xlmr-tass-sentiment
This model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6529
- Accuracy: 0.6701
- F1: 0.6679
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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.9273 | 1.0 | 121 | 1.6807 | 0.6098 | 0.5668 |
| 1.6355 | 2.0 | 242 | 1.5585 | 0.6764 | 0.6650 |
| 1.4125 | 3.0 | 363 | 1.7019 | 0.6774 | 0.6652 |
| 1.2037 | 4.0 | 484 | 1.6523 | 0.6691 | 0.6669 |
| 1.0487 | 5.0 | 605 | 1.7443 | 0.6691 | 0.6657 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2