Instructions to use callaghanmt/scibert-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use callaghanmt/scibert-embed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="callaghanmt/scibert-embed")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("callaghanmt/scibert-embed") model = AutoModel.from_pretrained("callaghanmt/scibert-embed", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from callaghanmt/scibert-embed: direct link, hf CLI and curl.
- Browser
- Download file 717 kB
-
https://huggingface.co/callaghanmt/scibert-embed/resolve/main/tokenizer.json
- Command line
-
hf download hf://callaghanmt/scibert-embed/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/callaghanmt/scibert-embed/resolve/main/tokenizer.json
717 kB
File too large to display, you can check the raw version instead.