---
license: mit
base_model: almanach/camembert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NLP_projet
  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. -->

# NLP_projet

This model is a fine-tuned version of [almanach/camembert-base](https://huggingface.co/almanach/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5036
- Precision: 0.9590
- Recall: 0.9634
- F1: 0.9612
- Accuracy: 0.9636

## 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: 2e-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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.7382        | 1.0   | 955  | 0.7058          | 0.9442    | 0.9551 | 0.9496 | 0.9554   |
| 0.6625        | 2.0   | 1910 | 0.5036          | 0.9590    | 0.9634 | 0.9612 | 0.9636   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2