---
base_model: camembert/camembert-base-ccnet
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_ccnet_classification_tools_classifier-only_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_classifier-only_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: 0.2297
- Accuracy: 0.975
- 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: 0.001
- 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   |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log        | 1.0   | 7    | 1.7771          | 0.5      | 0.0010 |
| No log        | 2.0   | 14   | 1.5458          | 0.425    | 0.0010 |
| No log        | 3.0   | 21   | 1.2091          | 0.75     | 0.0009 |
| No log        | 4.0   | 28   | 1.0227          | 0.75     | 0.0009 |
| No log        | 5.0   | 35   | 0.9105          | 0.7      | 0.0009 |
| No log        | 6.0   | 42   | 0.7765          | 0.825    | 0.0009 |
| No log        | 7.0   | 49   | 0.7397          | 0.75     | 0.0009 |
| No log        | 8.0   | 56   | 0.6652          | 0.825    | 0.0009 |
| No log        | 9.0   | 63   | 0.6385          | 0.775    | 0.0008 |
| No log        | 10.0  | 70   | 0.6051          | 0.8      | 0.0008 |
| No log        | 11.0  | 77   | 0.5246          | 0.9      | 0.0008 |
| No log        | 12.0  | 84   | 0.5391          | 0.825    | 0.0008 |
| No log        | 13.0  | 91   | 0.5511          | 0.825    | 0.0008 |
| No log        | 14.0  | 98   | 0.4780          | 0.85     | 0.0008 |
| No log        | 15.0  | 105  | 0.4328          | 0.925    | 0.0008 |
| No log        | 16.0  | 112  | 0.4016          | 0.875    | 0.0007 |
| No log        | 17.0  | 119  | 0.4902          | 0.8      | 0.0007 |
| No log        | 18.0  | 126  | 0.4016          | 0.9      | 0.0007 |
| No log        | 19.0  | 133  | 0.4164          | 0.9      | 0.0007 |
| No log        | 20.0  | 140  | 0.3814          | 0.825    | 0.0007 |
| No log        | 21.0  | 147  | 0.3147          | 0.95     | 0.0007 |
| No log        | 22.0  | 154  | 0.3544          | 0.9      | 0.0006 |
| No log        | 23.0  | 161  | 0.3438          | 0.9      | 0.0006 |
| No log        | 24.0  | 168  | 0.3181          | 0.95     | 0.0006 |
| No log        | 25.0  | 175  | 0.3433          | 0.875    | 0.0006 |
| No log        | 26.0  | 182  | 0.3197          | 0.95     | 0.0006 |
| No log        | 27.0  | 189  | 0.3510          | 0.875    | 0.0006 |
| No log        | 28.0  | 196  | 0.3495          | 0.9      | 0.0005 |
| No log        | 29.0  | 203  | 0.2704          | 0.95     | 0.0005 |
| No log        | 30.0  | 210  | 0.3081          | 0.975    | 0.0005 |
| No log        | 31.0  | 217  | 0.3389          | 0.875    | 0.0005 |
| No log        | 32.0  | 224  | 0.2695          | 0.95     | 0.0005 |
| No log        | 33.0  | 231  | 0.2903          | 0.925    | 0.0005 |
| No log        | 34.0  | 238  | 0.3012          | 0.925    | 0.0004 |
| No log        | 35.0  | 245  | 0.2804          | 0.95     | 0.0004 |
| No log        | 36.0  | 252  | 0.2716          | 0.95     | 0.0004 |
| No log        | 37.0  | 259  | 0.3125          | 0.875    | 0.0004 |
| No log        | 38.0  | 266  | 0.2501          | 0.925    | 0.0004 |
| No log        | 39.0  | 273  | 0.2442          | 0.95     | 0.0003 |
| No log        | 40.0  | 280  | 0.2284          | 0.95     | 0.0003 |
| No log        | 41.0  | 287  | 0.2363          | 0.975    | 0.0003 |
| No log        | 42.0  | 294  | 0.2656          | 0.975    | 0.0003 |
| No log        | 43.0  | 301  | 0.2723          | 0.925    | 0.0003 |
| No log        | 44.0  | 308  | 0.2302          | 0.95     | 0.0003 |
| No log        | 45.0  | 315  | 0.2391          | 0.95     | 0.0003 |
| No log        | 46.0  | 322  | 0.2414          | 0.95     | 0.0002 |
| No log        | 47.0  | 329  | 0.2506          | 0.975    | 0.0002 |
| No log        | 48.0  | 336  | 0.2616          | 0.975    | 0.0002 |
| No log        | 49.0  | 343  | 0.2376          | 0.975    | 0.0002 |
| No log        | 50.0  | 350  | 0.2310          | 0.975    | 0.0002 |
| No log        | 51.0  | 357  | 0.2271          | 0.95     | 0.0001 |
| No log        | 52.0  | 364  | 0.2484          | 0.95     | 0.0001 |
| No log        | 53.0  | 371  | 0.2633          | 0.95     | 0.0001 |
| No log        | 54.0  | 378  | 0.2788          | 0.925    | 0.0001 |
| No log        | 55.0  | 385  | 0.2626          | 0.975    | 0.0001 |
| No log        | 56.0  | 392  | 0.2454          | 0.975    | 0.0001 |
| No log        | 57.0  | 399  | 0.2373          | 0.975    | 5e-05  |
| No log        | 58.0  | 406  | 0.2340          | 0.975    | 0.0000 |
| No log        | 59.0  | 413  | 0.2291          | 0.975    | 0.0000 |
| No log        | 60.0  | 420  | 0.2297          | 0.975    | 0.0    |


### Framework versions

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