zstd not supported\n```\n","id":"cerebras/SlimPajama-627B"}


created_at: 2025-10-27 07:57:48
topic: persistent://default/default/0cc11db2-9aa1-48d4-ab09-c25c03dc2063
id: HuggingFaceM4/the_cauldron
encoding: FLOAT32
metadata:
{"chroma:document":"# Dataset: `HuggingFaceM4/the_cauldron`\n\n## 📝 Metadata\n- **Author:** HuggingFaceM4\n- **Downloads:** 58735\n- **Likes:** 502\n- **Tags:** size_categories:1M<n<10M, format:parquet, modality:image, modality:text, library:datasets, library:dask, library:mlcroissant, library:polars, arxiv:1603.07396, arxiv:2206.01718, arxiv:2208.05358, arxiv:1612.06890, arxiv:2310.00367, arxiv:1710.07300, arxiv:2312.12241, arxiv:1912.03098, arxiv:2211.08545, arxiv:2306.05425, arxiv:1709.00103, arxiv:2003.12462, arxiv:1612.00837, arxiv:2205.00363, arxiv:2403.09029, arxiv:2405.02246, region:us\n- **License:** Not specified\n\n## 📖 Description\n```text\nDataset Card for The Cauldron\n\t\n\n\n\n\t\n\t\t\n\t\tDataset description\n\t\n\nThe Cauldron is part of the Idefics2 release.\nIt is a massive collection of 50 vision-language datasets (training sets only) that were used for the fine-tuning of the vision-language model Idefics2.\n\n\t\n\t\t\n\t\tLoad the dataset\n\t\n\nTo load the dataset, install the library datasets with pip install datasets. Then,\nfrom datasets import load_dataset\nds = load_dataset(\"HuggingFaceM4/the_cauldron\", \"ai2d\")\n\nto download and load the… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceM4/the_cauldron....\n```\n\n## 📂 File System Sample\n- `.gitattributes`\n- `README.md`\n- `ai2d/train-00000-of-00001-2ce340398c113b79.parquet`\n- `aokvqa/train-00000-of-00002-88e828b9a932c295.parquet`\n- `aokvqa/train-00001-of-00002-ceb27cfe85e08680.parquet`\n- `chart2text/train-00000-of-00003-3a2ec464eb1cfc9b.parquet`\n- `chart2text/train-00001-of-00003-a65d11892445678c.parquet`\n- `chart2text/train-00002-of-00003-8626ac7f2c225705.parquet`\n- `chartqa/train-00000-of-00002-7733d4ca73ccd12e.parquet`\n- `chartqa/train-00001-of-00002-03251c406186eabb.parquet`\n- ... and more.\n\n## 📊 Data Structure\n### Config: `ai2d`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `images` | `list` |\n| `texts` | `list` |\n\n### Config: `aokvqa`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `images` | `list` |\n| `texts` | `list` |\n\n### Config: `chart2text`\n#### Split: `train`\n| Column Name | Data Type |\n|---|---|\n| `images` | `list` |\n| `texts` | `list` |\n\n","id":"HuggingFaceM4/the_cauldron"}


