Instructions to use McGill-NLP/MiniLM-L6-dmr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use McGill-NLP/MiniLM-L6-dmr with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("McGill-NLP/MiniLM-L6-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/MiniLM-L6-dmr with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("McGill-NLP/MiniLM-L6-dmr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a84a59434b12a2262b1c8d9e5471accc82ad0840fe712473025e4f1c5114e81d
- Size of remote file:
- 1.47 kB
- SHA256:
- b3b0a0734314b40eb8116f8b194e713ed7bc6582f88ca52ea280ab573b791e5c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.