---
license: apache-2.0
tags:
- text-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: misogynistic-statements-classification-model
  results: []
widget:
- text: Las mujeres deben ser madres antes que nada
  example_title: Machista
- text: >-
    Las mujeres tienen el mismo potencial y habilidades para los negocios que
    los hombres
  example_title: No machista
datasets:
- glombardo/misogynistic-statements-classification
language:
- es
output_data:
- format: class
- class_labels: ["Sexist", "Non-sexist"]
---

<!-- 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. -->

# Misogynistic statements classification model

**Model that classifies text as sexist or non-sexist.**

This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the [misogynistic-statements-classification dataset](https://huggingface.co/datasets/glombardo/misogynistic-statements-classification).
It achieves the following results on the evaluation set:
- Loss: 0.2493
- Accuracy: 0.9524

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3