---
language:
- en
metrics:
- accuracy
- f1
widget:
- text: >-
    Every woman wants to be a model. It's codeword for 'I get everything for
    free and people want me'
pipeline_tag: text-classification
---
### BERTweet-large-sexism-detector
This is a fine-tuned model of BERTweet-large on the Explainable Detection of Online Sexism (EDOS) dataset. It is intended to be used as a classification model for identifying tweets (0 - not sexist; 1 - sexist). 

More information about the original pre-trained model can be found [here](https://huggingface.co/docs/transformers/model_doc/bertweet)

Our model accuracy was 89.72 using the test set and 86.13 F1-score.

Classification examples:

|Prediction|Tweet|
|-----|--------|
|sexist         |Every woman wants to be a model. It's codeword for "I get everything for free and people want me" |
|not sexist     |basically I placed more value on her than I should then?|
# More Details 
For more details  about the datasets and eval results, see (we will updated the page with our paper link)
# How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer,pipeline
import torch
model = AutoModelForSequenceClassification.from_pretrained('NLP-LTU/bertweet-large-sexism-detector')
tokenizer = AutoTokenizer.from_pretrained('NLP-LTU/bertweet-large-sexism-detector') 
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
prediction=classifier("Every woman wants to be a model. It's codeword for 'I get everything for free and people want me' ")
# label_pred = 'not sexist' if prediction == 0 else 'sexist' 

print(prediction)
```
our system rank 10 out of 84 teams, and our results on the test set was:
```
              precision    recall  f1-score   support

  not sexsit     0.9355    0.9284    0.9319      3030
      sexist     0.7815    0.8000    0.7906       970

    accuracy                         0.8972      4000
   macro avg     0.8585    0.8642    0.8613      4000
weighted avg     0.8981    0.8972    0.8977      4000
```
tn 2813, fp 217, fn 194, tp 776```