---
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