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
| { | |
| "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 | |
| } | |