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---
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license: mit
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datasets:
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- damlab/HIV_FLT
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metrics:
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- accuracy
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widget:
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- text: 'C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C'
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example_title: 'V3'
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- text: 'M E P V D P R L E P W K H P G S Q P K T A C T N C Y C K K C C F H C Q V C F I T K A L G I S Y G R K K R R Q R R R A H Q N S Q T H Q A S L S K Q P T S Q P R G D P T G P K E S K K K V E R E T E T D P F D'
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example_title: 'Tat'
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- text: 'P Q I T L W Q R P L V T I K I G G Q L K E A L L D T G A D D T V L E E M N L P G R W K P K M I G G I G G F I K V R Q Y D Q I L I E I C G H K A I G T V L V G P T P V N I I G R N L L T Q I G C T L N F'
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example_title: 'PR'
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---
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# HIV_BERT model
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## Table of Contents
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- [Summary](#model-summary)
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- [Model Description](#model-description)
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- [Intended Uses & Limitations](#intended-uses-&-limitations)
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- [How to Use](#how-to-use)
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- [Training Data](#training-data)
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- [Training Procedure](#training-procedure)
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- [Preprocessing](#preprocessing)
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- [Training](#training)
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- [Evaluation Results](#evaluation-results)
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- [BibTeX Entry and Citation Info](#bibtex-entry-and-citation-info)
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## Summary
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The HIV-BERT model was trained as a refinement of the [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd) for HIV centric tasks. It was refined with whole viral genomes from the [Los Alamos HIV Sequence Database](https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). This pretraining is important for HIV related tasks as the original BFD database contains few viral proteins making it sub-optimal when used as the basis for transfer learning tasks. This model and other related HIV prediction tasks have been published (link).
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## Model Description
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Like the original [ProtBert-BFD model](https://huggingface.co/Rostlab/prot_bert_bfd), this model encodes each amino acid as an individual token. This model was trained using Masked Language Modeling: a process in which a random set of tokens are masked with the model trained on their prediction. This model was trained using the damlab/hiv-flt dataset with 256 amino acid chunks and a 15% mask rate.
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## Intended Uses & Limitations
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As a masked language model this tool can be used to predict expected mutations using a masking approach. This could be used to identify highly mutated sequences, sequencing artifacts, or other contexts. As a BERT model, this tool can also be used as the base for transfer learning. This pretrained model could be used as the base when developing HIV-specific classification tasks.
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## How to use
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As this is a BERT-style Masked Language learner, it can be used to determine the most likely amino acid at a masked position.
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```python
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from transformers import pipeline
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unmasker = pipeline("fill-mask", model="damlab/HIV_FLT")
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unmasker(f"C T R P N [MASK] N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C")
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[
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{
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"score": 0.9581968188285828,
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"token": 17,
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"token_str": "N",
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"sequence": "C T R P N N N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
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},
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{
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"score": 0.022986575961112976,
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"token": 12,
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"token_str": "K",
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"sequence": "C T R P N K N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
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},
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{
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"score": 0.003997281193733215,
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"token": 14,
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"token_str": "D",
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"sequence": "C T R P N D N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
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},
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{
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"score": 0.003636382520198822,
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"token": 15,
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"token_str": "T",
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"sequence": "C T R P N T N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
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},
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{
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"score": 0.002701344434171915,
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"token": 10,
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"token_str": "S",
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"sequence": "C T R P N S N T R K S I R I Q R G P G R A F V T I G K I G N M R Q A H C"
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}
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]
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```
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## Training Data
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The dataset [damlab/HIV_FLT](https://huggingface.co/datasets/damlab/HIV_FLT) was used to refine the original [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd). This dataset contains 1790 full HIV genomes from across the globe. When translated, these genomes contain approximately 3.9 million amino-acid tokens.
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## Training Procedure
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### Preprocessing
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As with the [rostlab/Prot-bert-bfd](https://huggingface.co/Rostlab/prot_bert_bfd) model, the rare amino acids U, Z, O, and B were converted to X and spaces were added between each amino acid. All strings were concatenated and chunked into 256 token chunks for training. A random 20% of chunks were held for validation.
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### Training
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Training was performed with the HuggingFace training module using the MaskedLM data loader with a 15% masking rate. The learning rate was set at E-5, 50K warm-up steps, and a cosine_with_restarts learning rate schedule and continued until 3 consecutive epochs did not improve the loss on the held-out dataset.
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## BibTeX Entry and Citation Info
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[More Information Needed]
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