metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-orm
results: []
xlm-roberta-base-orm
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: 0.1489
- Accuracy: 0.7726
- F1 Binary: 0.3856
- Precision: 0.3070
- Recall: 0.5185
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 51
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.1794 | 1.0 | 517 | 0.1732 | 0.3619 | 0.2385 | 0.1427 | 0.7258 |
| 0.1723 | 2.0 | 1034 | 0.1690 | 0.5982 | 0.2995 | 0.1970 | 0.6239 |
| 0.1522 | 3.0 | 1551 | 0.1818 | 0.8566 | 0.2847 | 0.4538 | 0.2074 |
| 0.1436 | 4.0 | 2068 | 0.1489 | 0.7726 | 0.3856 | 0.3070 | 0.5185 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0