File size: 2,736 Bytes
10d995a 8356876 10d995a 7c034ff 533d37e b573649 533d37e 10d995a 3bb6bbf 1710191 b63691f ec6857f b63691f 10d995a 4d1a1db 10d995a f60eeb6 10d995a dc98dd8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
---
license: mit
language: protein
tags:
- protein language model
datasets:
- Uniref50
---
# DistilProtBert
Distilled version of [ProtBert-UniRef100](https://huggingface.co/Rostlab/prot_bert) model.
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.
[Git](https://github.com/yarongef/DistilProtBert) repository.
# Model details
| **Model** | **# of parameters** | **# of hidden layers** | **Pretraining dataset** | **# of proteins** | **Pretraining hardware** |
|:--------------:|:-------------------:|:----------------------:|:-----------------------:|:------------------------------:|:------------------------:|
| ProtBert | 420M | 30 | UniRef100 | 216M | 512 16GB TPUs |
| DistilProtBert | 230M | 15 | UniRef50 | 43M | 5 v100 32GB GPUs |
## Intended uses & limitations
The model could be used for protein feature extraction or to be fine-tuned on downstream tasks.
### How to use
The model can be used the same as ProtBert and with ProtBert's tokenizer.
## Training data
DistilProtBert model was pretrained on [Uniref50](https://www.uniprot.org/downloads), a dataset consisting of ~43 million protein sequences (only sequences of length between 20 to 512 amino acids were used).
# Pretraining procedure
Preprocessing was done using ProtBert's tokenizer.
The details of the masking procedure for each sequence followed the original Bert (as mentioned in [ProtBert](https://huggingface.co/Rostlab/prot_bert)).
The model was pretrained on a single DGX cluster for 3 epochs in total. local batch size was 16, the optimizer used was AdamW with a learning rate of 5e-5 and mixed precision settings.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
| Task/Dataset | secondary structure (3-states) | Membrane |
|:-----:|:-----:|:-----:|
| CASP12 | 72 | |
| TS115 | 81 | |
| CB513 | 79 | |
| DeepLoc | | 86 |
Distinguish between proteins and their k-let shuffled versions:
_Singlet_
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.71 |
| ProtBert | 0.93 |
| DistilProtBert | 0.92 |
_Doublet_
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.68 |
| ProtBert | 0.92 |
| DistilProtBert | 0.91 |
_Triplet_
| Model | AUC |
|:--------------:|:-------:|
| LSTM | 0.61 |
| ProtBert | 0.92 |
| DistilProtBert | 0.87 | |