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