Instructions to use AI4Protein/deep_bpe_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_200")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_200") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_200") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ce0707c08375f3b2c485eb7e5907193ec4df6191b834bf10fd6a4083358f965
- Size of remote file:
- 344 MB
- SHA256:
- 49537f7450d23c92d0a1648c78b1adb092d7fdaea0fd11477116f314d55c5769
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