---
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: 0.2017
- 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6608        | 1.0   | 7    | 0.5726          | 0.7857   | 0.7857 | 0.7333    | 0.8462 |
| 0.5107        | 2.0   | 14   | 0.4116          | 0.8571   | 0.8462 | 0.8462    | 0.8462 |
| 0.35          | 3.0   | 21   | 0.3056          | 0.8929   | 0.8800 | 0.9167    | 0.8462 |
| 0.2166        | 4.0   | 28   | 0.2485          | 0.8571   | 0.8462 | 0.8462    | 0.8462 |
| 0.1356        | 5.0   | 35   | 0.1940          | 0.9286   | 0.9231 | 0.9231    | 0.9231 |
| 0.0705        | 6.0   | 42   | 0.1848          | 0.9286   | 0.9231 | 0.9231    | 0.9231 |
| 0.0491        | 7.0   | 49   | 0.1728          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0229        | 8.0   | 56   | 0.1725          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0129        | 9.0   | 63   | 0.1744          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0092        | 10.0  | 70   | 0.1793          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0075        | 11.0  | 77   | 0.1847          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0061        | 12.0  | 84   | 0.1890          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0058        | 13.0  | 91   | 0.1928          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0049        | 14.0  | 98   | 0.1954          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0045        | 15.0  | 105  | 0.1975          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0042        | 16.0  | 112  | 0.1990          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.004         | 17.0  | 119  | 0.2001          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0038        | 18.0  | 126  | 0.2010          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0038        | 19.0  | 133  | 0.2015          | 0.9643   | 0.9630 | 0.9286    | 1.0    |
| 0.0038        | 20.0  | 140  | 0.2017          | 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