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_maxPooling_stsModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/large_directOneEpoch_maxPooling_stsModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/large_directOneEpoch_maxPooling_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
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_MultiHeadGeneralizedPooling", | |
| "type": "sentence_generalized_pooling.multihead_generalized_pooling.MultiHeadGeneralizedPooling" | |
| } | |
| ] |