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-mar
results: []
xlm-roberta-base-mar
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: 0.1683
- Accuracy: 0.5801
- F1 Binary: 0.2557
- Precision: 0.1758
- Recall: 0.4686
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: 36
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Binary | Precision | Recall |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 363 | 0.1798 | 0.1539 | 0.2667 | 0.1539 | 1.0 |
| 0.1771 | 2.0 | 726 | 0.1632 | 0.4037 | 0.2860 | 0.1753 | 0.7758 |
| 0.1758 | 3.0 | 1089 | 0.1746 | 0.1560 | 0.2668 | 0.1540 | 0.9978 |
| 0.1758 | 4.0 | 1452 | 0.1683 | 0.5801 | 0.2557 | 0.1758 | 0.4686 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0