Instructions to use nlpie/compact-biobert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nlpie/compact-biobert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nlpie/compact-biobert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlpie/compact-biobert") model = AutoModelForMaskedLM.from_pretrained("nlpie/compact-biobert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8bb8a9e2c052006570b6abb064dd35fe5a2d1698836f63908c69f8ad7de2512
- Size of remote file:
- 263 MB
- SHA256:
- 85848b412a8e7d80c2fe81e07fe12f077cd2c563c176d4ae1ea080ab0227fcfe
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