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 1_Pooling/config.json from Starkdoorstep12/entity-resolution-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 89 Bytes
-
https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://Starkdoorstep12/entity-resolution-biencoder/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/1_Pooling/config.json
89 Bytes
| { | |
| "embedding_dimension": 768, | |
| "pooling_mode": "cls", | |
| "include_prompt": true | |
| } |