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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from faceless-void/protbert-sequence-unmasking: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/faceless-void/protbert-sequence-unmasking/resolve/main/training_args.bin
- Command line
-
hf download hf://faceless-void/protbert-sequence-unmasking/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/faceless-void/protbert-sequence-unmasking/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- bbf4301ccfa4480b9fdbbad530072f607f3924c466837454e9efcfa392017304
- Size of remote file:
- 5.11 kB
- SHA256:
- 8c64f819b82db521aed3aae169ccb123b22c7faf045c7f09555f4a5463923735
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