Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:3012496
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use tomaarsen/ModernBERT-base-gooaq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use tomaarsen/ModernBERT-base-gooaq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("tomaarsen/ModernBERT-base-gooaq") sentences = [ "how much is a car title transfer in minnesota?", "This complex is a larger molecule than the original crystal violet stain and iodine and is insoluble in water. ... Conversely, the the outer membrane of Gram negative bacteria is degraded and the thinner peptidoglycan layer of Gram negative cells is unable to retain the crystal violet-iodine complex and the color is lost.", "Get insurance on the car and provide proof. Bring this information (including the title) to the Minnesota DVS office, as well as $10 for the filing fee and $7.25 for the titling fee. There is also a $10 transfer tax, as well as a 6.5% sales tax on the purchase price.", "One of the risks of DNP is that it accelerates the metabolism to a dangerously fast level. Our metabolic system operates at the rate it does for a reason – it is safe. Speeding up the metabolism may help burn off fat, but it can also trigger a number of potentially dangerous side effects, such as: fever." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Inference
- Notebooks
- Google Colab
- Kaggle
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