---
license: apache-2.0
tags:
- 
datasets:
- EXIST Dataset
- MeTwo Machismo and Sexism Twitter Identification dataset

widget:
- text: "manejas muy bien para ser mujer"
- text: "En temas políticos hombres y mujeres son iguales"
- text: "Los ipad son unos equipos electrónicos"

metrics:
- accuracy
model-index:
- name: twitter_sexismo-finetuned-exist2021
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: EXIST Dataset
      type: EXIST Dataset
      args: es
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.83
---

# twitter_sexismo-finetuned-exist2021

This model is a fine-tuned version of [pysentimiento/robertuito-hate-speech](https://huggingface.co/pysentimiento/robertuito-hate-speech) on the EXIST dataset and MeTwo: Machismo and Sexism Twitter Identification dataset https://github.com/franciscorodriguez92/MeTwo.
It achieves the following results on the evaluation set:
- Loss: 0.54
- Accuracy: 0.83

## Model description
Model for the 'Somos NLP' Hackathon for detecting sexism in twitters in Spanish. Created by:
- **medardodt**
- **MariaIsabel**
- **ManRo**
- **lucel172**
- **robertou2**

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- my_learning_rate = 5E-5 
- my_adam_epsilon = 1E-8 
- my_number_of_epochs = 8
- my_warmup = 3
- my_mini_batch_size = 32
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

|Epoch|Training Loss|Validation Loss|Accuracy|F1|Precision|Precision|
|----|-------|-------|-------|-------|-------|-------| 
|1|0.389900 |0.397857 |0.827133 |0.699620 |0.786325 |0.630137 |
|2|0.064400 |0.544625 |0.831510 |0.707224 |0.794872 |0.636986 | 
|3|0.004800 |0.837723 |0.818381 |0.704626 |0.733333 |0.678082 | 
|4|0.000500 |1.045066 |0.820569 | 0.702899 |0.746154 |0.664384 |
|5|0.000200 |1.172727 |0.805252 |0.669145 |0.731707 |0.616438 | 
|6|0.000200 |1.202422 |0.827133 |0.720848 |0.744526 |0.698630 | 
|7|0.000000 |1.195012 |0.827133 |0.718861 |0.748148 |0.691781 | 
|8|0.000100 |1.215515 |0.824945 |0.705882 |0.761905 |0.657534 | 
|9|0.000100|1.233099 |0.827133 |0.710623 |0.763780 |0.664384 | 
|10|0.000100|1.237268 |0.829322 |0.713235 |0.769841 |0.664384 | 

### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Tokenizers 0.11.6


## Model in Action
Fast usage with pipelines:
``` python
###libraries required
!pip install transformers
from transformers import pipeline

### usage pipelines
model_checkpoint = "hackathon-pln-es/twitter_sexismo-finetuned-exist2021-metwo" 
pipeline_nlp = pipeline("text-classification", model=model_checkpoint)
pipeline_nlp("mujer al volante peligro!") 
#pipeline_nlp("¡me encanta el ipad!") 
#pipeline_nlp (["mujer al volante peligro!", "Los hombre tienen más manias que las mujeres", "me encanta el ipad!"] )

# OUTPUT MODEL #
# LABEL_0: "NON SEXISM"or LABEL_1: "SEXISM"  and score: probability of accuracy per model.

# [{'label': 'LABEL_1', 'score': 0.9967633485794067}]
# [{'label': 'LABEL_0', 'score': 0.9934417009353638}]

#[{‘label': 'LABEL_1', 'score': 0.9967633485794067},
# {'label': 'LABEL_1', 'score': 0.9755664467811584},
# {'label': 'LABEL_0', 'score': 0.9955045580863953}]
```