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⚡ v4.3: Performance optimizations — ONNX INT8, BGE embedder, batched classification, thread control (#4)
- v4.3 perf: Update chatbot.py (21788a8b7048598304fab13a6167bf3f67a8b9c5) - v4.3 perf: Update app.py (2035652dda03c9851d691969d7776b36425400e9) - v4.3 perf: Update README.md (25234d24bafcc1d8b8186d543d54d5d1e65a38e7) - v4.3 perf: Update requirements.txt (bf34137754d6da31083ba0ce75cb97fb5f131585) - v4.3 perf: Update compare.py (7fb08194ee5e1178ef6e2b4ccb966bf5b4fa0c10) - v4.3 perf: Update ml/export_onnx_v2.py (ad221bd3a31fbb57663c553257e0dd8e2cec068d)f4b6528
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Fix: DeBERTa-v3 fp16 crash on T4 — use fp32, batch=2, grad_accum=16
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Update notebook for T4 GPU: batch=4, grad_accum=8, eval_batch=8
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Add ClauseGuard v4 Colab notebook: DeBERTa-v3-large 2-stage training (LEDGAR→CUAD) with ASL
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Update ML requirements for DeBERTa-v3-large training
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Add ClauseGuard v4 training script (DeBERTa-v3-large + LEDGAR + CUAD + ASL)
Verified signature1dc0b52
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v2: Fix all versions + minimal UI redesign (no gradients, clean layout)
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Add Colab training notebook
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Fix: cast labels to float32 for BCEWithLogitsLoss compatibility
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🛡️ ClauseGuard v1.0 — Full codebase: Extension + Website + API + ML
Verified signature9548e93