Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:5749
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use ritulk/MPNET_finetuned_on_stsb_multi_mt_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ritulk/MPNET_finetuned_on_stsb_multi_mt_dataset with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ritulk/MPNET_finetuned_on_stsb_multi_mt_dataset") sentences = [ "Der Mann hat über die Internetkamera mit einem Mädchen gesprochen.", "Eine Gruppe älterer Menschen posiert um einen Esstisch.", "Ein Teenager spricht über eine Webcam mit einem Mädchen.", "Mindestlohngesetze schaden den am wenigsten Qualifizierten, den am wenigsten Produktiven am meisten." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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