transformers evalstate · COMPLETED
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
github.com/evalstate/transformers · ★ 2 · Forks 0 · Size 397.5 MB
SUMMARY
Technologies 22
Scored 22
Observed 22
Practices 6
Evidence 23
Skips 0
COVERAGE
Analyzed 5816 files · 25539 commits · 0 API calls
TECHNOLOGIES & DEPTH
Dockerfile LANGUAGE Depth 70
19 files · PRODUCTION
Shell LANGUAGE Depth 70
7 files · PRODUCTION
JSON LANGUAGE Depth 70
31 files · PRODUCTION
TOML LANGUAGE Depth 70
2 files · PRODUCTION
Markdown LANGUAGE Depth 70
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YAML LANGUAGE Depth 70
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SQL LANGUAGE Depth 70
1 files · PRODUCTION
Python LANGUAGE Depth 70
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pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
Poetry BUILD_TOOL Depth 80
1 files · CONFIGURATION
Elasticsearch DATABASE Depth 58
1 files · CONFIGURATION
FastAPI FRAMEWORK Depth 78
7 files · PRODUCTION, TEST
Jinja2 LIBRARY Depth 66
2 files · PRODUCTION, TEST
Laravel Pint QUALITY Depth 58
1 files · PRODUCTION
NumPy LIBRARY Depth 85
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PostgreSQL DATABASE Depth 62
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Pydantic LIBRARY Depth 58
1 files · PRODUCTION
Requests LIBRARY Depth 85
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TypeScript LANGUAGE Depth 81
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Uvicorn LIBRARY Depth 58
1 files · PRODUCTION
pandas LIBRARY Depth 85
9 files · PRODUCTION, TEST
pytest TESTING Depth 78
217 files · PRODUCTION, TEST
PRACTICES
documentation · observedautomated_tests · observedcontinuous_integration · observedcontainerization · observedlinting · observedformatting · absent
ACTIVITY & OWNERSHIP
First commit 2018-10-29
Last commit 2026-04-29
Active months 91
Commits 25539