---
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: 0.0960
- Precision: 0.7626
- Recall: 0.8061
- F1: 0.7838

## 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   | 0.0609          | 0.7207    | 0.7966 | 0.7568 |
| No log        | 2.0   | 108  | 0.0541          | 0.7140    | 0.8054 | 0.7570 |
| No log        | 3.0   | 162  | 0.0585          | 0.7595    | 0.8083 | 0.7831 |
| No log        | 4.0   | 216  | 0.0592          | 0.7384    | 0.8361 | 0.7842 |
| No log        | 5.0   | 270  | 0.0684          | 0.7379    | 0.7827 | 0.7597 |
| No log        | 6.0   | 324  | 0.0649          | 0.7568    | 0.8193 | 0.7868 |
| No log        | 7.0   | 378  | 0.0668          | 0.7585    | 0.8113 | 0.7840 |
| No log        | 8.0   | 432  | 0.0825          | 0.7094    | 0.8142 | 0.7582 |
| No log        | 9.0   | 486  | 0.0767          | 0.7653    | 0.8252 | 0.7941 |
| 0.0406        | 10.0  | 540  | 0.0841          | 0.7621    | 0.8040 | 0.7825 |
| 0.0406        | 11.0  | 594  | 0.0850          | 0.7678    | 0.8032 | 0.7851 |
| 0.0406        | 12.0  | 648  | 0.0880          | 0.7579    | 0.7944 | 0.7757 |
| 0.0406        | 13.0  | 702  | 0.0914          | 0.7619    | 0.8054 | 0.7831 |
| 0.0406        | 14.0  | 756  | 0.0950          | 0.7613    | 0.8003 | 0.7803 |
| 0.0406        | 15.0  | 810  | 0.0960          | 0.7626    | 0.8061 | 0.7838 |


### Framework versions

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