Feature Extraction
Transformers
Safetensors
English
bert
scibert
fine-tuned
scientific-embeddings
multi-document-summarization
scitldr
text-embeddings-inference
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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("callaghanmt/scibert_embed") model = AutoModel.from_pretrained("callaghanmt/scibert_embed") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 209f788d9419b2a740670771c180a9f7502ad32d6b0d7156e29aa4ead4cbcf96
- Size of remote file:
- 440 MB
- SHA256:
- 6b9e570b7412965a6a5310d035d3c5743f511c9d46f068679f6b779d21ea7f62
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