---
license: apache-2.0
dataset_info:
  features:
  - name: data
    dtype: string
  splits:
  - name: train
    num_bytes: 4864562
    num_examples: 98472
  download_size: 3180212
  dataset_size: 4864562
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-generation
- text2text-generation
language:
- en
pretty_name: Simple Daily Conversations Cleaned
size_categories:
- 10K<n<100K
---
# Dataset Card

<!-- Provide a quick summary of the dataset. -->

This dataset contains a cleaned version of simple daily conversations. It comprises nearly 98K text snippets representing informal, everyday dialogue, curated and processed for various Natural Language Processing tasks.

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset is ideal for:
- Training language models on informal, everyday conversational data.
- Research exploring linguistic patterns in casual conversation.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

- The dataset may not be suited for tasks requiring structured or contextually rich conversations (e.g., multi-turn dialogues with defined context).
- It is not intended for applications involving sensitive personal data since it represents generic conversational snippets.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset is stored in CSV format with a single column:
- **data:** Each record is a string representing one conversation snippet.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was created to support research and development in the field of natural language processing, particularly in areas where understanding everyday conversation is essential. 
It addresses the need for a clean, standardized corpus of informal text that reflects typical daily interactions.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

The raw data was collected from various sources containing informal conversation text. 
After aggregation, a thorough cleaning process was applied to remove noise, correct formatting, and eliminate any non-relevant content.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

- **Collection:** The initial texts were gathered from publicly available sources of conversational data.
- **Processing:** The data underwent preprocessing steps including lowercasing, punctuation normalization, and removal of extraneous symbols to ensure consistency across all entries.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

The dataset does not contain any personal, sensitive, or private information. 
All texts are generic in nature, ensuring privacy and confidentiality.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

The dataset reflects the informal nature of daily conversations and may carry inherent linguistic biases or idiosyncrasies common in casual speech. Users should be aware that:
- Informal language and slang may not be representative of formal writing.
- The dataset might include regional or cultural biases present in the source material.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users are encouraged to:
- Validate models trained on this dataset with additional data sources.
- Be mindful of the informal tone and potential biases when deploying applications in sensitive contexts.


## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```bibtex
@misc {aarohan_verma_2025,
	author       = { {Aarohan Verma} },
	title        = { simple-daily-conversations-cleaned },
	year         = 2025,
	url          = { https://huggingface.co/datasets/aarohanverma/simple-daily-conversations-cleaned },
	doi          = { 10.57967/hf/4880 },
	publisher    = { Hugging Face }
}
```

## Model Card Contact

For inquiries or further information, please contact:

LinkedIn: https://www.linkedin.com/in/aarohanverma/

Email: verma.aarohan@gmail.com