---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: trigger_cls
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# trigger_cls

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3221
- Accuracy: 0.8876
- Precision: 0.8878
- Recall: 0.8876
- F1: 0.8874

## 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   | 201  | 0.5449          | 0.8120   | 0.8036    | 0.8120 | 0.7961 |
| No log        | 2.0   | 402  | 0.3569          | 0.8757   | 0.8766    | 0.8757 | 0.8736 |
| 0.7649        | 3.0   | 603  | 0.3444          | 0.8826   | 0.8831    | 0.8826 | 0.8822 |
| 0.7649        | 4.0   | 804  | 0.3337          | 0.8832   | 0.8836    | 0.8832 | 0.8830 |
| 0.2524        | 5.0   | 1005 | 0.3221          | 0.8876   | 0.8878    | 0.8876 | 0.8874 |


### Framework versions

- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 4.0.0
- Tokenizers 0.21.1