Instructions to use damlab/HIV_BERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use damlab/HIV_BERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="damlab/HIV_BERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("damlab/HIV_BERT") model = AutoModelForMaskedLM.from_pretrained("damlab/HIV_BERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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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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## 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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