---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-finetuned-detests-wandb24
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-base-finetuned-detests-wandb24

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: 0.4371
- Accuracy: 0.7938
- F1-score: 0.7241
- Precision: 0.7136
- Recall: 0.7396
- Auc: 0.7396

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.458         | 1.0   | 153  | 0.4512          | 0.7725   | 0.4358   | 0.3863    | 0.5    | 0.5    |
| 0.4262        | 2.0   | 306  | 0.4371          | 0.7938   | 0.7241   | 0.7136    | 0.7396 | 0.7396 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1