Instructions to use AI4Protein/deep_bpe_800 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_800 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_800")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_800") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_800", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9fc4061c22f038619c742d4bab24b5b3e5e62f6330f7a71292614c936424a1a
- Size of remote file:
- 345 MB
- SHA256:
- 7dc1852fadc759c885fee86e81aa8f3197a2edc2a7f838d55a83183fed5ea111
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