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