Remove index portion for the time being

README.md

@@ -188,10 +188,6 @@ This is a [Multi-Vector Encoder](https://www.sbert.net/docs/multi_vector_encoder
 
 ![NDCG@10 on the MIRIAD medical benchmark versus active parameters, with mLateOn-medical at the top](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-multi-vector-encoder/mve_medical_model_size_ndcg.png)
 
-Late interaction stores one vector per token, so its index is larger than a dense one, and these medical passages are long (about 878 stored vectors each). [`HierarchicalTokenPooling`](https://sbert.net/docs/package_reference/multi_vector_encoder/modules.html#hierarchicaltokenpooling) compresses that cheaply: halving the index costs 0.003 NDCG@10, and even a tenth of the index still scores higher than every other model in the benchmark.
-
-![Index size for a 200,000-passage corpus versus NDCG@10, including the token pooling trajectory](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/train-multi-vector-encoder/mve_medical_index_size_ndcg.png)
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 The full recipe, the evaluation protocol, and the experiments behind each choice are described in [Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers](https://huggingface.co/blog/train-multi-vector-encoder).
 
 ## Model Details