Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:4314846
loss:CachedMultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use hotchpotch/ModernBERT-embedding-CMNRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hotchpotch/ModernBERT-embedding-CMNRL with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hotchpotch/ModernBERT-embedding-CMNRL") sentences = [ "what is grade 7 gcse equivalent to?", "Unlike the Google Home Mini (First Gen), the Nest Mini (Second Gen) can be used to actually enjoy music in every room of the house. While the Google Home Mini (First Gen) is a decent way to get music in every room of your home for cheap, the sound quality that comes from the speaker reflects the price of the product.", "In general, a grade 7-9 is roughly equivalent to A-A* under the old system, while a grade 4 and above is roughly equivalent to a C and above. Fewer students will receive a grade 9 than would have received an A* under the old grading system.", "['Pulling at a wet or dirty diaper.', 'Hiding to pee or poop.', \"Interest in others' use of the potty, or copying their behavior.\", 'Having a dry diaper for a longer-than-usual time.', 'Awakening dry from a nap.', \"Telling you that they're about to go, are going or have just gone in their diaper.\"]" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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