File size: 49,853 Bytes

be462dc

---
base_model: Snowflake/snowflake-arctic-embed-m
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:502
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How can the manipulation of prompts, known as "jailbreaking," lead
    to harmful recommendations from GAI systems?
  sentences:
  - "but this approach may still produce harmful recommendations in response to other\
    \ less-explicit, novel \nprompts (also relevant to CBRN Information or Capabilities,\
    \ Data Privacy, Information Security, and \nObscene, Degrading and/or Abusive\
    \ Content). Crafting such prompts deliberately is known as \n“jailbreaking,” or,\
    \ manipulating prompts to circumvent output controls. Limitations of GAI systems\
    \ can be \nharmful or dangerous in certain contexts. Studies have observed that\
    \ users may disclose mental health \nissues in conversations with chatbots – and\
    \ that users exhibit negative reactions to unhelpful responses \nfrom these chatbots\
    \ during situations of distress. \nThis risk encompasses diﬃculty controlling\
    \ creation of and public exposure to oﬀensive or hateful \nlanguage, and denigrating\
    \ or stereotypical content generated by AI. This kind of speech may contribute\
    \ \nto downstream harm such as fueling dangerous or violent behaviors. The spread\
    \ of denigrating or \nstereotypical content can also further exacerbate representational\
    \ harms (see Harmful Bias and \nHomogenization below).  \nTrustworthy AI Characteristics:\
    \ Safe, Secure and Resilient \n2.4. Data Privacy \nGAI systems raise several risks\
    \ to privacy. GAI system training requires large volumes of data, which in \n\
    some cases may include personal data. The use of personal data for GAI training\
    \ raises risks to widely"
  - "communities and using it to reinforce inequality. Various panelists suggested\
    \ that these harms could be \nmitigated by ensuring community input at the beginning\
    \ of the design process, providing ways to opt out of \nthese systems and use\
    \ associated human-driven mechanisms instead, ensuring timeliness of benefit payments,\
    \ \nand providing clear notice about the use of these systems and clear explanations\
    \ of how and what the \ntechnologies are doing. Some panelists suggested that\
    \ technology should be used to help people receive \nbenefits, e.g., by pushing\
    \ benefits to those in need and ensuring automated decision-making systems are\
    \ only \nused to provide a positive outcome; technology shouldn't be used to take\
    \ supports away from people who need \nthem. \nPanel 6: The Healthcare System.\
    \ This event explored current and emerging uses of technology in the \nhealthcare\
    \ system and consumer products related to health. \nWelcome:\n•\nAlondra Nelson,\
    \ Deputy Director for Science and Society, White House Office of Science and Technology\n\
    Policy\n•\nPatrick Gaspard, President and CEO, Center for American Progress\n\
    Moderator: Micky Tripathi, National Coordinator for Health Information Technology,\
    \ U.S Department of \nHealth and Human Services. \nPanelists: \n•\nMark Schneider,\
    \ Health Innovation Advisor, ChristianaCare\n•\nZiad Obermeyer, Blue Cross of\
    \ California Distinguished Associate Professor of Policy and Management,"
  - "have 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 \n\
    should 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 \nuse within sensitive domains, including,\
    \ but not limited to, criminal \njustice, employment, education, and health, should\
    \ additionally be \ntailored to the purpose, provide meaningful access for oversight,\
    \ \ninclude training for any people interacting with the system, and in\ncorporate\
    \ human consideration for adverse or high-risk decisions. \nReporting that includes\
    \ a description of these human governance \nprocesses and assessment of their\
    \ timeliness, accessibility, out"
- source_sentence: What are the potential consequences of model collapse in AI systems,
    particularly regarding output homogenization?
