metadata

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: []

xlm-roberta-base-toxicity (Spanish)

This model is a fine-tuned version of 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

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