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_maxPooling_mistranslationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use RomainDarous/large_directFourEpoch_maxPooling_mistranslationModel with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RomainDarous/large_directFourEpoch_maxPooling_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
Download sentence_bert_config.json from RomainDarous/large_directFourEpoch_maxPooling_mistranslationModel: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/RomainDarous/large_directFourEpoch_maxPooling_mistranslationModel/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://RomainDarous/large_directFourEpoch_maxPooling_mistranslationModel/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/RomainDarous/large_directFourEpoch_maxPooling_mistranslationModel/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 128, | |
| "do_lower_case": false | |
| } |