Create README.md

README.md

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+---
+license: openrail
+library_name: transformers
+---
+# Model Details
+Classifier-Bias-TahniatKhan is a prototype model crafted to classify content into two categories: "Biased" and "Non-Biased".
+# Model Architecture
+The model is built upon the distilbert-base-uncased architecture and has been fine-tuned on a custom dataset for the specific task of bias detection.
+# Dataset
+The model was trained on a BABE dataset containing news articles from various sources, annotated with one of the 2 bias levels. Biased_Text = 1810 UnBiased_Test=1810
+# Training Procedure
+The model was trained using the Adam optimizer for 6 epochs.
+Performance On our validation set, the model achieved:
+Accuracy: 78% F1 Score (Biased): 79% F1 Score (Non-Biased): 78%
+# How to Use
+To use this model for text classification, use the following code
+''''''
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("/Social-Media-Fairness/Tahniat-Classifier")
+model = AutoModelForSequenceClassification.from_pretrained("Social-Media-Fairness/Tahniat-Classifier")
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+result = classifier("you are stupid")
+print(result)
+# Caveats and Limitations
+The model's training data originates from a specific dataset (BABE) which might not represent all kinds of biases or content.
+The performance metrics are based on a random validation split, so the model's performance might vary in real-world applications.