---
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: []
---

<!-- 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_id

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: 0.0634
- 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