---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Revision_Meta_XLM
  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. -->

# Revision_Meta_XLM

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4710
- Accuracy: 0.9220
- F1: 0.7251

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 | Accuracy | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| No log        | 0.5102  | 150  | 0.3977          | 0.8526   | 0.4695 |
| 0.5230        | 1.0204  | 300  | 0.2417          | 0.9188   | 0.7149 |
| 0.5230        | 1.5306  | 450  | 0.2309          | 0.9220   | 0.7111 |
| 0.2723        | 2.0408  | 600  | 0.2386          | 0.9180   | 0.7130 |
| 0.2723        | 2.5510  | 750  | 0.2537          | 0.9259   | 0.7229 |
| 0.2107        | 3.0612  | 900  | 0.2225          | 0.9354   | 0.7547 |
| 0.2107        | 3.5714  | 1050 | 0.2462          | 0.9330   | 0.7376 |
| 0.1682        | 4.0816  | 1200 | 0.3432          | 0.9117   | 0.7101 |
| 0.1682        | 4.5918  | 1350 | 0.2572          | 0.9267   | 0.7195 |
| 0.1499        | 5.1020  | 1500 | 0.3172          | 0.9243   | 0.7344 |
| 0.1499        | 5.6122  | 1650 | 0.3080          | 0.9212   | 0.7206 |
| 0.1219        | 6.1224  | 1800 | 0.3767          | 0.9133   | 0.6921 |
| 0.1219        | 6.6327  | 1950 | 0.4346          | 0.9039   | 0.7024 |
| 0.1055        | 7.1429  | 2100 | 0.3059          | 0.9338   | 0.7309 |
| 0.1055        | 7.6531  | 2250 | 0.3716          | 0.9236   | 0.7237 |
| 0.0786        | 8.1633  | 2400 | 0.4106          | 0.9188   | 0.7174 |
| 0.0786        | 8.6735  | 2550 | 0.4007          | 0.9228   | 0.7250 |
| 0.0673        | 9.1837  | 2700 | 0.4195          | 0.9236   | 0.7309 |
| 0.0673        | 9.6939  | 2850 | 0.4486          | 0.9149   | 0.7102 |
| 0.0589        | 10.2041 | 3000 | 0.5117          | 0.9078   | 0.6939 |
| 0.0589        | 10.7143 | 3150 | 0.4755          | 0.9149   | 0.7154 |
| 0.0494        | 11.2245 | 3300 | 0.4287          | 0.9204   | 0.7198 |
| 0.0494        | 11.7347 | 3450 | 0.3905          | 0.9314   | 0.7322 |
| 0.0426        | 12.2449 | 3600 | 0.4332          | 0.9243   | 0.7333 |
| 0.0426        | 12.7551 | 3750 | 0.4228          | 0.9307   | 0.7385 |
| 0.0339        | 13.2653 | 3900 | 0.4589          | 0.9275   | 0.7380 |
| 0.0339        | 13.7755 | 4050 | 0.5148          | 0.9141   | 0.7128 |
| 0.0272        | 14.2857 | 4200 | 0.4624          | 0.9236   | 0.7273 |
| 0.0272        | 14.7959 | 4350 | 0.4710          | 0.9220   | 0.7251 |


### Framework versions

- Transformers 5.3.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2