dbiir/UER-py

Health v2 · maintenance only

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3112 stars · 520 forks observed · 2026-08-28

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

UER-py is a PyTorch framework for pre-training transformer language models (BERT, GPT-2, T5, ELMo, etc.) and fine-tuning them on downstream NLP tasks, with a modular encoder/decoder architecture. It also ships a model zoo of pre-trained checkpoints, particularly strong for Chinese NLP.

Use cases

• pre-train a BERT model from scratch on my own corpus
• fine-tune a pre-trained transformer for text classification
• download Chinese pre-trained language models
• train a GPT-2 model in PyTorch
• run named entity recognition with a fine-tuned BERT
• experiment with modular transformer architectures for NLP research

When to choose

• you need to pre-train or fine-tune medium-sized (<1B parameter) text models in PyTorch
• you work on Chinese NLP tasks and want a strong model zoo
• you want modular, research-friendly control over transformer components

When to avoid

• you need multi-modal or very large model training - use its successor TencentPretrain instead
• you just want inference with an off-the-shelf model via Hugging Face Transformers
• you need production serving infrastructure rather than training tooling

Facets

framework · maturity maintenance

llm-training machine-learning nlp deep-learning machine-learning deep-learning large-language-models python cross-platform pytorch bert pre-training fine-tuning model-zoo chinese-nlp gpt-2 t5 transformers natural-language-processing gpu

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