Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

ldldld
/
snowflake-arctic-embed-m-finetuned

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 ldldld/snowflake-arctic-embed-m-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use ldldld/snowflake-arctic-embed-m-finetuned with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("ldldld/snowflake-arctic-embed-m-finetuned")
    
    sentences = [
        "What is the purpose of the Artificial Intelligence Ethics for the Intelligence Community as mentioned in the context?",
        "You should be able to opt out, where appropriate, and \nhave access to a person who can quickly consider and \nremedy problems you encounter. You should be able to opt \nout from automated systems in favor of a human alternative, where \nappropriate. Appropriateness should be determined based on rea­\nsonable expectations in a given context and with a focus on ensuring \nbroad accessibility and protecting the public from especially harm­\nful impacts. In some cases, a human or other alternative may be re­\nquired by law. You should have access to timely human consider­\nation and remedy by a fallback and escalation process if an automat­\ned system fails, it produces an error, or you would like to appeal or \ncontest its impacts on you. Human consideration and fallback \nshould be accessible, equitable, effective, maintained, accompanied \nby appropriate operator training, and should not impose an unrea­\nsonable burden on the public. Automated systems with an intended",
        "points to numerous examples of effective and proactive stakeholder engagement, including the Community-\nBased Participatory Research Program developed by the National Institutes of Health and the participatory \ntechnology assessments developed by the National Oceanic and Atmospheric Administration.18\nThe National Institute of Standards and Technology (NIST) is developing a risk \nmanagement framework to better manage risks posed to individuals, organizations, and \nsociety by AI.19 The NIST AI Risk Management Framework, as mandated by Congress, is intended for \nvoluntary use to help incorporate trustworthiness considerations into the design, development, use, and \nevaluation of AI products, services, and systems. The NIST framework is being developed through a consensus-\ndriven, open, transparent, and collaborative process that includes workshops and other opportunities to provide \ninput. The NIST framework aims to foster the development of innovative approaches to address",
        "of Artificial Intelligence Ethics for the Intelligence Community to guide personnel on whether and how to \ndevelop and use AI in furtherance of the IC's mission, as well as an AI Ethics Framework to help implement \nthese principles.22\nThe National Science Foundation (NSF) funds extensive research to help foster the \ndevelopment of automated systems that adhere to and advance their safety, security and \neffectiveness. Multiple NSF programs support research that directly addresses many of these principles: \nthe National AI Research Institutes23 support research on all aspects of safe, trustworthy, fair, and explainable \nAI algorithms and systems; the Cyber Physical Systems24 program supports research on developing safe \nautonomous and cyber physical systems with AI components; the Secure and Trustworthy Cyberspace25 \nprogram supports research on cybersecurity and privacy enhancing technologies in automated systems; the"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
snowflake-arctic-embed-m-finetuned
437 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
ldldld's picture
ldldld
Add new SentenceTransformer model.
086cd7c 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
    37.3 kB
    Add new SentenceTransformer model. over 1 year ago
  • config.json
    675 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
    436 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