Update README.md

README.md

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 library_name: transformers
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-# Eka Doc Classifier (~96M)
+# Med Doc Classifier (~96M)
 
-A compact vision model for classifying health-app uploads — prescriptions shot at an angle, X-rays, Aadhaar cards, discharge summaries, the occasional accidental selfie. A frozen **SigLIP2-base** tower (`google/siglip2-base-patch16-naflex`) with lightweight heads answers three questions about an image in a single pass:
-
-- **What it is** — a two-level label: 5 groups (L1) → 27 leaf classes (L2)
-- **Is it medical?** — binary head
-- **Is it handwritten?** — binary head
+The med-doc-classifier is a lightweight, encoder-only model (~96M parameters) built on the popular SigLIP2-base image backbone. In a single pass it sorts an image into one of 27 classes across two hierarchical levels, from broad group down to specific leaf, and answers two further questions: whether the subject matter is medical, and whether it was handwritten.
 
 The model was built by training many specialised models, ensembling them, and distilling the ensemble into this one compact student.