Remove README

README.md

@@ -1,47 +0,0 @@
----
-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
-
-```