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:
- 1536609a6785d7594f96571b74a94b550f3517b2c3be43168250840c0687bfc5
- Size of remote file:
- 1.06 kB
- SHA256:
- 8a37b914570a137609bc9fec92c7bb98c4b9c8f591f4b2562a18d5fdc7eb0ee2
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