Instructions to use McGill-NLP/gte-base-dmr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use McGill-NLP/gte-base-dmr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("McGill-NLP/gte-base-dmr") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Transformers
How to use McGill-NLP/gte-base-dmr with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("McGill-NLP/gte-base-dmr", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ce0a85a83273d1ccc2fd75ae966f9720f6c3c3f184d298559d982e7c03209d1e
- Size of remote file:
- 128 Bytes
- SHA256:
- 74a549d340534515b83e1d83fe9bb9c1bbf690eae7aff33516e825e4b6f5de4a
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