---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinic-best
  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. -->

# distilbert-base-uncased-finetuned-clinic-best

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1552
- Accuracy: 0.9410

## Model description

“The parameters are as follows:

```python
{'num_train_epoch': 9, 'alpha': 0.16484386886358915, 'temperature': 2}

These were obtained using grid search with Optuna.
```

## Training and evaluation data

Clinic dataset from 'datasets'

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.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: 9

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 318  | 0.8641          | 0.7094   |
| 1.1497        | 2.0   | 636  | 0.4002          | 0.8703   |
| 1.1497        | 3.0   | 954  | 0.2381          | 0.9113   |
| 0.3873        | 4.0   | 1272 | 0.1893          | 0.9313   |
| 0.2047        | 5.0   | 1590 | 0.1716          | 0.9339   |
| 0.2047        | 6.0   | 1908 | 0.1632          | 0.9387   |
| 0.165         | 7.0   | 2226 | 0.1589          | 0.9394   |
| 0.151         | 8.0   | 2544 | 0.1561          | 0.94     |
| 0.151         | 9.0   | 2862 | 0.1552          | 0.9410   |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0