Metadata-Version: 2.3
Name: text2dataset
Version: 0.2.0
Summary: Easily turn large English text datasets into Japanese text datasets using open LLMs
Project-URL: Repository, https://github.com/llm-jp/text2dataset
Author-email: speed1313 <speedtry13@icloud.com>
License: MIT License
        
        Copyright (c) 2024 speed
        
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License-File: LICENSE
Requires-Python: >=3.12.1
Requires-Dist: click>=8.1.7
Requires-Dist: datasets>=3.0.0
Requires-Dist: transformers==4.45.2
Requires-Dist: vllm>=0.6.1
Requires-Dist: wandb>=0.18.0
Description-Content-Type: text/markdown

# text2dataset
[![pypi](https://img.shields.io/pypi/v/text2dataset.svg)](https://pypi.python.org/pypi/text2dataset)

Easily turn large English text datasets into Japanese text datasets using open LLMs.

A tool for converting a datasets.Dataset by translating the data in the "txt" column using Open LLM like gemma2 with vLLM, and adding a new "txt_ja" column (translated text in Japanese).
This tool is inspired by [img2dataset](https://github.com/rom1504/img2dataset).

## Features
- Save the intermediate results in shards:
  - By setting the `number_sample_per_shard` parameter, the dataset can be saved in shards as specified by the number of samples per shard.
- Resume from checkpoint:
  - By setting the `resume_from_checkpoint` parameter, the translation can be resumed from where it left off.
- Logging with wandb:
  - By setting the `use_wandb` parameter, the metrics such as examples_per_sec and count can be logged to wandb.
- Push to Hugging Face Hub:
  - By setting the `push_to_hub` parameter, the translated dataset can be pushed to the Hugging Face Hub.

## Installation
```bash
$ git clone https://github.com/llm-jp/text2dataset.git
$ rye sync
```

## Usage

### Translation
```bash
$ python src/text2dataset/main.py \
    --model_id llm-jp/llm-jp-3-3.7b-instruct \
    --batch_size 16384 \
    --input_path data/english_quotes.json \
    --source_column text \
    --target_column text_ja \
    --push_to_hub True \
    --push_to_hub_path speed/english_quotes_ja \
    --output_dir data/english_quotes_ja \
    --output_format json
```

Using the [`llm-jp/llm-jp-3-3.7b-instruct`](https://huggingface.co/llm-jp/llm-jp-3-3.7b-instruct) model on an A100 GPU, 2508 English quotes were translated into Japanese in just 21 seconds.
The result dataset is available at [speed/english_quotes_ja](https://huggingface.co/datasets/speed/english_quotes).

![english_quotes](images/english_quotes_ja.png)


### Paraphrasing
You can also use text2dataset to paraphrase texts by changing the prompt template with specifying the `prompt_template_path` parameter.
```bash
$ python src/text2dataset/main.py \
    --model_id google/gemma-2-2b-it \
    --batch_size 16384 \
    --input_path data/english_quotes.json \
    --source_column text \
    --target_column text_paraphrase \
    --push_to_hub True \
    --push_to_hub_path speed/english_quotes_paraphrase \
    --output_dir data/english_quotes_paraphrase \
    --output_format json \
    --prompt_template_path config/paraphrase.yaml
```
The result dataset is available at [speed/english_quotes_paraphrase](https://huggingface.co/datasets/speed/english_quotes_paraphrase).

![english_quotes](images/english_quotes_paraphrase.png)

## Areas for Improvement
- Data Paarallel Inference:
  - Currently, only one model is used for inference. This can be improved by using DataParallel. If you know how to do this with vLLM, please let me know or Pull Request.

# Development

## PyPI Release
```bash
git tag -a v0.x.x -m "version 0.x.x"
git push origin --tags
```


## References
- https://github.com/vllm-project/vllm
- https://github.com/rom1504/img2dataset
- https://huggingface.co/datasets/Abirate/english_quotes
