Sentence Similarity
sentence-transformers
PyTorch
English
deberta-v2
feature-extraction
Generated from Trainer
dataset_size:99470
loss:CachedGISTEmbedLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use bobox/DeBERTa2-0.9B-ST-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use bobox/DeBERTa2-0.9B-ST-v2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("bobox/DeBERTa2-0.9B-ST-v2") sentences = [ "what is the wisconsin idea", "The Netherlands Time is in the Central European Time Zone . Central European Standard Time ( CET ) is 1 hours ahead of Greenwich Mean Time ( GMT+1 ). Like most states in Europe, Summer (Daylight-Saving) Time is observed in The Netherlands Time, where the time is shifted forward by 1 hour; 2 hours ahead of Greenwich Mean Time ( GMT+2 ). After the Summer months the time in The Netherlands Time is shifted back by 1 hour to Central European Time (CET) or ( GMT+1 )", "Unless stated otherwise, these amounts are the total that is recommended for your dog over a 24 hour period. Most adult dogs should eat two meals a day (puppies often require three or more feedings), so youâll need to divide the amount in the table by the number of meals you are offering.", "McCarthy's book, The Wisconsin Idea, published in 1912, describes the major problems facing the country, some of the Progressive reforms already passed, and his guiding vision for the future. Library-Archives." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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