seq2seq-lm-trainer

This is a simple example of seq2seq lm training, leveraging Hugging Face's Trainer for efficient model training.
The repository includes a configurable interface for dataset processing and evaluation metrics, allowing for seamless adaptation to various tasks and datasets.

Features

• Utilize the seq2seq lm model
• Easy configuration for custom dataset processing and evaluation metrics
• Integration with Hugging Face's Trainer for efficient training and evaluation

Usage

1. Dataset processing: Modify data_processing.py to accommodate your own dataset. The script should take care of loading, preprocessing, and tokenizing the data as required by the T5 model.

2. Evaluation metric: Customize the evaluation metric by modifying eval_metric.py. This script should implement the necessary logic to compute the desired evaluation metric for your task (e.g., BLEU score, ROUGE score, etc.).

3. Training and evaluation: Execute main.py to start the training and evaluation process. This script will use the custom dataset processing and evaluation metric functions specified in the previous steps, along with the Hugging Face Trainer, to efficiently train and evaluate the T5 model on your task.

Requirements

• Python 3.6 or later
• Hugging Face Transformers library
• PyTorch
• tqdm

To install the required packages, run:

pip install -r requirements.txt

Example

An example dataset and evaluation metric (e.g., machine translation with BLEU score) can be provided in the repository to demonstrate the usage and modification of the data processing and evaluation metric scripts.

License

This project is licensed under the MIT License.