---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: sentiment-roberta-es-2025-II
  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. -->

# sentiment-roberta-es-2025-II

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the Tweets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8121
- Accuracy: 0.8826
- Precision: 0.8829
- Recall: 0.8826
- F1: 0.8820

## 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.6043        | 1.0   | 656  | 0.6682          | 0.8826   | 0.8824    | 0.8826 | 0.8825 |
| 0.5065        | 2.0   | 1312 | 0.6298          | 0.8720   | 0.8721    | 0.8720 | 0.8720 |
| 0.4613        | 3.0   | 1968 | 0.6470          | 0.8826   | 0.8889    | 0.8826 | 0.8832 |
| 0.2488        | 4.0   | 2624 | 0.7832          | 0.8765   | 0.8811    | 0.8765 | 0.8771 |
| 0.149         | 5.0   | 3280 | 0.8121          | 0.8826   | 0.8829    | 0.8826 | 0.8820 |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1