Update README.md

README.md

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+---
+license: cc-by-sa-4.0
+language:
+- en
+pipeline_tag: text-generation
+---
 # **PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers**
 
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/plapt-protein-ligand-binding-affinity/protein-ligand-affinity-prediction-on-csar)](https://paperswithcode.com/sota/protein-ligand-affinity-prediction-on-csar?p=plapt-protein-ligand-binding-affinity)
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 We source protein-ligand pairs and their corresponding affinity values from an open-source binding affinity dataset on hugginface, [binding_affinity](https://huggingface.co/datasets/jglaser/binding_affinity). We then used ProtBERT and ChemBERTa for encoding proteins and ligands respectively, giving us high quality vector-space representations. The encoding process is detailed in the `encoding.ipynb` notebook. The dataset, already encoded, is available on our [Google Drive](https://drive.google.com/drive/folders/1e-ujgHx5bW0JKxSZY5u34As77o4-IIFs?usp=sharing) for ease of access and use.
 
 #### Importing Encoders and Running the Notebook
-For users to import the encoders and run the Wolfram notebook (`WL Notebooks/FinalEssay.nb`), we provide the `encoders_to_onnx.ipynb` notebook. This ensures that users can replicate our encoding process and utilize the full capabilities of PLAPT.
+For users to import the encoders and run the Wolfram notebook (`WL Notebooks/FinalEssay.nb`), we provide the `encoders_to_onnx.ipynb` notebook. This ensures that users can replicate our encoding process and utilize the full capabilities of PLAPT.