Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:221599363
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use thebajajra/RexBERT-large-embed-pf-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use thebajajra/RexBERT-large-embed-pf-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("thebajajra/RexBERT-large-embed-pf-v0.1") sentences = [ "I found out that this novel was based on real ...", "I found out that this novel was based on real people only by reading the afterword. This is a tremendously important piece of information about the book.", "I recently got a mbp 16 and although I’m very impressed by the speakers I still wanted to purchase a set of external speakers for the desk setup. The thing is since these are so good I don’t even know at which price point I should be shopping to get something better. \n\nThe other day a youtuber I watch said that he has been using the mbp 16 speakers instead of his $200 speakers because he doesn’t feel the need to anymore.\n\nSo, is a pair of $60 speakers going to be better or do I need to go higher in price to really hear a difference?", "Larry A Winters is a real good story teller. His use and knowledge of Jessie Black as the heroine indicates a familiarity that makes the reader wonder if Larry and Jessie are one and the same. A real page turner but not quite in the cant put it down stage." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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