Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:51741
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use RomainDarous/large_directOneEpoch_additivePooling_noisedInit_stsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/large_directOneEpoch_additivePooling_noisedInit_stsModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/large_directOneEpoch_additivePooling_noisedInit_stsModel") sentences = [ "Starsza para azjatycka pozuje z noworodkiem przy stole obiadowym.", "Koszykarz ma zamiar zdobyć punkty dla swojej drużyny.", "Grupa starszych osób pozuje wokół stołu w jadalni.", "Możliwe, że układ słoneczny taki jak nasz może istnieć poza galaktyką." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
large_directOneEpoch_additivePooling_noisedInit_stsModel / 1_MultiHeadGeneralizedPooling /multihead_pooling_weights.pt
- Xet hash:
- 279e90e36610c4720227f4c8256a8ebb858f5a0b096617c2bdb593d84f12a681
- Size of remote file:
- 4.73 MB
- SHA256:
- a5cf56211cb3299d68f79b6ab08b1c18693ada152f735ca95d37c846558a8c43
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