pytorch / fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Summary
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| Main Code: 65,518 LOC (453 files) = PY (96%) + CU (1%) + CPP (<1%) + YAML (<1%) + PYX (<1%) + CUH (<1%) + LUA (<1%) + H (<1%) + CFG (<1%) + TOML (<1%) Secondary code: Test: 9,718 LOC (76); Generated: 0 LOC (0); Build & Deploy: 34 LOC (2); Other: 58,096 LOC (636); |
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| Duplication: 18% | |||
| File Size: 8% long (>1000 LOC), 34% short (<= 200 LOC) | |||
| Unit Size: 8% long (>100 LOC), 48% short (<= 10 LOC) | |||
| Conditional Complexity: 3% complex (McCabe index > 50), 63% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (19 components) | ||
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4 years, 4 months old
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11% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
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Features of interest:
TODOs
39 files |
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Latest commit date: 2022-01-21
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25
commits
(30 days)
12
contributors
(30 days) |
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