Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
dense
Generated from Trainer
dataset_size:2108
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use obe-ai-system/sbert-obe-csematch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use obe-ai-system/sbert-obe-csematch with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("obe-ai-system/sbert-obe-csematch") sentences = [ "Demonstrate what is Curse of Dimensionality?", "Enhance the learning parameters to achieve maximum performance.", "Enhance the learning parameters to achieve maximum performance.", "Demonstrate an understanding of propositional and predicate logic." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Welcome to the community
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