Update README.md

README.md

@@ -230,7 +230,7 @@ _Thanks to Dr. Paul K. Yu for sharing his environment configuration._
 
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-The notebook [`4. Protein Embedding Generation.ipynb`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/experiments/4.%20Protein%20Embedding%20Generation.ipynb) has a dependency (`bio_embeddings`) that requires it to be run on Unix or a Unix-like operating system. We did not include this dependency in [`environment_experiments.yaml`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/environment_experiments.yaml) to maintain cross-platform compatibility; you have to install it following the instructions [here](https://docs.bioembeddings.com/v0.2.3/). If you are using Windows, consider using [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/install) (WSL) or a virtual machine.
+The notebook [`4. Protein Embedding Generation.ipynb`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/experiments/4.%20Protein%20Embedding%20Generation.ipynb) has a dependency (`bio_embeddings`) that requires it to be run on Unix or a Unix-like operating system. If you are using Windows, consider using [Windows Subsystem for Linux](https://learn.microsoft.com/en-us/windows/wsl/install) (WSL) or a virtual machine. We did not include `bio_embeddings` in [`environment_experiments.yaml`](https://github.com/bioinfodlsu/phage-host-prediction/blob/main/environment_experiments.yaml) to maintain cross-platform compatibility; you have to install it following the instructions [here](https://docs.bioembeddings.com/v0.2.3/). 
 
 Moreover, generating protein embeddings should ideally be done on a machine with a GPU. The largest (and best-performing) protein language model that we used, ProtT5, consumes 5.9 GB of GPU memory. If your local machine does not have a GPU or if its GPU has insufficient memory, we recommend using a cloud GPU platform.