Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:13284
loss:MultipleNegativesRankingLoss
domain:agri-food
task:ontology-alignment
Eval Results (legacy)
text-embeddings-inference
Instructions to use P2M2/Ows2-MiniLM_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use P2M2/Ows2-MiniLM_1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("P2M2/Ows2-MiniLM_1") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
File size: 594 Bytes
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