Sentence Similarity
sentence-transformers
Safetensors
English
mpnet
feature-extraction
Generated from Trainer
dataset_size:314315
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use mrunali496/mpnet-base-all-nli-pair with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use mrunali496/mpnet-base-all-nli-pair with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("mrunali496/mpnet-base-all-nli-pair") sentences = [ "A person dressed in red and black outside a cracked wall.", "A person in red and black near a wall.", "Two women are in a car with a man.", "a baby cries while getting their diaper changed" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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