---
license: apache-2.0
base_model: alex-miller/ODABert
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: cva-quant-weighted-classifier
  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. -->

# cva-quant-weighted-classifier

This model is a fine-tuned version of [alex-miller/ODABert](https://huggingface.co/alex-miller/ODABert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1539
- Accuracy: 0.9643
- F1: 0.9630
- Precision: 0.9286
- Recall: 1.0

## 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: 6e-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6832        | 1.0   | 4    | 0.6577          | 0.6786   | 0.7097 | 0.6111    | 0.8462 |
| 0.6332        | 2.0   | 8    | 0.6121          | 0.8571   | 0.8462 | 0.8462    | 0.8462 |
| 0.587         | 3.0   | 12   | 0.5636          | 0.8571   | 0.8462 | 0.8462    | 0.8462 |
| 0.5308        | 4.0   | 16   | 0.5053          | 0.8571   | 0.8462 | 0.8462    | 0.8462 |
| 0.4738        | 5.0   | 20   | 0.4425          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.3972        | 6.0   | 24   | 0.3848          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.3347        | 7.0   | 28   | 0.3371          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.2769        | 8.0   | 32   | 0.2950          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.2321        | 9.0   | 36   | 0.2621          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.1847        | 10.0  | 40   | 0.2343          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.1524        | 11.0  | 44   | 0.2120          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.1374        | 12.0  | 48   | 0.1935          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.1112        | 13.0  | 52   | 0.1792          | 0.9286   | 0.9231 | 0.9231    | 0.9231 |
| 0.0881        | 14.0  | 56   | 0.1687          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0785        | 15.0  | 60   | 0.1623          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.065         | 16.0  | 64   | 0.1585          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0625        | 17.0  | 68   | 0.1570          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0566        | 18.0  | 72   | 0.1554          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0587        | 19.0  | 76   | 0.1544          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0537        | 20.0  | 80   | 0.1539          | 0.9643   | 0.9630 | 0.9286    | 1.0    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1