Commit History
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Add Kaggle silver training data, retrain model, reorganize data directory
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)- Add 2,483 Kaggle resumes with Gemini-extracted labels + BIO tagging + noise augmentation - Retrain model (25 epochs): entity F1 97.77%, clean F1 99.18% - Reorganize training/data/ into sources/, gold/ subdirectories - Add implementation guide (docs/implementation_guide.md) - Remove one-off scripts (build_long_resumes, convert_dataturks, label_resume_pdfs) - Fix Windows UTF-8 encoding in structured_postprocess and train_ner - Add --extra-train flag to train_ner.py for loading additional training files - Export updated ONNX + quantized model Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>ae7305b
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Fix gold labels: 100% match rate (was 88.8%)
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)- Removed 194 hallucinated annotations that didn't exist in extracted text - Corrected 33 annotations with wrong spacing/quotes - Fixed exbc16/exbc17 key swap (annotations were under wrong resume) - Match rate: 2325/2325 (100%) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>b5f1c8a -
Add 93 gold-labeled resume resource training examples
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)Hand-annotated by Claude (not model-predicted) from 98 resume PDFs. 2542 total entities, 2196 matched (86.4%). Compared to silver labels: - Skills: 229 → 3293 (14x improvement) - Dates: 174 → 581 (3.3x) - Companies: 330 → 504 (1.5x) - Languages: 0 → 22 (new) Replaces silver-labeled resume_resource_labeled.json in training pipeline. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>1fcd140 -
Improve post-processing and add 98 real-world resume training data
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)Post-processing fixes: - Drop short COMPANY fragments (<=3 chars) not in gazetteer - Drop short SKILL fragments (<=3 chars lowercase) with allowlist for known short skills (Go, R, C, Bun, Vue, dbt, etc.) - Reject URLs misclassified as EMAIL (// or www. without @) - Filter language proficiency words from SKILL (Native, Fluent, etc.) - Drop TITLE spans <=2 chars Training data: - Add 98 silver-labeled resumes from resume-resource.com PDFs - Wire resume_resource_labeled.json into training pipeline - Add label_resume_pdfs.py for PDF extraction and auto-labeling Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>a11ed90 -
Add 50 long dense resume training examples and new synthetic formats
Somasundaram Ayyappan +1: Claude Opus 4.6 (1M context)- 50 hand-crafted resumes (38/50 exceed 512 subword tokens, avg 549) - Diverse: 15+ industries, 10+ countries, dense OCR-style formatting - Uses + and | separators, bullet points, PROJECTS/LANGUAGES sections - 3 new synthetic formats (F, G, H) with dense layouts - Remove 512-token truncation from generated data - Wire long resumes into training data pipeline Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>efba759 -
Clean up training pipeline and add export benchmarks
Somasundaram Ayyappan750e1a2 -
Initial commit
Somasundaram Ayyappan496f788