  sentences:
  - "President ordered the full Federal government to work to root out inequity, embed\
    \ fairness in decision-\nmaking processes, and affirmatively advance civil rights,\
    \ equal opportunity, and racial justice in America.1 The \nPresident has spoken\
    \ forcefully about the urgent challenges posed to democracy today and has regularly\
    \ called \non people of conscience to act to preserve civil rights—including the\
    \ right to privacy, which he has called “the \nbasis for so many more rights that\
    \ we have come to take for granted that are ingrained in the fabric of this \n\
    country.”2\nTo advance President Biden’s vision, the White House Office of Science\
    \ and Technology Policy has identified \nfive principles that should guide the\
    \ design, use, and deployment of automated systems to protect the American \n\
    public in the age of artificial intelligence. The Blueprint for an AI Bill of\
    \ Rights is a guide for a society that \nprotects all people from these threats—and\
    \ uses technologies in ways that reinforce our highest values. \nResponding to\
    \ the experiences of the American public, and informed by insights from researchers,\
    \ \ntechnologists, advocates, journalists, and policymakers, this framework is\
    \ accompanied by a technical \ncompanion—a handbook for anyone seeking to incorporate\
    \ these protections into policy and practice, including \ndetailed steps toward\
    \ actualizing these principles in the technological design process. These principles\
    \ help \nprovide guidance whenever automated systems can meaningfully impact the\
    \ public’s rights, opportunities,"
  - "Synopsis of Responses to OSTP’s Request for Information on the Use and Governance\
    \ of Biometric\nTechnologies in the Public and Private Sectors. Science and Technology\
    \ Policy Institute. Mar. 2022.\nhttps://www.ida.org/-/media/feature/publications/s/sy/synopsis-of-responses-to-request-for\n\
    information-on-the-use-and-governance-of-biometric-technologies/ida-document-d-33070.ashx\n\
    73\n \nNIST Trustworthy and Responsible AI  \nNIST AI 600-1 \nArtificial Intelligence\
    \ Risk Management \nFramework: Generative Artificial \nIntelligence Profile \n\
    \ \n \n \nThis publication is available free of charge from: \nhttps://doi.org/10.6028/NIST.AI.600-1\
    \ \n \n \n \n \n \n \n \n \n \n \n \n \n \n \nNIST Trustworthy and Responsible\
    \ AI  \nNIST AI 600-1 \nArtificial Intelligence Risk Management \nFramework: Generative\
    \ Artificial \nIntelligence Profile \n \n \n \nThis publication is available free\
    \ of charge from: \nhttps://doi.org/10.6028/NIST.AI.600-1 \n \nJuly 2024 \n \n\
    \ \n \n \nU.S. Department of Commerce"
  - "new model’s outputs. In addition to threatening the robustness of the model overall,\
    \ model collapse \ncould lead to homogenized outputs, including by amplifying\
    \ any homogenization from the model used to \ngenerate the synthetic training\
    \ data. \nTrustworthy AI Characteristics: Fair with Harmful Bias Managed, Valid\
    \ and Reliable \n2.7. Human-AI Conﬁguration \nGAI system use can involve varying\
    \ risks of misconﬁgurations and poor interactions between a system \nand a human\
    \ who is interacting with it. Humans bring their unique perspectives, experiences,\
    \ or domain-\nspeciﬁc expertise to interactions with AI systems but may not have\
    \ detailed knowledge of AI systems and \nhow they work. As a result, human experts\
    \ may be unnecessarily “averse” to GAI systems, and thus \ndeprive themselves\
    \ or others of GAI’s beneﬁcial uses.  \nConversely, due to the complexity and\
    \ increasing reliability of GAI technology, over time, humans may \nover-rely\
    \ on GAI systems or may unjustiﬁably perceive GAI content to be of higher quality\
    \ than that \nproduced by other sources. This phenomenon is an example of automation\
    \ bias, or excessive deference \nto automated systems. Automation bias can exacerbate\
    \ other risks of GAI, such as risks of confabulation \nor risks of bias or homogenization."
- source_sentence: How is sensitive data defined in relation to individual privacy
    and potential harm?
