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---
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license: mit
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---
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# Model Card for [HIV-BERT]
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## Table of Contents
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- [Table of Contents](#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, 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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[Code snippet of AutoModelForMaskedLM prediction of V3 amino acids.]
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## Training Data
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[The dataset damlab/HIV_FLT was used to refine the original 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 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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## Evaluation Results
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[Table of Prot-Bert and HIV-Bert loss on HIV sequence datasets]
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## BibTeX Entry and Citation Info
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[More Information Needed]
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