Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+- sweatSmile/neet-biology-qa
+language:
+- en
+base_model:
+- distilbert/distilbert-base-uncased
+pipeline_tag: question-answering
+library_name: transformers
+tags:
+- neet
+- biology
+- exam
+- bio
+---
+
+DistilBERT NEET Biology MCQ Classifier
+
+This model is a fine-tuned version of DistilBERT (base uncased) specifically trained to classify the correct option for NEET-style multiple-choice biology questions. It selects the best answer among four choices (A, B, C, D).
+
+-------------------------------------------------------------------------
+Training Data
+
+Source: sweatSmile / NEET Biology QA Dataset
+
+Domain: NEET (Undergraduate Medical Entrance Exam) – Biology
+
+Format: Each question has 4 options with one correct answer
+
+Dataset Size: 793 questions
+
+Split: 80% train / 20% validation
+
+-------------------------------------------------------------------------
+Training Configuration
+
+Base Model:	distilbert-base-uncased
+
+Epochs:	10
+
+Batch Size:	4
+
+Learning Rate:	5e-5
+
+Weight Decay:	0.01
+
+Task Type:	Multiple Choice Classification
+
+-------------------------------------------------------------------------
+Results
+
+Validation Accuracy	72.96% (~73%)
+
+Final Training Loss	~0.35
+
+-------------------------------------------------------------------------
+Limitations
+
+Trained on a relatively small dataset (793 questions).
+
+Limited to NEET-level biology content; not suitable for physics or chemistry.
+
+Does not support:
+
+Assertion-reasoning questions
+
+Diagram-based questions
+
+Paragraph/Case study type questions
+
+-------------------------------------------------------------------------
+Intended Use
+
+Educational Research
+
+AI-powered NEET Biology assistants
+
+MCQ practice evaluation
+
+Baseline model for future fine-tuning with larger datasets
+
+-------------------------------------------------------------------------
+NOTE:
+
+Not recommended as a final exam-ready solution without further fine-tuning and validation.
+
+-------------------------------------------------------------------------
+License: MIT