  sentences:
  - "recognized voluntary consensus standard for web content and other information\
    \ and communications \ntechnology. \nNIST has released Special Publication 1270,\
    \ Towards a Standard for Identifying and Managing Bias \nin Artificial Intelligence.59\
    \ The special publication: describes the stakes and challenges of bias in artificial\
    \ \nintelligence and provides examples of how and why it can chip away at public\
    \ trust; identifies three categories \nof bias in AI – systemic, statistical,\
    \ and human – and describes how and where they contribute to harms; and \ndescribes\
    \ three broad challenges for mitigating bias – datasets, testing and evaluation,\
    \ and human factors – and \nintroduces preliminary guidance for addressing them.\
    \ Throughout, the special publication takes a socio-\ntechnical perspective to\
    \ identifying and managing AI bias. \n29\nAlgorithmic \nDiscrimination \nProtections\
    \ \nYou should be protected from abusive data practices via built-in \nprotections\
    \ and you should have agency over how data about \nyou is used. You should be\
    \ protected from violations of privacy through \ndesign choices that ensure such\
    \ protections are included by default, including \nensuring that data collection\
    \ conforms to reasonable expectations and that \nonly data strictly necessary\
    \ for the specific context is collected. Designers, de\nvelopers, and deployers\
    \ of automated systems should seek your permission \nand respect your decisions\
    \ regarding collection, use, access, transfer, and de"
  - "of this framework. It describes the set of: civil rights, civil liberties, and\
    \ privacy, including freedom of speech, \nvoting, and protections from discrimination,\
    \ excessive punishment, unlawful surveillance, and violations of \nprivacy and\
    \ other freedoms in both public and private sector contexts; equal opportunities,\
    \ including equitable \naccess to education, housing, credit, employment, and\
    \ other programs; or, access to critical resources or \nservices, such as healthcare,\
    \ financial services, safety, social services, non-deceptive information about\
    \ goods \nand services, and government benefits. \n10\n \n \n \nApplying The Blueprint\
    \ for an AI Bill of Rights \nSENSITIVE DATA: Data and metadata are sensitive if\
    \ they pertain to an individual in a sensitive domain \n(defined below); are generated\
    \ by technologies used in a sensitive domain; can be used to infer data from a\
    \ \nsensitive domain or sensitive data about an individual (such as disability-related\
    \ data, genomic data, biometric \ndata, behavioral data, geolocation data, data\
    \ related to interaction with the criminal justice system, relationship \nhistory\
    \ and legal status such as custody and divorce information, and home, work, or\
    \ school environmental \ndata); or have the reasonable potential to be used in\
    \ ways that are likely to expose individuals to meaningful \nharm, such as a loss\
    \ of privacy or financial harm due to identity theft. Data and metadata generated\
    \ by or about"
  - "Generated explicit or obscene AI content may include highly realistic “deepfakes”\
    \ of real individuals, \nincluding children. The spread of this kind of material\
    \ can have downstream negative consequences: in \nthe context of CSAM, even if\
    \ the generated images do not resemble speciﬁc individuals, the prevalence \n\
    of such images can divert time and resources from eﬀorts to ﬁnd real-world victims.\
    \ Outside of CSAM, \nthe creation and spread of NCII disproportionately impacts\
    \ women and sexual minorities, and can have \nsubsequent negative consequences\
    \ including decline in overall mental health, substance abuse, and \neven suicidal\
    \ thoughts.  \nData used for training GAI models may unintentionally include CSAM\
    \ and NCII. A recent report noted \nthat several commonly used GAI training datasets\
    \ were found to contain hundreds of known images of \n \n12 \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 \nGAI value chains involve many third-party components\
    \ such as procured datasets, pre-trained models,"
- source_sentence: How might GAI facilitate access to CBRN weapons and relevant knowledge
    for malicious actors in the future?
