Instructions to use NoesisLab/Collins-Embedding-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use NoesisLab/Collins-Embedding-3M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("NoesisLab/Collins-Embedding-3M") 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] - Notebooks
- Google Colab
- Kaggle
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README.md
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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- dense
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- loss:MultipleNegativesRankingLoss
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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license: apache-2.0
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- sentence-transformers
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- sentence-similarity
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- feature-extraction
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pipeline_tag: sentence-similarity
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library_name: sentence-transformers
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license: apache-2.0
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