{"data":{"slug":"dbiir-uer-py","name":"UER-py","tagline":"Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo","github_url":"https://github.com/dbiir/UER-py","owner":"dbiir","repo":"UER-py","owner_avatar_url":"https://avatars.githubusercontent.com/u/13671736?v=4","primary_language":"Python","stars":3112,"forks":520,"topics":["albert","bart","bert","chinese","classification","clue","elmo","fine-tuning","gpt","gpt-2","model-zoo","natural-language-processing","ner","pegasus","pre-training","pytorch","roberta","t5","unilm","xlm-roberta"],"archived":false,"github_pushed_at":"2024-05-09T11:12:55+00:00","maintenance_label":"Dormant","stars_delta_30d":2,"url":"https://www.graphcanon.com/tools/dbiir-uer-py","markdown_url":"https://www.graphcanon.com/tools/dbiir-uer-py.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/dbiir-uer-py","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=dbiir-uer-py","description":"Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo","homepage_url":"https://github.com/dbiir/UER-py/wiki","license":"Apache-2.0","open_issues":136,"watchers":72,"ai_summary":"UER-py is a comprehensive framework for pre-training models using PyTorch. It includes a variety of pre-trained models and supports tasks like classification, NER, and fine-tuning.","readme_excerpt":"## Requirements\n* Python >= 3.6\n* torch >= 1.1\n* six >= 1.12.0\n* argparse\n* packaging\n* regex\n* For the pre-trained model conversion (related with TensorFlow) you will need TensorFlow\n* For the tokenization with sentencepiece model you will need [SentencePiece](https://github.com/google/sentencepiece)\n* For developing a stacking model you will need LightGBM and [BayesianOptimization](https://github.com/fmfn/BayesianOptimization)\n* For the pre-training with whole word masking you will need word segmentation tool such as [jieba](https://github.com/fxsjy/jieba)\n* For the use of CRF in sequence labeling downstream task you will need [pytorch-crf](https://github.com/kmkurn/pytorch-crf)\n\n\n<br/>","github_created_at":"2019-04-10T12:00:20+00:00","created_at":"2026-07-11T11:37:47.024017+00:00","updated_at":"2026-08-23T18:01:23.567489+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"albert","name":"albert"},{"slug":"bart","name":"bart"},{"slug":"bert","name":"bert"},{"slug":"chinese","name":"chinese"},{"slug":"classification","name":"classification"},{"slug":"clue","name":"clue"},{"slug":"elmo","name":"elmo"},{"slug":"fine-tuning","name":"fine-tuning"}],"trust":{"provenance":{"is_fork":false,"github_id":180572200,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-23T18:01:22.762Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":836,"last_release_at":null,"stars_delta_30d":2,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:37:48.319Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-23T18:01:23.202Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-08-23T18:01:23.202Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-23T18:01:23.202Z"}},"decision_facts":{"hosting":null,"pricing":{"model":"freemium","summary":"The framework itself is free and open-source under Apache 2.0 license providing flexibility for modification with no costs."},"requirements":{"notes":["- Requires Python environment setup","- Needs PyTorch installation"],"min_ram_gb":8,"requires_docker":false},"constraints":{"min_ram_gb":8,"pricing_model":"freemium","requires_docker":false},"when_to_use":["- When you need to work exclusively within the PyTorch ecosystem, UER-py provides extensive support for various pre-trained models and tasks without the necessity of switching frameworks.","- If your project requires access to a wide range of pre-trained models like BERT, RoBERTa, ALBERT, T5, GPT-2, or PEGASUS available in their model zoo."],"when_not_to_use":["- When you require more framework flexibility and are open to using TensorFlow or other deep learning libraries outside PyTorch.","- If your project is sensitive to maintenance updates but the UER-py repository has not seen recent active contribution, preferring a tool actively maintained might be better."],"source":"enrich:decision_facts","observed_at":"2026-07-12T15:25:41.060Z"},"constraint_facets":{"min_ram_gb":8,"pricing_model":"freemium","requires_docker":false},"decision_summary":[{"label":"Pricing","value":"freemium - The framework itself is free and open-source under Apache 2.0 license providing flexibility for modification with no costs."},{"label":"Requirements","value":"Min 8 GB RAM; - Requires Python environment setup; - Needs PyTorch installation"},{"label":"Adopt for","value":"UER-py, an open-source PyTorch framework with a diverse model zoo for training and fine-tuning language models."}]}}