Awesome
<p align="center"> <img src="banner.png" alt="blah" /><br> </p> <p align = "center"> <img src="https://img.shields.io/static/v1?label=python&message=3.9&color=blue&style=flat-square"/> <a href="https://pytorch.org/"><img src="https://img.shields.io/static/v1?label=pytorch&message=1.9.0&color=blue&style=flat-square"/></a> <a href="https://huggingface.co/transformers/"><img src="https://img.shields.io/static/v1?label=huggingface&message=4.7.0&color=yellow&style=flat-square"/></a> <a href="https://huggingface.co/docs/datasets/"><img src="https://img.shields.io/static/v1?label=hf-datasets&message=1.11.0&color=yellow&style=flat-square"/></a> <a href="https://www.linkedin.com/company/alchemab-therapeutics-ltd/"><img src="https://img.shields.io/badge/LinkedIn-blue?style=flat&logo=linkedin&labelColor=blue"/></a> <a href="https://twitter.com/alchemabtx"><img src="https://img.shields.io/twitter/follow/alchemabtx?style=social"/></a> </p>AntiBERTa
This is a repository with Jupyter notebooks describing how we pre-trained the model, and how we apply the model for fine-tuning.
FAQs
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What dataset did you use? This is described in our paper, but briefly, we used a section of the Observed Antibody Space (OAS) database (Kovaltsuk et al., 2018) for pre-training, and a snapshot of SAbDab (Dunbar et al., 2014) as of 26 August, 2021. We've included small snippets of the OAS database that we used for pre-training, and the paratope prediction datasets under
assets
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Why HuggingFace? We felt that the maturity of the library and its straight-forward API were key advantages. Not to mention it fits really well with cloud compute architectures like AWS.