dd885cee22
Model: PraxySante/qwen3-0.6b-sft-asr-correction-v15-context-full Source: Original Platform
44 lines
1.2 KiB
Markdown
44 lines
1.2 KiB
Markdown
---
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license: mit
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language: [fr]
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pipeline_tag: text-generation
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tags: [grammar-correction, medical, french, asr-correction, context-aware]
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---
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# Qwen3-0.6B-SFT-ASR-Correction-FR v15-context-full
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Modele final de fine-tuning SFT avec contexte pour la correction d'erreurs ASR medicales en francais.
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## Configuration
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- **Base model**: Qwen/Qwen3-0.6B
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- **Fine-tuning**: SFT (Supervised Fine-Tuning)
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- **Context**: Oui (3 champs: context, input errone, target corrige)
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- **Dataset**: merged_with_context.jsonl (~5.5M paires)
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- **GPUs**: 3x Tesla V100-SXM3-32GB
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- **Effective batch size**: 48 (3 GPUs x 4 batch x 4 grad accum)
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- **Learning rate**: 5e-6
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- **Max seq length**: 384
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- **Steps**: 79 836 (1 epoch)
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## Metriques finales
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- **Train loss**: 0.0726
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- **Best eval loss**: 0.073076
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- **Global step**: 79836
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## Courbe d'evaluation
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- step=1000: eval_loss=0.076286
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- step=11000: eval_loss=0.073498
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- step=21000: eval_loss=0.073254
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- step=31000: eval_loss=0.073145
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- step=41000: eval_loss=0.073099
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- step=51000: eval_loss=0.073082
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- step=61000: eval_loss=0.073087
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- step=71000: eval_loss=0.073086
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## Reprise d'entrainement
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Ce modele a ete repris depuis le checkpoint-31000 de v15-context-continued.
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