---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: xlm-roberta-base-finetuned-detests24
  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. -->

# xlm-roberta-base-finetuned-detests24

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: 1.0941
- Accuracy: 0.8151
- F1-score: 0.7439
- Precision: 0.7380
- Recall: 0.7509
- Auc: 0.7509

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.4432        | 1.0   | 153  | 0.4079          | 0.8298   | 0.7158   | 0.7778    | 0.6893 | 0.6893 |
| 0.4326        | 2.0   | 306  | 0.5061          | 0.7447   | 0.7078   | 0.7052    | 0.7840 | 0.7840 |
| 0.2533        | 3.0   | 459  | 0.5227          | 0.7676   | 0.7195   | 0.7070    | 0.7709 | 0.7709 |
| 0.3354        | 4.0   | 612  | 0.5113          | 0.8347   | 0.7689   | 0.7645    | 0.7737 | 0.7737 |
| 0.2157        | 5.0   | 765  | 0.8228          | 0.8020   | 0.7484   | 0.7321    | 0.7830 | 0.7830 |
| 0.1815        | 6.0   | 918  | 0.9407          | 0.8036   | 0.7528   | 0.7359    | 0.7917 | 0.7917 |
| 0.0829        | 7.0   | 1071 | 0.9539          | 0.8363   | 0.7648   | 0.7676    | 0.7621 | 0.7621 |
| 0.1077        | 8.0   | 1224 | 0.9649          | 0.8200   | 0.7501   | 0.7445    | 0.7566 | 0.7566 |
| 0.0473        | 9.0   | 1377 | 1.0557          | 0.8200   | 0.7439   | 0.7439    | 0.7439 | 0.7439 |
| 0.0632        | 10.0  | 1530 | 1.0941          | 0.8151   | 0.7439   | 0.7380    | 0.7509 | 0.7509 |


### Framework versions

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