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---
base_model: camembert/camembert-base-ccnet
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_ccnet_classification_tools_NEFTune_fr_V2
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_NEFTune_fr_V2
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: 0.5108
- Accuracy: 0.9062
- Learning Rate: 0.0001
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.802 | 1.0 | 15 | 1.3063 | 0.7708 | 0.0001 |
| 0.9616 | 2.0 | 30 | 0.7143 | 0.8438 | 0.0001 |
| 0.4359 | 3.0 | 45 | 0.3769 | 0.9271 | 0.0001 |
| 0.2292 | 4.0 | 60 | 0.3546 | 0.9167 | 0.0001 |
| 0.1448 | 5.0 | 75 | 0.2678 | 0.9479 | 0.0001 |
| 0.095 | 6.0 | 90 | 0.4425 | 0.9062 | 9e-05 |
| 0.0762 | 7.0 | 105 | 0.3686 | 0.9062 | 0.0001 |
| 0.0817 | 8.0 | 120 | 0.4784 | 0.9062 | 0.0001 |
| 0.0506 | 9.0 | 135 | 0.4753 | 0.8958 | 0.0001 |
| 0.0245 | 10.0 | 150 | 0.3736 | 0.9167 | 0.0001 |
| 0.0347 | 11.0 | 165 | 0.5036 | 0.9062 | 0.0001 |
| 0.0141 | 12.0 | 180 | 0.4478 | 0.9167 | 8e-05 |
| 0.0196 | 13.0 | 195 | 0.4295 | 0.9167 | 0.0001 |
| 0.009 | 14.0 | 210 | 0.3942 | 0.9167 | 0.0001 |
| 0.0076 | 15.0 | 225 | 0.5108 | 0.9062 | 0.0001 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
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