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 checkpoint-19496/optimizer.pt from Starkdoorstep12/entity-resolution-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 3.77 GB
-
https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/checkpoint-19496/optimizer.pt
- Command line
-
hf download hf://Starkdoorstep12/entity-resolution-biencoder/checkpoint-19496/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/checkpoint-19496/optimizer.pt
3.77 GB
- Xet hash:
- 60242a8cd1bfecd5e034440665f5734e397d5eb1812a10b450fff2b9f382bb79
- Size of remote file:
- 3.77 GB
- SHA256:
- cb09b6e752ace5a049d758bf2ce82f0a5e94c9c6bd47a1e9536eab835fb8daf4
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