Instructions to use Dauka-transformers/interpro_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dauka-transformers/interpro_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Dauka-transformers/interpro_bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Dauka-transformers/interpro_bert") model = AutoModelForMaskedLM.from_pretrained("Dauka-transformers/interpro_bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da5f078f69bdf6696ebae6e6de1178c8f0291c9a32b2860c2f67448c5afc4fd4
- Size of remote file:
- 16.2 MB
- SHA256:
- 228af9df9af4161ad4520213548c2d26ac237b2d24af9517dda7e005a1e96fa9
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