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--- |
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library_name: transformers |
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base_model: Aubins/distil-bumble-bert |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bbq-distil_bumble_bert |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bbq-distil_bumble_bert |
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This model is a fine-tuned version of [Aubins/distil-bumble-bert](https://huggingface.co/Aubins/distil-bumble-bert) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1032 |
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- Accuracy: 0.9627 |
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- Precision: 0.9432 |
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- Recall: 0.9470 |
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- F1: 0.9451 |
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- Roc Auc: 0.9965 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:| |
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| 0.3899 | 0.1709 | 500 | 0.3561 | 0.8325 | 0.7513 | 0.7559 | 0.7536 | 0.9373 | |
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| 0.3563 | 0.3419 | 1000 | 0.3456 | 0.8429 | 0.7693 | 0.7662 | 0.7678 | 0.9444 | |
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| 0.3987 | 0.5128 | 1500 | 0.3510 | 0.8402 | 0.7658 | 0.7612 | 0.7635 | 0.9424 | |
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| 0.4003 | 0.6838 | 2000 | 0.3447 | 0.8595 | 0.7909 | 0.7957 | 0.7933 | 0.9523 | |
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| 0.2942 | 0.8547 | 2500 | 0.3214 | 0.8660 | 0.7998 | 0.8063 | 0.8031 | 0.9577 | |
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| 0.288 | 1.0256 | 3000 | 0.3118 | 0.8774 | 0.8158 | 0.8245 | 0.8201 | 0.9642 | |
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| 0.2941 | 1.1966 | 3500 | 0.2656 | 0.8886 | 0.8303 | 0.8439 | 0.8370 | 0.9715 | |
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| 0.2818 | 1.3675 | 4000 | 0.2618 | 0.9015 | 0.8458 | 0.8676 | 0.8566 | 0.9763 | |
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| 0.265 | 1.5385 | 4500 | 0.2281 | 0.9093 | 0.8589 | 0.8764 | 0.8676 | 0.9804 | |
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| 0.1927 | 1.7094 | 5000 | 0.1938 | 0.9297 | 0.8929 | 0.9004 | 0.8966 | 0.9869 | |
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| 0.1919 | 1.8803 | 5500 | 0.1726 | 0.9394 | 0.9038 | 0.9190 | 0.9113 | 0.9902 | |
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| 0.1421 | 2.0513 | 6000 | 0.1578 | 0.9426 | 0.9111 | 0.9206 | 0.9158 | 0.9918 | |
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| 0.1481 | 2.2222 | 6500 | 0.1429 | 0.9464 | 0.9189 | 0.9233 | 0.9211 | 0.9930 | |
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| 0.1363 | 2.3932 | 7000 | 0.1219 | 0.9562 | 0.9317 | 0.9397 | 0.9357 | 0.9948 | |
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| 0.2112 | 2.5641 | 7500 | 0.1173 | 0.9594 | 0.9391 | 0.9412 | 0.9402 | 0.9956 | |
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| 0.1424 | 2.7350 | 8000 | 0.1102 | 0.9604 | 0.9391 | 0.9445 | 0.9418 | 0.9961 | |
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| 0.1744 | 2.9060 | 8500 | 0.1032 | 0.9627 | 0.9432 | 0.9470 | 0.9451 | 0.9965 | |
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### Framework versions |
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- Transformers 4.49.0 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.3.2 |
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- Tokenizers 0.21.0 |
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