---
language:
- fa
license: cc-by-sa-4.0
library_name: spacy
pipeline_tag: token-classification
tags:
- spacy
- token-classification
- dependency-parsing
- persian
- farsi
---

# fa_dep_news_lg

Persian dependency pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, trainable_lemmatizer, parser. No NER, see fa_core_news_lg.

## Install

```bash
pip install https://huggingface.co/Phazel/fa_dep_news_lg/resolve/main/fa_dep_news_lg-3.8.0-py3-none-any.whl
```

## Use

```python
import spacy

nlp = spacy.load("fa_dep_news_lg")
doc = nlp("شرکت ایران خودرو اعلام کرد که تولید خود را افزایش می‌دهد.")

print([(t.text, t.pos_, t.tag_, t.lemma_, t.dep_) for t in doc])
```

## Accuracy

Scored with `spacy benchmark accuracy` on the held-out PerDT test split.

| Metric | Score |
| --- | ---: |
| Tokenization accuracy | 99.96 |
| XPOS tag accuracy | 96.55 |
| UPOS tag accuracy | 96.68 |
| Morphological features | 96.70 |
| Lemma accuracy | 98.08 |
| Unlabelled attachment (UAS) | 90.96 |
| Labelled attachment (LAS) | 86.60 |
| Sentence segmentation F | 99.18 |

## Other packages in this family

| Pipeline | Tier | LAS | ENTS_F | Wheel |
| --- | --- | ---: | ---: | ---: |
| [`fa_dep_news_sm`](https://huggingface.co/Phazel/fa_dep_news_sm) | `sm` | 85.15 | - | 7.9 MB |
| [`fa_core_news_sm`](https://huggingface.co/Phazel/fa_core_news_sm) | `sm` | 85.15 | 71.87 | 13.5 MB |
| [`fa_ent_news_sm`](https://huggingface.co/Phazel/fa_ent_news_sm) | `sm` | - | 71.87 | 5.9 MB |
| [`fa_dep_news_md`](https://huggingface.co/Phazel/fa_dep_news_md) | `md` | 86.34 | - | 62.6 MB |
| [`fa_core_news_md`](https://huggingface.co/Phazel/fa_core_news_md) | `md` | 86.34 | 74.71 | 68.5 MB |
| [`fa_ent_news_md`](https://huggingface.co/Phazel/fa_ent_news_md) | `md` | - | 74.71 | 60.6 MB |
| `fa_dep_news_lg` (this one) | `lg` | 86.60 | - | 229.3 MB |
| [`fa_core_news_lg`](https://huggingface.co/Phazel/fa_core_news_lg) | `lg` | 86.60 | 75.94 | 235.2 MB |
| [`fa_ent_news_lg`](https://huggingface.co/Phazel/fa_ent_news_lg) | `lg` | - | 75.94 | 227.3 MB |
| [`fa_core_news_trf`](https://huggingface.co/Phazel/fa_core_news_trf) | `trf` | 90.79 | 82.89 | 608.2 MB |

Tiers: `sm` hash embeddings, no vectors, `md` 50k x 300d floret vectors, `lg` 200k x 300d floret vectors, `trf` fine-tuned transformer, GPU recommended.

Standalone vector tables, usable as `--paths.vectors` for your own training:

| Vectors | Rows | Used by | Wheel |
| --- | ---: | --- | ---: |
| [`fa_floret_400k`](https://huggingface.co/Phazel/fa_floret_400k) | 50,000 | `md` tier | 54.5 MB |
| [`fa_floret_full_wiki`](https://huggingface.co/Phazel/fa_floret_full_wiki) | 50,000 | no shipped pipeline | 54.9 MB |
| [`fa_floret_wiki_200k`](https://huggingface.co/Phazel/fa-floret-wiki-vectors) | 200,000 | `lg` tier | 221.3 MB |

Training scripts, configs and evaluation: <https://github.com/Fazel94/spacy-persian>.

## Sources

| Source | Author | Licence |
| --- | --- | --- |
| [UD_Persian-PerDT (PerUDT v1.0)](https://github.com/UniversalDependencies/UD_Persian-PerDT) | Mohammad Sadegh Rasooli, Pegah Safari, Amirsaeid Moloodi, Alireza Nourian | CC BY-SA 4.0 |
| [spaCy lang/fa language data (stop words originally from HAZM)](https://github.com/explosion/spaCy/tree/master/spacy/lang/fa) | Explosion and spaCy contributors | MIT |
| [fa_floret static vectors (lg tier: 200k rows x 300d floret table trained on the full Persian Wikipedia dump, 5 epochs, with floret-torch)](https://huggingface.co/Phazel/fa-floret-wiki-vectors) | Kiyarash Fazeli | CC BY-SA 4.0 |

## Notes

Trained on UD_Persian-PerDT, licensed CC BY-SA 4.0; this pipeline is therefore distributed under CC BY-SA 4.0 with attribution to the treebank authors. Multiword tokens (pronominal clitics, enclitic copulas) were merged with `spacy convert --merge-subtokens`, so a small number of XPOS tags are composite (e.g. N_IANM_PR_JOPER) and ~1.5% of lemmas contain a space. doc.noun_chunks under-fires on this pipeline: spacy/lang/fa/syntax_iterators.py matches ClearNLP labels that do not exist in Universal Dependencies, see docs/upstream/fa-noun-chunks.md. This is the `lg` tier: identical architecture to `sm`/`md` but a larger static floret vector table (200,000 rows x 300 dimensions, minn=maxn=5, hash_count=2) trained on the full Persian Wikipedia dump for 5 epochs with floret-torch. Same zero-OOV rationale as `md` (see docs/MODELS.md): floret hashes subwords into a fixed table, so `token.has_vector` is always True despite Persian's ZWNJ (U+200C) inconsistency.