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---
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-LPFT-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-LPFT-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: 0.6249
- Accuracy: 0.8030
- F1: 0.4583
- Precision: 0.3929
- Recall: 0.55
## 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: 1337
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6889 | 1.0 | 77 | 0.6662 | 0.6667 | 0.3333 | 0.2391 | 0.55 |
| 0.6354 | 2.0 | 154 | 0.6400 | 0.7879 | 0.2632 | 0.2778 | 0.25 |
| 0.6131 | 3.0 | 231 | 0.6525 | 0.8409 | 0.2759 | 0.4444 | 0.2 |
| 0.5588 | 4.0 | 308 | 0.6100 | 0.8030 | 0.4091 | 0.375 | 0.45 |
| 0.4774 | 5.0 | 385 | 0.6230 | 0.8106 | 0.4444 | 0.4 | 0.5 |
| 0.4569 | 6.0 | 462 | 0.6283 | 0.8106 | 0.4681 | 0.4074 | 0.55 |
| 0.4519 | 7.0 | 539 | 0.6239 | 0.8030 | 0.4583 | 0.3929 | 0.55 |
| 0.4671 | 8.0 | 616 | 0.6284 | 0.8106 | 0.4681 | 0.4074 | 0.55 |
| 0.4231 | 9.0 | 693 | 0.6249 | 0.8030 | 0.4583 | 0.3929 | 0.55 |
### Framework versions
- Transformers 4.57.3
- Pytorch 2.9.1+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
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