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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from faceless-void/protbert-sequence-unmasking: direct link, hf CLI and curl.
- Browser
- Download file 3.36 GB
-
https://huggingface.co/faceless-void/protbert-sequence-unmasking/resolve/main/optimizer.pt
- Command line
-
hf download hf://faceless-void/protbert-sequence-unmasking/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/faceless-void/protbert-sequence-unmasking/resolve/main/optimizer.pt
3.36 GB
- Xet hash:
- 31cefe93a7d90dd6c8b1ddc29a0f1c16501d154b3e4f59b77926179018d6c279
- Size of remote file:
- 3.36 GB
- SHA256:
- f4dc4fcb562226d5edc21ddb0974464384d2c3310c08d904af8f5159399ef923
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