Instructions to use viswavi/datafinder-scibert-keyword-queries with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use viswavi/datafinder-scibert-keyword-queries with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="viswavi/datafinder-scibert-keyword-queries")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("viswavi/datafinder-scibert-keyword-queries") model = AutoModel.from_pretrained("viswavi/datafinder-scibert-keyword-queries", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from viswavi/datafinder-scibert-keyword-queries: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/viswavi/datafinder-scibert-keyword-queries/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://viswavi/datafinder-scibert-keyword-queries/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/viswavi/datafinder-scibert-keyword-queries/resolve/main/pytorch_model.bin
440 MB
- Xet hash:
- c2180a39118ec0ffe3f4ba13fde0d35311a307257c7fb3457bb1fb71502a874c
- Size of remote file:
- 440 MB
- SHA256:
- ef0d46f630ed7fe8428c3b992bc91db0d78ec742fa71c59a4d976502af8eac6f
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