Sentence Similarity
sentence-transformers
Safetensors
Spanish
bert
feature-extraction
bne
entity-linking
spanish
cultural-heritage
Eval Results (legacy)
text-embeddings-inference
Instructions to use hsilvosa/bne-biencoder-entity-linker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hsilvosa/bne-biencoder-entity-linker with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hsilvosa/bne-biencoder-entity-linker") sentences = [ "Esa es una persona feliz", "Ese es un perro feliz", "Esa es una persona muy feliz", "Hoy es un día soleado" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hsilvosa/bne-biencoder-entity-linker: direct link, hf CLI and curl.
- Browser
- Download file 730 kB
-
https://huggingface.co/hsilvosa/bne-biencoder-entity-linker/resolve/main/tokenizer.json
- Command line
-
hf download hf://hsilvosa/bne-biencoder-entity-linker/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hsilvosa/bne-biencoder-entity-linker/resolve/main/tokenizer.json
730 kB
File too large to display, you can check the raw version instead.