Fill-Mask
Transformers
Safetensors
English
bert
protein
protbert
masked-language-modeling
bioinformatics
sequence-prediction
Instructions to use faceless-void/protbert-sequence-unmasking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use faceless-void/protbert-sequence-unmasking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="faceless-void/protbert-sequence-unmasking")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("faceless-void/protbert-sequence-unmasking") model = AutoModelForMaskedLM.from_pretrained("faceless-void/protbert-sequence-unmasking") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a45cc0e3f8c56ba7788911c789a62fa08b9520ef69865d6161d9d3cd83f0e6
- Size of remote file:
- 1.68 GB
- SHA256:
- 35ca0dbb1dfacbad8e44c11d2325f14f8ef97d9c8281932bdec94e02aeca619c
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