Instructions to use AI4Protein/deep_bpe_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4Protein/deep_bpe_50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AI4Protein/deep_bpe_50")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("AI4Protein/deep_bpe_50") model = AutoModelForMaskedLM.from_pretrained("AI4Protein/deep_bpe_50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93d84caf7d0e41760d8995c64aa9691a4e004a827fb738acb244281ea4a2c938
- Size of remote file:
- 343 MB
- SHA256:
- be005c14bbda7303f74465f375343dbe583cd07434f58811d18c84400e1c8c6c
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