Create README.md

README.md

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+---
+language:
+- en
+---
+### 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)
+
+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('vinai/bertweet-large') 
+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)
+```