Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- f1
+- accuracy
+pipeline_tag: text-classification
+---
+### 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
+
+```