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---
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license: mit
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language: protein
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tags:
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- protein language model
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datasets:
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- Uniref50
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---
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# DistilProtBert model
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Distilled protein language of [ProtBert](https://huggingface.co/Rostlab/prot_bert).
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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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# Model description
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DistilProtBert was pretrained on millions of proteins sequences.
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This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of
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publicly available data) with an automatic process to generate inputs and labels from those protein sequences.
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Few important differences between DistilProtBert model and the original ProtBert version are:
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1. The size of the model
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2. The size of the pretraining dataset
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3. Time & hardware used for pretraining
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## Intended uses & limitations
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The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
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### How to use
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The model can be used the same as ProtBert.
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## Training data
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DistilProtBert model was pretrained on [Uniref50](https://www.uniprot.org/downloads), a dataset consisting of ~43 million protein sequences after length filtering (only sequences of length 20 to 512 amino acid were used).
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