---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base_42
  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-base_42

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3930
- F1-score: 0.8657
- Accuracy: 0.8657
- Precision: 0.8658
- Recall: 0.8659

## 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-06
- train_batch_size: 16
- eval_batch_size: 16
- 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: 6

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1-score | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| No log        | 1.0   | 379  | 0.4004          | 0.8284   | 0.8287   | 0.8299    | 0.8282 |
| 0.5426        | 2.0   | 758  | 0.3531          | 0.8502   | 0.8503   | 0.8508    | 0.8500 |
| 0.4202        | 3.0   | 1137 | 0.3569          | 0.8564   | 0.8565   | 0.8566    | 0.8563 |
| 0.3646        | 4.0   | 1516 | 0.3520          | 0.8688   | 0.8688   | 0.8689    | 0.8687 |
| 0.3646        | 5.0   | 1895 | 0.4078          | 0.8564   | 0.8565   | 0.8577    | 0.8569 |
| 0.3229        | 6.0   | 2274 | 0.3930          | 0.8657   | 0.8657   | 0.8658    | 0.8659 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0