920232796/bert_seq2seq

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1308 stars · 207 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A lightweight PyTorch framework for fine-tuning pretrained language models (BERT, RoBERTa, Nezha, GPT2, T5, BART) on Chinese NLP tasks using the UniLM seq2seq approach. It supports sequence-to-sequence generation, text classification, sequence labeling with optional CRF, relation extraction, and similar-sentence generation.

Use cases

• generate automatic summaries or titles from Chinese text
• fine-tune BERT for named entity recognition with CRF
• train a seq2seq model to write poems or couplets
• do sentiment analysis or text classification with BERT
• continue writing articles with GPT2
• extract relation triples from text
• generate similar sentences with SimBERT

When to choose

• you need a simple, lightweight PyTorch setup for Chinese NLP fine-tuning
• you want seq2seq tasks like summarization or title generation with BERT-style models
• you want quick examples for NER, classification, and relation extraction in one framework

When to avoid

• you need production-grade distributed training or the latest model architectures
• you primarily work with English-only NLP pipelines
• you prefer using Hugging Face Transformers directly with its broader ecosystem

Facets

framework · maturity maintenance

machine-learning nlp llm-training deep-learning machine-learning deep-learning python bert seq2seq unilm t5 gpt2 ner text-classification pytorch chinese-nlp text-summarization natural-language-processing

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