metadata
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: []
sentiment-roberta-es-2025-II
This model is a fine-tuned version of 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