{"adoption": {"forks": 520, "observed_at": "2026-08-28T04:07:43.987051+00:00", "stars": 3112}, "canonical_url": "https://ross.abutalabs.com/products/uer-py", "card": {"archived": false, "artifact_type": "framework", "description": "Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo", "domain": ["machine-learning", "deep-learning", "large-language-models"], "enriched": true, "function": ["llm-training", "machine-learning", "nlp", "deep-learning"], "health_score": 20, "homepage": "https://github.com/dbiir/UER-py/wiki", "language": "Python", "license": "Apache-2.0", "license_family": "permissive", "maturity": "maintenance", "member_repos": ["dbiir/UER-py"], "name": "dbiir/UER-py", "platform": ["python", "cross-platform"], "pushed_at": "2024-05-09T11:12:55+00:00", "repo": "dbiir/UER-py", "stars": 3112, "tags": ["pytorch", "bert", "pre-training", "fine-tuning", "model-zoo", "chinese-nlp", "gpt-2", "t5", "transformers", "natural-language-processing", "gpu"], "topics": ["bert", "pre-training", "fine-tuning", "gpt", "chinese", "natural-language-processing", "pytorch", "elmo", "classification", "ner", "t5", "unilm", "roberta", "albert", "clue", "gpt-2", "model-zoo", "bart", "pegasus", "xlm-roberta"], "urls": [], "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"], "what_it_is": "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.", "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"], "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"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/uer-py", "repo": "dbiir/UER-py", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:07:43.987051+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T07:26:42.065336+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "edd54b530a9183547a28fe76299695b10a885d9a8e288093afd4f36384b40b91", "fetched_at": "2026-08-28T04:07:43.987051+00:00", "kind": "readme", "missing": false, "url": "https://github.com/dbiir/UER-py"}, {"content_hash": "19d3ba8540cac8bc417b07bead172ea314684b0b2a37b2ca14d502fc4e41e1f4", "fetched_at": "2026-08-29T09:41:51.122010+00:00", "kind": "homepage", "missing": false, "url": "https://github.com/dbiir/UER-py/wiki"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+00:00", "flags": ["no_releases"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2702, "days_push": 846, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}