README.md

metadata

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: []

MultiPRIDE-DualEncoder-LPFT-es

This model is a fine-tuned version of 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