Create README.md

README.md

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+### Model Description
+This model is a Binary classification model trained on the Liar Dataset using the BERT (bert-base-uncased) architecture. The primary task is to classify news articles into different categories, making it suitable for fake news detection.
+BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model known for its effectiveness in natural language processing tasks.
+
+The model classifies the input text into 2 classes, True (Real News) or False (Fake News).
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+The original labels include 'true', 'mostly-true', 'half-true', 'barely-true', 'false', and 'pants-fire'. 
+In this custom mapping, statements labeled as 'true', 'mostly-true', and 'half-true' are all categorized as 'true', while 'barely-true', 'false', and 'pants-fire' are grouped under the 'false' category. 
+This mapping simplifies the classification task into a binary problem, aiming to distinguish between truthful and non-truthful statements.
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+Bias: The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions.