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_directTwoEpoch_additivePooling_noisedInit_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_mistranslationModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/large_directTwoEpoch_additivePooling_noisedInit_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_additivePooling_noisedInit_mistranslationModel / 1_MultiHeadGeneralizedPooling /multihead_pooling_weights.pt
- Xet hash:
- 2549ffdc07296a1d2a835d4104cbc2834e72b73c80cd8128903cbbce93568e21
- Size of remote file:
- 4.73 MB
- SHA256:
- 03af2601dce9852325a65a2b74c3154e235936a827d249262f12cb20e5403fa5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.