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README.md

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-license: bigscience-openrail-m
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+# UnBIAS Classification Model Card
+
+## Model Description
+
+**UnBIAS** is a state-of-the-art model designed to classify texts based on their bias levels. The model categorizes texts into three classes: "Highly Biased", "Slightly Biased", and "Neutral".
+
+## Model Architecture
+
+The model is built upon the `bert-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 dataset containing news articles from various sources, annotated with one of the three bias levels. The dataset contains:
+- **Highly Biased**: 4000 articles
+- **Slightly Biased**: 4000 articles
+- **Neutral**: 4000 articles
+
+(Replace `XXXX` with actual counts, if available.)
+
+## Training Procedure
+
+The model was trained with a learning rate of `X.Xe-X`, using the Adam optimizer for `XX` epochs. We used a batch size of `XX` and a train-validation split of `XX%-XX%`.
+
+(Replace placeholders with the actual values used during training.)
+
+## Performance
+
+On our validation set, the model achieved:
+- **Accuracy**: 95%
+- **F1 Score (Highly Biased)**: 78%
+- **F1 Score (Slightly Biased)**: 75%
+- **F1 Score (Neutral)**: 72%
+
+(Replace placeholders with actual performance metrics.)
+
+## How to Use
+
+To use this model for text classification, use the following code:
+
+```python
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+ 
+tokenizer = AutoTokenizer.from_pretrained("newsmediabias/UnBIAS-classifier")
+model = AutoModelForSequenceClassification.from_pretrained("newsmediabias/UnBIAS-classifier")
+
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+result = classifier("Women are bad driver.")
+print(result)
+```