Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:4460010
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RomainDarous/large_directFourEpoch_additivePooling_randomInit_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/large_directFourEpoch_additivePooling_randomInit_mistranslationModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/large_directFourEpoch_additivePooling_randomInit_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_directFourEpoch_additivePooling_randomInit_mistranslationModel / 1_MultiHeadGeneralizedPooling /multihead_pooling_weights.pt
- Xet hash:
- 90dd2b13a64112c1906be958d247ebe391d636cbdcbe4247cee6c2140ed64bc2
- Size of remote file:
- 4.73 MB
- SHA256:
- 7d9188a29c5778d5fa7bd98f536bda726530003ba0181cc51b4f0eb589501271
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