---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: criminal-case-classifier1
  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. -->

# criminal-case-classifier1

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: 1.8530
- Accuracy: 0.5077

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 300

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9563        | 0.31  | 10   | 1.1314          | 0.3385   |
| 1.1275        | 0.62  | 20   | 1.0607          | 0.4769   |
| 1.0692        | 0.94  | 30   | 1.0871          | 0.2923   |
| 1.0717        | 1.25  | 40   | 1.1759          | 0.4154   |
| 1.0113        | 1.56  | 50   | 1.1322          | 0.3538   |
| 0.8463        | 1.88  | 60   | 1.1809          | 0.3846   |
| 0.8573        | 2.19  | 70   | 1.0676          | 0.4154   |
| 0.8711        | 2.5   | 80   | 1.0690          | 0.3846   |
| 0.809         | 2.81  | 90   | 1.1253          | 0.4154   |
| 0.7148        | 3.12  | 100  | 1.0913          | 0.4769   |
| 0.5847        | 3.44  | 110  | 1.0920          | 0.5077   |
| 0.5486        | 3.75  | 120  | 1.0597          | 0.5538   |
| 0.5184        | 4.06  | 130  | 1.1016          | 0.4769   |
| 0.2637        | 4.38  | 140  | 1.1908          | 0.4923   |
| 0.3562        | 4.69  | 150  | 1.0238          | 0.5385   |
| 0.3292        | 5.0   | 160  | 1.1011          | 0.5692   |
| 0.1333        | 5.31  | 170  | 1.3049          | 0.5385   |
| 0.1256        | 5.62  | 180  | 1.2819          | 0.5538   |
| 0.1415        | 5.94  | 190  | 1.4929          | 0.5231   |
| 0.0942        | 6.25  | 200  | 1.5290          | 0.5538   |
| 0.0548        | 6.56  | 210  | 1.4844          | 0.5538   |
| 0.0457        | 6.88  | 220  | 1.6174          | 0.5077   |
| 0.0226        | 7.19  | 230  | 1.6499          | 0.5538   |
| 0.032         | 7.5   | 240  | 1.7371          | 0.5077   |
| 0.0158        | 7.81  | 250  | 1.8099          | 0.5385   |
| 0.0244        | 8.12  | 260  | 1.9706          | 0.4769   |
| 0.0134        | 8.44  | 270  | 1.8825          | 0.5231   |
| 0.0117        | 8.75  | 280  | 1.8414          | 0.5077   |
| 0.0111        | 9.06  | 290  | 1.8478          | 0.5077   |
| 0.0107        | 9.38  | 300  | 1.8530          | 0.5077   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2