---
library_name: transformers
base_model: cardiffnlp/twitter-xlm-roberta-base-hate-spanish
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MultiPRIDE-DualEncoder-MainStage-es
  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. -->

# MultiPRIDE-DualEncoder-MainStage-es

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-hate-spanish](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-hate-spanish) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4609
- Accuracy: 0.8409
- F1: 0.5532
- Precision: 0.4815
- Recall: 0.65

## 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: 8
- eval_batch_size: 8
- seed: 67
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6799        | 1.0   | 77   | 0.6352          | 0.7879   | 0.3636 | 0.3333    | 0.4    |
| 0.5849        | 2.0   | 154  | 0.5760          | 0.8258   | 0.3784 | 0.4118    | 0.35   |
| 0.5354        | 3.0   | 231  | 0.5289          | 0.8106   | 0.5098 | 0.4194    | 0.65   |
| 0.4986        | 4.0   | 308  | 0.4998          | 0.7879   | 0.5333 | 0.4       | 0.8    |
| 0.4482        | 5.0   | 385  | 0.4677          | 0.8333   | 0.56   | 0.4667    | 0.7    |
| 0.4401        | 6.0   | 462  | 0.4600          | 0.8333   | 0.5417 | 0.4643    | 0.65   |
| 0.406         | 7.0   | 539  | 0.4643          | 0.8333   | 0.56   | 0.4667    | 0.7    |
| 0.4508        | 8.0   | 616  | 0.4609          | 0.8409   | 0.5532 | 0.4815    | 0.65   |


### Framework versions

- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1