---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-uncased-Federal-Regulations
  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. -->

# bert-base-uncased-Federal-Regulations

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6193
- Accuracy: 0.7332
- Precision: 0.7510
- Recall: 0.7332
- F1: 0.7394
- Roc Auc: 0.7821
- Confusion Matrix: [[2590, 795], [498, 963]]

## 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: 4e-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 | Confusion Matrix            |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:---------------------------:|
| 0.5846        | 1.0   | 600  | 0.6114          | 0.6620   | 0.7393    | 0.6620 | 0.6759 | 0.7668  | [[2092, 1293], [345, 1116]] |
| 0.5123        | 2.0   | 1200 | 0.5976          | 0.7210   | 0.7535    | 0.7210 | 0.7301 | 0.7848  | [[2465, 920], [432, 1029]]  |
| 0.4449        | 3.0   | 1800 | 0.6193          | 0.7332   | 0.7510    | 0.7332 | 0.7394 | 0.7821  | [[2590, 795], [498, 963]]   |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3