Feature Extraction
sentence-transformers
Safetensors
English
Portuguese
lexical_embedding
custom_code
Instructions to use cnmoro/LexicalEmbed-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use cnmoro/LexicalEmbed-Base with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("cnmoro/LexicalEmbed-Base", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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Concept:
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"Some text" will be similar to "Sm txt"
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"King" will *not* be similar to "Queen" or "Royalty"
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"Dog" will *not* be similar to "Animal"
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"Doge" will be similar to "Dog"
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Concept:
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"Some text" will be similar to "Sm txt"
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"King" will **not** be similar to "Queen" or "Royalty"
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"Dog" will **not** be similar to "Animal"
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"Doge" will be similar to "Dog"
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