Instructions to use Vinuit/SentinelAI-Filter-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Vinuit/SentinelAI-Filter-ONNX with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Upload training_log.json with huggingface_hub
Browse files- training_log.json +45 -0
training_log.json
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{
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"experiment_id": "bert_dual_head_20260213_060359",
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"config": {
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"model_name": "bert-base-uncased",
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"num_category_classes": 7,
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"num_severity_classes": 4,
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"batch_size": 16,
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"learning_rate": 0.0003,
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"num_epochs": 3,
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"max_length": 128,
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"lora_r": 8,
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"lora_alpha": 16,
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"lora_dropout": 0.1,
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"seed": 42
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},
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"device": "cuda",
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"epochs": [
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{
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"epoch": 1,
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"train_loss": 2.1082094073295594,
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"val_loss": 1.4672169902107932,
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"val_category_acc": 0.6914285714285714,
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"val_severity_acc": 0.7457142857142857
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},
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{
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"epoch": 2,
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"train_loss": 1.4187353157997131,
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"val_loss": 1.252626198259267,
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"val_category_acc": 0.7628571428571429,
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"val_severity_acc": 0.7771428571428571
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},
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{
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"epoch": 3,
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"train_loss": 1.2215006688662937,
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"val_loss": 1.1916442256082187,
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"val_category_acc": 0.7628571428571429,
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"val_severity_acc": 0.7828571428571428
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}
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],
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"final_test_metrics": {
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"loss": 1.1840462291782552,
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"category_accuracy": 0.7628571428571429,
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"severity_accuracy": 0.7828571428571428
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}
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}
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