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RomainDarous
/
large_directTwoEpoch_meanPooling_mistranslationModel

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:4460010
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use RomainDarous/large_directTwoEpoch_meanPooling_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use RomainDarous/large_directTwoEpoch_meanPooling_mistranslationModel with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("RomainDarous/large_directTwoEpoch_meanPooling_mistranslationModel")
    
    sentences = [
        "Malformed target specific variable definition",
        "Hedefe özgü değişken tanımı bozuk",
        "Kan alle data in die gids lees",
        "слава Украине! героям слава!"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
large_directTwoEpoch_meanPooling_mistranslationModel / 1_Pooling
296 Bytes
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  • 1 contributor
History: 1 commit
RomainDarous's picture
RomainDarous
Add new SentenceTransformer model
3b9be23 verified about 1 year ago
  • config.json
    296 Bytes
    Add new SentenceTransformer model about 1 year ago