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---
license: cc-by-nc-sa-4.0
language:
- en
metrics:
- f1
- accuracy
widget:
- text: Girls like attention and they get desperate
tags:
- sexism
datasets:
- tum-nlp/sexism-socialmedia-balanced
---
# BERTweet for sexism detection
This is a fine-tuned BERTweet large ([BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/)) model for detecting sexism.
The training dataset is **new balanced** version of Explainable Detection of Online Sexism ([**EDOS**](https://github.com/rewire-online/edos))--[sexism-socialmedia-balanced](https://huggingface.co/datasets/tum-nlp/sexism-socialmedia-balanced)--consisting of 16000 entries in
English gathered from social media platforms: Twitter and Gab. It achieved a **Macro-F1** score of **0.85** and an **Accuracy** of **0.88** on the test set for the EDOS task.
## How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('tum-nlp/bertweet-sexism')
model = AutoModelForSequenceClassification.from_pretrained('tum-nlp/bertweet-sexism')
# Create the pipeline for classification
sexism_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Predict
sexism_classifier("Girls like attention and they get desperate")
```
## Licensing Information
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png |