Update README.md

README.md

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 **Model Summary:** Granite-embedding-english-r2 is a 149M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. 
 
-The r2 models feature an increased context length of **8192** and deliver superior performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR), conversational multi-turn (MTRAG), TableIR (TBD), and on many enterprise use cases.
+The r2 models feature an increased context length of 8192 and deliver superior performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR), conversational multi-turn (MTRAG), TableIR (TBD), and on many enterprise use cases.
 
 These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-english-r2 is optimized to ensure strong alignment between query and passage embeddings.
 
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 ## Evaluation Results
-The performance of the Granite Embedding English models on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below. 
+The performance of the granite embedding r2 models on MTEB Retrieval (i.e., BEIR) and code retrieval (CoIR) benchmarks is reported below. 
 The average speed to encode documents on a single 5090 GPU using a sliding window with 512 context length is also reported.
 
 | Model                              | Parameters (M) | Embedding Size | BEIR Retrieval (15) | MTEB-v2 (56)| CoIR (10) | MLDR (En) | MTRAG (4) |  Encoding Speed (documents/sec) |