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 config_sentence_transformers.json from Starkdoorstep12/entity-resolution-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 284 Bytes
-
https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://Starkdoorstep12/entity-resolution-biencoder/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/config_sentence_transformers.json
284 Bytes
| { | |
| "__version__": { | |
| "pytorch": "2.14.0+cu130", | |
| "sentence_transformers": "6.1.0", | |
| "transformers": "5.17.0" | |
| }, | |
| "default_prompt_name": null, | |
| "model_type": "SentenceTransformer", | |
| "prompts": { | |
| "document": "", | |
| "query": "" | |
| }, | |
| "similarity_fn_name": "cosine" | |
| } |