text2dataset

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

Fig: Japanese translation of the Abirate/english_quotes dataset using the llm-jp/llm-jp-3-3.7b-instruct model.

Overview

text2dataset is 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). You can also use text2dataset to paraphrase texts by changing the prompt template. This tool is inspired by 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.
• Custom Prompt Template:
  • By specifying the prompt_template_path parameter, you can customize the prompt template for any translation task (e.g., paraphrasing, summarization etc.).

Installation

$ git clone https://github.com/llm-jp/text2dataset.git
$ cd text2dataset
$ rye sync

Usage

Translation

$ 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 model on an A100 GPU, 2508 English quotes were translated into Japanese in just 21 seconds.

Fig: Japanese translation of the Abirate/english_quotes dataset using the llm-jp/llm-jp-3-3.7b-instruct model.

The result dataset is available at speed/english_quotes_ja.

Paraphrasing

You can also use text2dataset to paraphrase texts by changing the prompt template with specifying the prompt_template_path parameter.

$ 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

Fig: Paraphrase of the Abirate/english_quotes dataset using the google/gemma-2-2b-it/ model.

The result dataset is available at speed/english_quotes_paraphrase.

Translation of neuralwork/arxiver dataset

You can directly translate datasets in Hugging Face by specifying the path name in input_path.

In this example, the abstract column of the neuralwork/arxiver dataset is translated by specifying the input_path as neuralwork/arxiver and the source_column parameter as abstract.

$ python src/text2dataset/main.py \
    --model_id google/gemma-2-2b-it \
    --batch_size 16384 \
    --input_path neuralwork/arxiver \
    --source_column abstract \
    --target_column abstract_ja \
    --push_to_hub True \
    --push_to_hub_path speed/arxiver_ja \
    --output_dir data/arxiver_ja \
    --output_format json \
    --use_wandb True \
    --wandb_run_name arxiver

neuralwork/arxiver dataset contains 138k rows of abstracts, and it took 2.5 hours to translate them into Japanese using the google/gemma-2-2b-it model on a A100 GPU. The result dataset is available at speed/arxiver_ja.

Fig: Translation of the neuralwork/arxiver dataset using the google/gemma-2-2b-it/ model.

Fig: Wandb logs for the translation of the neuralwork/arxiver dataset using the google/gemma-2-2b-it/ model.

Tips

• Translation on Multiple GPUs in Parallel

To run translations on multiple GPUs concurrently, split the input dataset into several shards (directories) and execute the translation for each shard in parallel. Remember to set the gpu_id parameter to the corresponding GPU ID for each shard.

Areas for Improvement

Data Parallel Inference

Currently, we need to manually split the input dataset into shards and run the translation for each shard in parallel to utilize multiple GPUs. It would be great to have a built-in feature to automatically split the input dataset into shards and run the translation on multiple GPUs in parallel. If you have any ideas or suggestions, please feel free to open an issue or Pull Request.

Note

When using this tool, please pay attention to the license of both the dataset being translated and the LLM you use.

Development

Contribution

Welcome to any contributions! If you have any questions or suggestions, please feel free to open an issue or Pull Request.

PyPI Release

git tag -a v0.x.x -m "version 0.x.x"
git push origin --tags

Lint and Format

$ rye lint
$ rye format

References

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