license: mit
Model Card for [HIV-BERT]
Table of Contents
- Table of Contents
- Summary
- Model Description
- Intended Uses & Limitations
- How to Use
- Training Data
- Training Procedure
- Evaluation Results
- BibTeX Entry and Citation Info
Summary
[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).]
Model Description
[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.]
Intended Uses & Limitations
[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.]
How to use
[Code snippet of AutoModelForMaskedLM prediction of V3 amino acids.]
Training Data
[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.]
Training Procedure
Preprocessing
[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.]
Training
[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.]
Evaluation Results
[Table of Prot-Bert and HIV-Bert loss on HIV sequence datasets]
BibTeX Entry and Citation Info
[More Information Needed]