Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:47610
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use SIRIS-Lab/affilgood-dense-retriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SIRIS-Lab/affilgood-dense-retriever with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SIRIS-Lab/affilgood-dense-retriever") sentences = [ "[MENTION] Gustavus And Louise Pfeiffer Research Foundation [CITY] Bangor [COUNTRY] United States", "[MENTION] Gustavus And Louise Pfeiffer Research Foundation [CITY] Bangor [COUNTRY] United States", "[MENTION] Fifth Tianjin Central Hospital [CITY] Tianjin [COUNTRY] China", "[MENTION] Purdue Research Foundation [ACRONYM] PRF [CITY] West Lafayette [COUNTRY] United States" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SIRIS-Lab/affilgood-dense-retriever: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/SIRIS-Lab/affilgood-dense-retriever/resolve/main/tokenizer.json
- Command line
-
hf download hf://SIRIS-Lab/affilgood-dense-retriever/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/SIRIS-Lab/affilgood-dense-retriever/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- e418814e67cd5f4160399dd26926437450a995b6f97ebf3d04cbd6a63faec850
- Size of remote file:
- 17.1 MB
- SHA256:
- 44bc27f1c2bc1189b4bda0a3c563b961c21609fbe8f02ca6a16992b626b33190
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