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
File size: 335 Bytes
cff9a03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"architectures": [
"LexicalHFModel"
],
"auto_map": {
"AutoConfig": "lexical_model.LexicalConfig",
"AutoModel": "lexical_model.LexicalHFModel"
},
"embed_dim": 2048,
"model_type": "lexical_embedding",
"padding_idx": 8100,
"torch_dtype": "float32",
"transformers_version": "4.46.3",
"vocab_size": 8101
}
|