This repository provides a lightweight PyTorch framework for various Natural Language Processing (NLP) tasks, leveraging BERT and similar models. It targets researchers and developers needing a flexible tool for sequence-to-sequence generation (e.g., poetry, summarization), text classification, and sequence labeling (e.g., NER, POS tagging), with support for multiple pre-trained models like BERT, RoBERTa, GPT2, T5, and BART.

How It Works

The framework utilizes a unified approach where different NLP tasks are handled by configuring model architecture and task-specific heads on top of pre-trained encoder models. It supports various pre-trained models by loading their parameters, allowing users to switch between them by setting model_name. Task selection is managed via the model_class parameter, enabling tasks like seq2seq, cls_classifier, sequence_labeling, and sequence_labeling_crf. This modular design simplifies experimentation with different models and tasks.

Quick Start & Requirements

• Install via pip: pip install bert-seq2seq tqdm
• Requires PyTorch.
• Pre-trained model weights need to be downloaded separately from provided links (e.g., Hugging Face, Baidu Pan).
• Official examples demonstrate usage for specific tasks.

Highlighted Details

• Supports a wide range of NLP tasks including poetry generation, couplet generation, automatic summarization, text classification, sentiment analysis, NER, POS tagging, and relation extraction.
• Integrates with popular pre-trained models like BERT, RoBERTa, GPT2, T5, BART, and Huawei's Nezha.
• Offers specific implementations like sequence labeling with CRF loss for improved performance.
• Includes examples for SimBERT for sentence similarity tasks.

Maintenance & Community

• Active development with frequent updates noted in the changelog (last update mentioned: Nov 12, 2021).
• QQ group available for community discussion and support (975907202).
• Links to personal blog for detailed explanations of tasks and code.

Licensing & Compatibility

• The README does not explicitly state a license. Code snippets reference Hugging Face Transformers and bert4keras, which have permissive licenses. However, the absence of a clear license file requires caution for commercial use.

Limitations & Caveats

• The project's last update was in late 2021, indicating potential lack of maintenance for newer models or techniques.
• Pre-trained model weights must be manually downloaded and configured, adding an extra setup step.
• Some specific features like rhyme enforcement in poetry generation were noted as temporarily unsupported in past updates.