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Cheselle
/
finetuned-arctic-sentence

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:600
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Cheselle/finetuned-arctic-sentence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Cheselle/finetuned-arctic-sentence with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Cheselle/finetuned-arctic-sentence")
    
    sentences = [
        "How can organizations tailor their measurement of GAI risks based on specific characteristics?",
        "3 \nthe abuse, misuse, and unsafe repurposing by humans (adversarial or not), and others result \nfrom interactions between a human and an AI system.  \n• \nTime scale: GAI risks may materialize abruptly or across extended periods. Examples include \nimmediate (and/or prolonged) emotional harm and potential risks to physical safety due to the \ndistribution of harmful deepfake images, or the long-term effect of disinformation on societal \ntrust in public institutions.",
        "12 \nCSAM. Even when trained on “clean” data, increasingly capable GAI models can synthesize or produce \nsynthetic NCII and CSAM. Websites, mobile apps, and custom-built models that generate synthetic NCII \nhave moved from niche internet forums to mainstream, automated, and scaled online businesses.  \nTrustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy Enhanced \n2.12. \nValue Chain and Component Integration",
        "case context. \nOrganizations may choose to tailor how they measure GAI risks based on these characteristics. They may \nadditionally wish to allocate risk management resources relative to the severity and likelihood of \nnegative impacts, including where and how these risks manifest, and their direct and material impacts \nharms in the context of GAI use. Mitigations for model or system level risks may differ from mitigations \nfor use-case or ecosystem level risks."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
finetuned-arctic-sentence
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Cheselle's picture
Cheselle
Add new SentenceTransformer model.
baa7639 verified over 1 year ago
  • 1_Pooling
    Add new SentenceTransformer model. over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    28.1 kB
    Add new SentenceTransformer model. over 1 year ago
  • config.json
    666 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • config_sentence_transformers.json
    277 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • model.safetensors
    438 MB
    xet
    Add new SentenceTransformer model. over 1 year ago
  • modules.json
    349 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • sentence_bert_config.json
    53 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • special_tokens_map.json
    695 Bytes
    Add new SentenceTransformer model. over 1 year ago
  • tokenizer.json
    712 kB
    Add new SentenceTransformer model. over 1 year ago
  • tokenizer_config.json
    1.38 kB
    Add new SentenceTransformer model. over 1 year ago
  • vocab.txt
    232 kB
    Add new SentenceTransformer model. over 1 year ago