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jet-taekyo
/
snowflake_finetuned_recursive

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:714
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use jet-taekyo/snowflake_finetuned_recursive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use jet-taekyo/snowflake_finetuned_recursive with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("jet-taekyo/snowflake_finetuned_recursive")
    
    sentences = [
        "What are some examples of data privacy issues mentioned in the context?",
        "on a principle of local control, such that those individuals closest to the data subject have more access while \nthose who are less proximate do not (e.g., a teacher has access to their students’ daily progress data while a \nsuperintendent does not). \nReporting. In addition to the reporting on data privacy (as listed above for non-sensitive data), entities devel-\noping technologies related to a sensitive domain and those collecting, using, storing, or sharing sensitive data \nshould, whenever appropriate, regularly provide public reports describing: any data security lapses or breaches \nthat resulted in sensitive data leaks; the number, type, and outcomes of ethical pre-reviews undertaken; a \ndescription of any data sold, shared, or made public, and how that data was assessed to determine it did not pres-\nent a sensitive data risk; and ongoing risk identification and management procedures, and any mitigation added",
        "DATA PRIVACY \nHOW THESE PRINCIPLES CAN MOVE INTO PRACTICE\nReal-life examples of how these principles can become reality, through laws, policies, and practical \ntechnical and sociotechnical approaches to protecting rights, opportunities, and access. \nThe Privacy Act of 1974 requires privacy protections for personal information in federal \nrecords systems, including limits on data retention, and also provides individuals a general \nright to access and correct their data. Among other things, the Privacy Act limits the storage of individual \ninformation in federal systems of records, illustrating the principle of limiting the scope of data retention. Under \nthe Privacy Act, federal agencies may only retain data about an individual that is “relevant and necessary” to \naccomplish an agency’s statutory purpose or to comply with an Executive Order of the President. The law allows",
        "DATA PRIVACY \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \n•\nAn insurer might collect data from a person's social media presence as part of deciding what life\ninsurance rates they should be offered.64\n•\nA data broker harvested large amounts of personal data and then suffered a breach, exposing hundreds of\nthousands of people to potential identity theft. 65\n•\nA local public housing authority installed a facial recognition system at the entrance to housing complexes to\nassist law enforcement with identifying individuals viewed via camera when police reports are filed, leading\nthe community, both those living in the housing complex and not, to have videos of them sent to the local\npolice department and made available for scanning by its facial recognition software.66\n•"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
snowflake_finetuned_recursive
439 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
jet-taekyo's picture
jet-taekyo
Add new SentenceTransformer model.
ef3de2b 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
    37.7 kB
    Add new SentenceTransformer model. over 1 year ago
  • config.json
    681 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