microsoft / AzureML-BERT
End-to-End recipes for pre-training and fine-tuning BERT using Azure Machine Learning Service
Summary
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| Main Code: 4,367 LOC (24 files) = PY (100%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 1,945 LOC (10); |
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| Duplication: 20% | |||
| File Size: 0% long (>1000 LOC), 22% short (<= 200 LOC) | |||
| Unit Size: 27% long (>100 LOC), 38% short (<= 10 LOC) | |||
| Conditional Complexity: 27% complex (McCabe index > 50), 38% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (5 components) | ||
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3 years, 1 month old
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0% 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
4 files |
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Latest commit date: 2020-07-07
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