Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
dense
Generated from Trainer
dataset_size:1042218
loss:CachedMultipleNegativesRankingLoss
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use chirag1701/resolve72-biencoder-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chirag1701/resolve72-biencoder-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chirag1701/resolve72-biencoder-v1") sentences = [ "query: ironclad | 221 david street, burleson, tx", "query: red bike shop | ", "query: ironclad | 221 david st, burleson, texas", "query: g0lden massage | sudbury way, null, carmichael, ca" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download 2_Normalize/config.json from chirag1701/resolve72-biencoder-v1: direct link, hf CLI and curl.
- Browser
- Download file 97 Bytes
-
https://huggingface.co/chirag1701/resolve72-biencoder-v1/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://chirag1701/resolve72-biencoder-v1/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/chirag1701/resolve72-biencoder-v1/resolve/main/2_Normalize/config.json
97 Bytes
| { | |
| "module_input_name": "sentence_embedding", | |
| "module_output_name": "sentence_embedding" | |
| } |