---
base_model: camembert/camembert-base-ccnet
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_ccnet_classification_tools_qlora-8bit_fr
  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. -->

# camembert_ccnet_classification_tools_qlora-8bit_fr

This model is a fine-tuned version of [camembert/camembert-base-ccnet](https://huggingface.co./camembert/camembert-base-ccnet) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5066
- Accuracy: 0.85
- Learning Rate: 0.0

## 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: 0.0001
- train_batch_size: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Rate   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 2.0629        | 1.0   | 7    | 2.0715          | 0.125    | 0.0001 |
| 2.0638        | 2.0   | 14   | 2.0676          | 0.15     | 0.0001 |
| 2.0614        | 3.0   | 21   | 2.0661          | 0.1      | 0.0001 |
| 2.0676        | 4.0   | 28   | 2.0622          | 0.125    | 0.0001 |
| 2.0502        | 5.0   | 35   | 2.0530          | 0.15     | 0.0001 |
| 2.0409        | 6.0   | 42   | 2.0480          | 0.175    | 9e-05  |
| 2.0457        | 7.0   | 49   | 2.0431          | 0.1      | 0.0001 |
| 2.0401        | 8.0   | 56   | 2.0398          | 0.125    | 0.0001 |
| 2.0239        | 9.0   | 63   | 2.0283          | 0.275    | 0.0001 |
| 2.0257        | 10.0  | 70   | 2.0131          | 0.3      | 0.0001 |
| 1.999         | 11.0  | 77   | 1.9926          | 0.425    | 0.0001 |
| 2.0053        | 12.0  | 84   | 1.9866          | 0.4      | 8e-05  |
| 1.9893        | 13.0  | 91   | 1.9710          | 0.475    | 0.0001 |
| 1.9762        | 14.0  | 98   | 1.9572          | 0.475    | 0.0001 |
| 1.9726        | 15.0  | 105  | 1.9398          | 0.425    | 0.0001 |
| 1.9456        | 16.0  | 112  | 1.9152          | 0.5      | 0.0001 |
| 1.9425        | 17.0  | 119  | 1.8971          | 0.575    | 0.0001 |
| 1.9078        | 18.0  | 126  | 1.8788          | 0.625    | 7e-05  |
| 1.901         | 19.0  | 133  | 1.8608          | 0.65     | 0.0001 |
| 1.8865        | 20.0  | 140  | 1.8407          | 0.625    | 0.0001 |
| 1.8696        | 21.0  | 147  | 1.8234          | 0.675    | 0.0001 |
| 1.8618        | 22.0  | 154  | 1.7964          | 0.725    | 0.0001 |
| 1.8238        | 23.0  | 161  | 1.7778          | 0.725    | 0.0001 |
| 1.8164        | 24.0  | 168  | 1.7739          | 0.675    | 6e-05  |
| 1.823         | 25.0  | 175  | 1.7442          | 0.675    | 0.0001 |
| 1.7892        | 26.0  | 182  | 1.7265          | 0.7      | 0.0001 |
| 1.7865        | 27.0  | 189  | 1.7110          | 0.75     | 0.0001 |
| 1.7643        | 28.0  | 196  | 1.7114          | 0.725    | 0.0001 |
| 1.7613        | 29.0  | 203  | 1.6859          | 0.775    | 0.0001 |
| 1.7681        | 30.0  | 210  | 1.6686          | 0.775    | 5e-05  |
| 1.7251        | 31.0  | 217  | 1.6547          | 0.8      | 0.0000 |
| 1.7215        | 32.0  | 224  | 1.6431          | 0.8      | 0.0000 |
| 1.7304        | 33.0  | 231  | 1.6285          | 0.75     | 0.0000 |
| 1.7182        | 34.0  | 238  | 1.6164          | 0.8      | 0.0000 |
| 1.7099        | 35.0  | 245  | 1.6066          | 0.825    | 0.0000 |
| 1.6902        | 36.0  | 252  | 1.6060          | 0.8      | 4e-05  |
| 1.6839        | 37.0  | 259  | 1.5949          | 0.875    | 0.0000 |
| 1.6627        | 38.0  | 266  | 1.5822          | 0.875    | 0.0000 |
| 1.6558        | 39.0  | 273  | 1.5723          | 0.875    | 0.0000 |
| 1.6667        | 40.0  | 280  | 1.5639          | 0.85     | 0.0000 |
| 1.6663        | 41.0  | 287  | 1.5576          | 0.85     | 0.0000 |
| 1.642         | 42.0  | 294  | 1.5506          | 0.825    | 3e-05  |
| 1.6479        | 43.0  | 301  | 1.5471          | 0.825    | 0.0000 |
| 1.6425        | 44.0  | 308  | 1.5414          | 0.825    | 0.0000 |
| 1.6214        | 45.0  | 315  | 1.5351          | 0.85     | 0.0000 |
| 1.6331        | 46.0  | 322  | 1.5310          | 0.875    | 0.0000 |
| 1.6406        | 47.0  | 329  | 1.5281          | 0.875    | 0.0000 |
| 1.6332        | 48.0  | 336  | 1.5265          | 0.875    | 2e-05  |
| 1.6474        | 49.0  | 343  | 1.5218          | 0.875    | 0.0000 |
| 1.6122        | 50.0  | 350  | 1.5184          | 0.875    | 0.0000 |
| 1.6166        | 51.0  | 357  | 1.5162          | 0.875    | 0.0000 |
| 1.6245        | 52.0  | 364  | 1.5343          | 0.875    | 0.0000 |
| 1.6027        | 53.0  | 371  | 1.5272          | 0.85     | 0.0000 |
| 1.6305        | 54.0  | 378  | 1.5261          | 0.85     | 1e-05  |
| 1.6172        | 55.0  | 385  | 1.5173          | 0.85     | 0.0000 |
| 1.6109        | 56.0  | 392  | 1.5168          | 0.875    | 0.0000 |
| 1.6037        | 57.0  | 399  | 1.5154          | 0.875    | 5e-06  |
| 1.6031        | 58.0  | 406  | 1.4970          | 0.875    | 0.0000 |
| 1.617         | 59.0  | 413  | 1.4961          | 0.875    | 0.0000 |
| 1.5955        | 60.0  | 420  | 1.5066          | 0.85     | 0.0    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1