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 eval/Information-Retrieval_evaluation_biencoder-val_results.csv from Starkdoorstep12/entity-resolution-biencoder: direct link, hf CLI and curl.
- Browser
- Download file 609 Bytes
-
https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/eval/Information-Retrieval_evaluation_biencoder-val_results.csv
- Command line
-
hf download hf://Starkdoorstep12/entity-resolution-biencoder/eval/Information-Retrieval_evaluation_biencoder-val_results.csv
-
curl -L -o Information-Retrieval_evaluation_biencoder-val_results.csv https://huggingface.co/Starkdoorstep12/entity-resolution-biencoder/resolve/main/eval/Information-Retrieval_evaluation_biencoder-val_results.csv
609 Bytes
| epoch,steps,cosine-Accuracy@1,cosine-Accuracy@5,cosine-Accuracy@10,cosine-Accuracy@30,cosine-Precision@1,cosine-Recall@1,cosine-Precision@3,cosine-Recall@3,cosine-Precision@5,cosine-Recall@5,cosine-Precision@10,cosine-Recall@10,cosine-MRR@10,cosine-NDCG@10,cosine-MAP@100 | |
| 1.0,9748,0.929,1.0,1.0,1.0,0.929,0.929,0.33333333333333326,1.0,0.20000000000000004,1.0,0.10000000000000002,1.0,0.9638333333333332,0.9732722934892877,0.9638333333333332 | |
| 2.0,19496,0.929,1.0,1.0,1.0,0.929,0.929,0.33333333333333326,1.0,0.20000000000000004,1.0,0.10000000000000002,1.0,0.9638333333333332,0.9732722934892877,0.9638333333333332 | |