Trimmed MetaCLIP-2 text vocab for Bengali

README.md

@@ -14,41 +14,13 @@ datasets:
 
 # metaclip-2-worldwide-m16-384-ben-32768
 
-This model is a 82.6% smaller version of [facebook/metaclip-2-worldwide-m16-384](https://huggingface.co/facebook/metaclip-2-worldwide-m16-384) 
-optimized for Bengali language via **text vocabulary trimming** mined on 
-[Lumberjackk/fineweb-2-trimming](https://huggingface.co/datasets/Lumberjackk/fineweb-2-trimming).
-
-Only the **text encoder** vocabulary and embeddings are trimmed.  
-The **vision encoder** is kept identical to the original.
+This model is a 82.6% smaller version of [facebook/metaclip-2-worldwide-m16-384](https://huggingface.co/facebook/metaclip-2-worldwide-m16-384)
+optimized for Bengali via text vocabulary trimming.
 
 ## Model Statistics
-- **Original text vocabulary size:** 901,629 tokens
-- **Trimmed text vocabulary size:** 32,767 tokens
+- **Original vocab size:** 901,629
+- **Trimmed vocab size:** 32,768
 - **Vocabulary reduction:** 96.4%
-- **Original model size:** 538,547,201 parameters
-- **Trimmed model size:** 93,690,369 parameters
+- **Original params:** 538,547,201
+- **Trimmed params:** 93,690,369
 - **Size reduction:** 82.6%
-
-## Usage
-
-```python
-from transformers import AutoProcessor, AutoModel
-import torch
-
-model = AutoModel.from_pretrained("provisoirement/metaclip-2-worldwide-m16-384-ben-32768")
-processor = AutoProcessor.from_pretrained("provisoirement/metaclip-2-worldwide-m16-384-ben-32768")
-
-# Text-only encoding
-texts = ["a photo of a cat", "a photo of a dog"]
-inputs = processor(text=texts, return_tensors="pt", padding=True)
-with torch.inference_mode():
-    text_features = model.get_text_features(**inputs)
-
-# Image + text (standard CLIP usage)
-# from PIL import Image
-# image = Image.open("image.jpg")
-# inputs = processor(images=image, text=texts, return_tensors="pt", padding=True)
-# with torch.inference_mode():
-#     outputs = model(**inputs)
-#     logits_per_image = outputs.logits_per_image
-```