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