---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xlm-roberta-large
  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

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1663
- Accuracy: 0.9515
- Precision: 0.9446
- Recall: 0.9515
- F1: 0.9429

## 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: 44
- eval_batch_size: 44
- seed: 42
- optimizer: Use 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log        | 1.0   | 137  | 0.1615          | 0.954    | 0.9494    | 0.954  | 0.9463 |
| No log        | 2.0   | 274  | 0.1538          | 0.9515   | 0.9453    | 0.9515 | 0.9456 |
| No log        | 3.0   | 411  | 0.1840          | 0.95     | 0.9440    | 0.95   | 0.9454 |
| 0.1742        | 4.0   | 548  | 0.2269          | 0.9465   | 0.9435    | 0.9465 | 0.9448 |
| 0.1742        | 5.0   | 685  | 0.2550          | 0.949    | 0.9434    | 0.949  | 0.9450 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.1
- Tokenizers 0.21.0