transformers_tasks  by HarderThenHarder

NLP algorithms using transformers, supporting diverse tasks

created 2 years ago
2,363 stars

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Project Summary

This repository provides a collection of Natural Language Processing (NLP) tasks implemented using the Hugging Face transformers library. It aims to offer readily adaptable code for researchers and developers to fine-tune pre-trained models on their own datasets for tasks like text classification, information extraction, text matching, and more.

How It Works

The project leverages the Hugging Face transformers library, a popular framework for easily loading, training, and fine-tuning transformer models. It organizes implementations for various NLP tasks, including text matching (e.g., Sentence-BERT, SimCSE), information extraction (e.g., UIE), prompt-based learning (PET, p-tuning), text classification (BERT-CLS), RLHF, and text generation. Users can replace the default training datasets with their own to train custom models.

Quick Start & Requirements

  • Install: pip install transformers (and other dependencies as needed per task).
  • Prerequisites: Python 3.6+. Specific tasks may require additional libraries or models from Hugging Face Hub.
  • Resources: Requires standard Python environment; GPU recommended for training.
  • Docs: huggingface transformers installation

Highlighted Details

  • Supports a wide range of NLP tasks, from traditional classification and extraction to advanced RLHF and LLM applications.
  • Includes implementations for both supervised and unsupervised learning methods (e.g., SimCSE for unsupervised text matching).
  • Features examples for fine-tuning large language models (LLMs) like ChatGLM-6B.
  • Offers tools like a Tokenizer Viewer for debugging.

Maintenance & Community

  • Project status is marked as "WIP" (Work In Progress).
  • Author: Pankeyu.
  • Stars: 1.3k.

Licensing & Compatibility

  • The README does not explicitly state a license. It is based on the Hugging Face transformers library, which is typically Apache 2.0 licensed.

Limitations & Caveats

The project is marked as "WIP," indicating ongoing development and potential for changes or incomplete features. The specific licensing for this repository is not clearly stated in the README.

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Last commit

1 year ago

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