Update README.md

README.md

@@ -35,9 +35,9 @@ Teraflop AI’s data engine allows for the massively parallel processing of web-
 
 We additionally provide bge-base-en-v1.5 embeddings for the first 512 tokens of each state jurisdiction and federal case law as well as the post-processed documents. Mean pooling and normalization were used for the embeddings.
 
-We used the Sentence Transformers library maintained by Tom Aarsen of Hugging Face to distribute the embedding process across multiple GPUs. You can find an example of how to use multiprocessing for embeddings [here](https://github.com/UKPLab/sentence-transformers/blob/66e0ee30843dd411c64f37f65447bb38c7bf857a/examples/applications/computing-embeddings/computing_embeddings_multi_gpu.py).
+We used the Sentence Transformers library maintained by Tom Aarsen of Hugging Face to distribute the embedding process across multiple GPUs. Find an example of how to use multiprocessing for embeddings [here](https://github.com/UKPLab/sentence-transformers/blob/66e0ee30843dd411c64f37f65447bb38c7bf857a/examples/applications/computing-embeddings/computing_embeddings_multi_gpu.py).
 
-We improved the inference throughput of the embedding process by using Tri Dao’s Flash Attention. You can find the Flash Attention repository [here](https://github.com/Dao-AILab/flash-attention).
+We improved the inference throughput of the embedding process by using Tri Dao’s Flash Attention. Find the Flash Attention repository [here](https://github.com/Dao-AILab/flash-attention).
 
 You can read the research paper on the BGE embedding models by Shitao Xiao and Zheng Liu [here](https://arxiv.org/pdf/2309.07597.pdf).