Instructions to use viswavi/datafinder-scibert-nl-queries with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viswavi/datafinder-scibert-nl-queries with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="viswavi/datafinder-scibert-nl-queries")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("viswavi/datafinder-scibert-nl-queries") model = AutoModel.from_pretrained("viswavi/datafinder-scibert-nl-queries") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da65a7553552302864d398948432c4f52042e9b54d68ba5a44df7954919dcf9c
- Size of remote file:
- 440 MB
- SHA256:
- ac3a035cacbe9e95530db2299991a3b7384924a60d6ee85a4c3ba8c6013db65e
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