Sentence Similarity
sentence-transformers
English
sklearn-random-forest
feature-extraction
education
course-outcomes
program-outcomes
co-po-mapping
Instructions to use Jrine/co-po with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Jrine/co-po with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Jrine/co-po") 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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license: MIT
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[🏠 Homepage](https://huggingface.co/Jrine/co-po) • [📊 Model Files](https://huggingface.co/Jrine/co-po/tree/main) • [💬 Discussions](https://huggingface.co/Jrine/co-po/discussions)
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[---
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language:
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license: MIT
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[🏠 Homepage](https://huggingface.co/Jrine/co-po) • [📊 Model Files](https://huggingface.co/Jrine/co-po/tree/main) • [💬 Discussions](https://huggingface.co/Jrine/co-po/discussions)
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</div>](https://huggingface.co/Jrine/co-po/blob/main/Readme.md)
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