---
library_name: transformers
license: mit
base_model: xaviergillard/xlm-roberta-large-vieille-france
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: xlm-roberta-large-vieille-france-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. -->

# xlm-roberta-large-vieille-france-v2

This model is a fine-tuned version of [xaviergillard/xlm-roberta-large-vieille-france](https://huggingface.co/xaviergillard/xlm-roberta-large-vieille-france) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0724
- Precision: 0.7526
- Recall: 0.8044
- F1: 0.7776

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log        | 1.0   | 54   | 0.0555          | 0.6035    | 0.6928 | 0.6451 |
| No log        | 2.0   | 108  | 0.0457          | 0.7216    | 0.7963 | 0.7571 |
| No log        | 3.0   | 162  | 0.0480          | 0.7147    | 0.8060 | 0.7576 |
| No log        | 4.0   | 216  | 0.0462          | 0.7173    | 0.8100 | 0.7608 |
| No log        | 5.0   | 270  | 0.0494          | 0.7536    | 0.8036 | 0.7778 |
| No log        | 6.0   | 324  | 0.0580          | 0.7619    | 0.8044 | 0.7825 |
| No log        | 7.0   | 378  | 0.0538          | 0.7487    | 0.7971 | 0.7721 |
| No log        | 8.0   | 432  | 0.0597          | 0.7520    | 0.8165 | 0.7829 |
| No log        | 9.0   | 486  | 0.0638          | 0.7345    | 0.8052 | 0.7682 |
| 0.0888        | 10.0  | 540  | 0.0658          | 0.7579    | 0.8173 | 0.7865 |
| 0.0888        | 11.0  | 594  | 0.0636          | 0.7506    | 0.8100 | 0.7792 |
| 0.0888        | 12.0  | 648  | 0.0685          | 0.7496    | 0.8011 | 0.7745 |
| 0.0888        | 13.0  | 702  | 0.0695          | 0.7507    | 0.8133 | 0.7808 |
| 0.0888        | 14.0  | 756  | 0.0715          | 0.7511    | 0.8076 | 0.7783 |
| 0.0888        | 15.0  | 810  | 0.0724          | 0.7526    | 0.8044 | 0.7776 |


### Framework versions

- Transformers 4.48.3
- Pytorch 2.1.2
- Datasets 3.3.0
- Tokenizers 0.21.0