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--- |
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language: |
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- es |
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library_name: transformers |
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tags: |
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- generated_from_trainer |
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- optuna |
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- shap |
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- toxic |
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- toxicity |
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- news |
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- tweets |
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metrics: |
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- f1 |
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- accuracy |
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pipeline_tag: text-classification |
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base_model: xlm-roberta-base |
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model-index: |
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- name: xlm-roberta-base-finetuned |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# xlm-roberta-base-toxicity (Spanish) |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on 2 datasets, labelled with not_toxic (0) / toxic (1) content from news or tweets. |
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- a private one, provided by @Newtral, containing both tweets and news. |
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- one used for data augmentation purposes, containing only news, obtained from [SurgeHQ.ai](https://app.surgehq.ai/datasets/spanish-toxicity) |
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**This model can not be used for commercial purposes** |
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## Training and evaluation data |
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The test dataset was provided by @Newtral and was kept fixed. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.4852 |
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- eval_f1: 0.8009 |
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- eval_accuracy: 0.901 |
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- eval_runtime: 13.6483 |
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- eval_samples_per_second: 366.347 |
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- eval_steps_per_second: 22.933 |
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- epoch: 5.0 |
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- step: 3595 |
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## Training procedure |
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- Cleaning |
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- Data Augmentation |
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- Optuna for Grid Search |
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- Shap for interpretability |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 7.889038893287002e-06 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 37 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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### Framework versions |
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- Transformers 4.18.0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
