Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
Generated from Trainer
dataset_size:662
loss:TripletLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use sdiazlor/modernbert-embed-base-biencoder-human-rights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use sdiazlor/modernbert-embed-base-biencoder-human-rights with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sdiazlor/modernbert-embed-base-biencoder-human-rights") sentences = [ "into (ETS No. 55), which entered\n\ninto\n\nThe current state of signatures and ratifications of the Convention and its Protocols as well as the complete list of declarations and reservations are available at www.conventions.coe.int.\n\nOnly the English and French versions of the Convention are authentic.\n\nEuropean Court of Human Rights\n\nCouncil of Europe\n\n67075 Strasbourg cedex\n\nFrance\n\nwww.echr.coe.int\n\nContents", "Can you provide the current state of signatures and ratifications of the Convention and its Protocols as well as the complete list of declarations and reservations which are available at www.conventions.coe.int?", "What is the binding force of a judgment in a court case?", "The current state of signatures and ratifications of the OECD and its Conventions as well as the complete list of declarations and reservations are available at www.oecd.org." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
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