---
language:
- en
metrics:
- f1
- accuracy
pipeline_tag: text-classification
widget:
 - text: "Every woman wants to be a model. It's codeword for 'I get everything for free and people want me'"
---
### distilbert-base-sexism-detector
This is a fine-tuned model of distilbert-base 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). 

**This is a light model with an 81.2 F1 score. Use this model for fase prediction using the online API, if you like to see our best model with 86.3 F1 score , use this [link](https://huggingface.co/NLP-LTU/BERTweet-large-sexism-detector).**

Classification examples (use these example in the Hosted Inference API in the right panel ):

|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/distilbert-sexism-detector')
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased') 
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(label_pred)

```
```
              precision    recall  f1-score   support

  not sexsit     0.9000    0.9264    0.9130      3030
      sexist     0.7469    0.6784    0.7110       970

    accuracy                         0.8662      4000
   macro avg     0.8234    0.8024    0.8120      4000
weighted avg     0.8628    0.8662    0.8640      4000

```