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KaiserML
/
RhetoriBERT

Sentence Similarity
sentence-transformers
Safetensors
English
nomic_bert
feature-extraction
Generated from Trainer
dataset_size:35934
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
custom_code
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KaiserML/RhetoriBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use KaiserML/RhetoriBERT with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("KaiserML/RhetoriBERT", trust_remote_code=True)
    
    sentences = [
        "Stating purpose of the current research with reference to gaps or issues in the literature",
        "During the 15-year study, 10% of the osseointegrated implants in the edentulous jaw showed signs of peri-implantitis, leading to their failure.",
        "This paper provides an in-depth exploration of the qualitative case study methodology, addressing the lack of comprehensive guidance for novice researchers in this area.",
        "As a novice researcher in management science, I have been drawn to the qualitative case study methodology due to its ability to provide rich, in-depth insights into complex real-world situations."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
RhetoriBERT
548 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 5 commits
Corran's picture
Corran
Update README.md
ff50a02 verified 12 months ago
  • 1_Pooling
    Add new SentenceTransformer model over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    30.7 kB
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  • config.json
    1.7 kB
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  • config_sentence_transformers.json
    205 Bytes
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  • configuration_hf_nomic_bert.py
    1.96 kB
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  • model.safetensors
    547 MB
    xet
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  • modules.json
    229 Bytes
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  • sentence_bert_config.json
    54 Bytes
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  • special_tokens_map.json
    695 Bytes
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    1.41 kB
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  • vocab.txt
    232 kB
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