Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:100000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use SikioN/wikidata-e5-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use SikioN/wikidata-e5-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SikioN/wikidata-e5-finetuned") sentences = [ "query: 1960: Jim Baxter member of sports team", "passage: Scotland national football team", "passage: Fulkerson Prize", "passage: 1922" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77dc3d4fce0d076c99c0646439de3c9ecd4de828e88bd2425b832cb769a45340
- Size of remote file:
- 17.1 MB
- SHA256:
- ef04f2b385d1514f500e779207ace0f53e30895ce37563179e29f4022d28ca38
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