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---
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
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