Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:623829
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use Starkdoorstep12/entity-resolution-biencoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Starkdoorstep12/entity-resolution-biencoder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Starkdoorstep12/entity-resolution-biencoder") sentences = [ "Uptown Pub! | 1589 Clover Lane, Wichita Falls, TX", "Moa LLC Services | 1964 341, VEEDERSBURG, IN", "Wong Peak-(One) | 2658 61, Vincennes, Indiana", "Uptown Pub! Trading | " ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Starkdoorstep12/entity-resolution-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/tokenizer.json
- Command line
-
hf download hf://Starkdoorstep12/entity-resolution-biencoder/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- dc706d753e9f1b0960c5766ad584e4ed96ae1720a88a2fde9b2974237e4b28b8
- Size of remote file:
- 13.6 MB
- SHA256:
- 92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
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