Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+---
+
+DistilBERT AI-vs-Human Text Classifier
+
+This is a binary text classification model built on top of distilbert-base-uncased.
+It has been fine-tuned to distinguish between AI-generated and human-written text.
+
+Base model: DistilBERT (uncased)
+
+Task: Sequence classification
+
+Labels:
+
+0 → Human-written text
+
+1 → AI-generated text
+
+Framework: PyTorch, Hugging Face Transformers
+
+Training Details
+
+Dataset size: 1367 samples
+
+Batch size: 16
+
+Epochs: 10
+
+Learning rate: 5e-6
+
+Performance
+
+Best validation metrics:
+
+Accuracy: 0.5693
+
+Precision: 0.6162
+
+Recall: 0.9858 
+
+F1-score: 0.6814
+
+Usage
+Load the model and tokenizer with the Hugging Face Transformers library, provide a text input, and the model will output a label indicating whether the text is AI-generated or human-written.
+
+License
+MIT License