Instructions to use yarongef/DistilProtBert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yarongef/DistilProtBert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="yarongef/DistilProtBert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("yarongef/DistilProtBert") model = AutoModelForMaskedLM.from_pretrained("yarongef/DistilProtBert") - Notebooks
- Google Colab
- Kaggle
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# DistilProtBert
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Distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
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In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
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Check out our paper [DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts](https://www.biorxiv.org/content/10.1101/2022.05.09.491157v1) for more details.
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# DistilProtBert
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A Distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
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In addition to cross entropy and cosine teacher-student losses, DistilProtBert was pretrained on a masked language modeling (MLM) objective and it only works with capital letter amino acids.
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Check out our paper [DistilProtBert: A distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts](https://www.biorxiv.org/content/10.1101/2022.05.09.491157v1) for more details.
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