metadata
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: trigger_id
results: []
trigger_id
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: 0.0634
- Accuracy: 0.9780
- Precision: 0.7114
- Recall: 0.6376
- F1: 0.6725
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 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 | 38 | 0.1618 | 0.9513 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 76 | 0.0873 | 0.9742 | 0.7385 | 0.5685 | 0.6424 |
| No log | 3.0 | 114 | 0.0693 | 0.9773 | 0.7357 | 0.5968 | 0.6590 |
| No log | 4.0 | 152 | 0.0665 | 0.9771 | 0.6768 | 0.6777 | 0.6773 |
| No log | 5.0 | 190 | 0.0634 | 0.9780 | 0.7114 | 0.6376 | 0.6725 |
Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.1