Sentence Similarity
sentence-transformers
Safetensors
distilbert
feature-extraction
Generated from Trainer
dataset_size:1327190
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RomainDarous/preTrained_meanPooling_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/preTrained_meanPooling_mistranslationModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/preTrained_meanPooling_mistranslationModel") sentences = [ "यहाँका केही धार्मिक सम्पदाहरू यस प्रकार रहेका छन्।", "A party works journalists from advertisements about a massive Himalayan post.", "Some religious affiliations here remain.", "In Spain, the strict opposition of Roman Catholic churches is found to have assumed a marriage similar to male beach wives." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2928140f8004b183591f1834f0ab062b21131bd2b329d32d886b220a43e74bb5
- Size of remote file:
- 539 MB
- SHA256:
- dbbc5f2d5511cb456059e2355c006214e670f7b2d9b3b879412e673e4aeab832
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