metadata

license: mit
tags:
  - generated_from_trainer
  - optuna
  - shap
  - toxic
  - toxicity
  - news
  - tweets
model-index:
  - name: xlm-roberta-base-finetuned
    results: []
language:
  - es
metrics:
  - f1
  - accuracy
library_name: transformers
pipeline_tag: text-classification

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

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

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

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