Feature Extraction
sentence-transformers
Safetensors
English
bert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:3011496
loss:SpladeLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/splade-cocondenser-gooaq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/splade-cocondenser-gooaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/splade-cocondenser-gooaq") sentences = [ "how much percent of alcohol is in scotch?", "Our 24-hour day comes from the ancient Egyptians who divided day-time into 10 hours they measured with devices such as shadow clocks, and added a twilight hour at the beginning and another one at the end of the day-time, says Lomb. \"Night-time was divided in 12 hours, based on the observations of stars.", "After distillation, a Scotch Whisky can be anywhere between 60-75% ABV, with American Whiskey rocketing right into the 90% region. Before being placed in casks, Scotch is usually diluted to around 63.5% ABV (68% for grain); welcome to the stage cask strength Whisky.", "Money For Nothing. In season four Dominic West, the ostensible star of the series, requested a reduced role so that he could spend more time with his family in London. On the show it was explained that Jimmy McNulty had taken a patrol job which required less strenuous work." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle