NLP algorithms using transformers, supporting diverse tasks
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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
pip install transformers
(and other dependencies as needed per task).Highlighted Details
Maintenance & Community
Licensing & Compatibility
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.
1 year ago
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