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
- Xet hash:
- 4f24d9bb7b072304b19adc3419b9fdca5c0c5c40b3e31aecb60d97740903a753
- Size of remote file:
- 66.4 MB
- SHA256:
- ed3741f068bcec9b34e9d299dac8ccca5c55d5df5f5cdb4bc6f39113214c4310
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