  sentences:
  - "https://doi.org/10.6028/NIST.AI.600-1 \n \nJuly 2024 \n \n \n \n \nU.S. Department\
    \ of Commerce  \nGina M. Raimondo, Secretary \nNational Institute of Standards\
    \ and Technology  \nLaurie E. Locascio, NIST Director and Under Secretary of Commerce\
    \ for Standards and Technology  \n \n \n \n \nAbout AI at NIST: The National Institute\
    \ of Standards and Technology (NIST) develops measurements, \ntechnology, tools,\
    \ and standards to advance reliable, safe, transparent, explainable, privacy-enhanced,\
    \ \nand fair artiﬁcial intelligence (AI) so that its full commercial and societal\
    \ beneﬁts can be realized without \nharm to people or the planet. NIST, which\
    \ has conducted both fundamental and applied work on AI for \nmore than a decade,\
    \ is also helping to fulﬁll the 2023 Executive Order on Safe, Secure, and Trustworthy\
    \ \nAI. NIST established the U.S. AI Safety Institute and the companion AI Safety\
    \ Institute Consortium to \ncontinue the eﬀorts set in motion by the E.O. to build\
    \ the science necessary for safe, secure, and \ntrustworthy development and use\
    \ of AI. \nAcknowledgments: This report was accomplished with the many helpful\
    \ comments and contributions"
  - "the AI lifecycle; or other issues that diminish transparency or accountability\
    \ for downstream \nusers. \n2.1. CBRN Information or Capabilities \nIn the future,\
    \ GAI may enable malicious actors to more easily access CBRN weapons and/or relevant\
    \ \nknowledge, information, materials, tools, or technologies that could be misused\
    \ to assist in the design, \ndevelopment, production, or use of CBRN weapons or\
    \ other dangerous materials or agents. While \nrelevant biological and chemical\
    \ threat knowledge and information is often publicly accessible, LLMs \ncould\
    \ facilitate its analysis or synthesis, particularly by individuals without formal\
    \ scientiﬁc training or \nexpertise.  \nRecent research on this topic found that\
    \ LLM outputs regarding biological threat creation and attack \nplanning provided\
    \ minimal assistance beyond traditional search engine queries, suggesting that\
    \ state-of-\nthe-art LLMs at the time these studies were conducted do not substantially\
    \ increase the operational \nlikelihood of such an attack. The physical synthesis\
    \ development, production, and use of chemical or \nbiological agents will continue\
    \ to require both applicable expertise and supporting materials and \ninfrastructure.\
    \ The impact of GAI on chemical or biological agent misuse will depend on what\
    \ the key \nbarriers for malicious actors are (e.g., whether information access\
    \ is one such barrier), and how well GAI \ncan help actors address those barriers."
  - "played a central role in shaping the Blueprint for an AI Bill of Rights. The\
    \ core messages gleaned from these \ndiscussions include that AI has transformative\
    \ potential to improve Americans’ lives, and that preventing the \nharms of these\
    \ technologies is both necessary and achievable. The Appendix includes a full\
    \ list of public engage-\nments. \n4\n AI BILL OF RIGHTS\nFFECTIVE SYSTEMS\nineffective\
    \ systems. Automated systems should be \ncommunities, stakeholders, and domain\
    \ experts to identify \nSystems should undergo pre-deployment testing, risk \n\
    that demonstrate they are safe and effective based on \nincluding those beyond\
    \ the intended use, and adherence to \nprotective measures should include the\
    \ possibility of not \nAutomated systems should not be designed with an intent\
    \ \nreasonably foreseeable possibility of endangering your safety or the safety\
    \ of your community. They should \nstemming from unintended, yet foreseeable,\
    \ uses or \n \n \n \n \n  \n \n \nSECTION TITLE\nBLUEPRINT FOR AN\nSAFE AND E\
    \ \nYou should be protected from unsafe or \ndeveloped with consultation from\
    \ diverse \nconcerns, risks, and potential impacts of the system. \nidentification\
    \ and mitigation, and ongoing monitoring \ntheir intended use, mitigation of unsafe\
    \ outcomes \ndomain-specific standards. Outcomes of these \ndeploying the system\
    \ or removing a system from use. \nor"
- source_sentence: What are some key lessons learned from technological diffusion
    in urban planning that could inform the integration of AI technologies in communities?
  sentences:
  - "State University\n•\nCarl Holshouser, Senior Vice President for Operations and\
    \ Strategic Initiatives, TechNet\n•\nSurya Mattu, Senior Data Engineer and Investigative\
    \ Data Journalist, The Markup\n•\nMariah Montgomery, National Campaign Director,\
    \ Partnership for Working Families\n55\n \n \n \n \nAPPENDIX\nPanelists discussed\
    \ the benefits of AI-enabled systems and their potential to build better and more\
    \ \ninnovative infrastructure. They individually noted that while AI technologies\
    \ may be new, the process of \ntechnological diffusion is not, and that it was\
    \ critical to have thoughtful and responsible development and \nintegration of\
    \ technology within communities. Some panelists suggested that the integration\
    \ of technology \ncould benefit from examining how technological diffusion has\
    \ worked in the realm of urban planning: \nlessons learned from successes and\
    \ failures there include the importance of balancing ownership rights, use \n\
    rights, and community health, safety and welfare, as well ensuring better representation\
    \ of all voices, \nespecially those traditionally marginalized by technological\
    \ advances. Some panelists also raised the issue of \npower structures – providing\
    \ examples of how strong transparency requirements in smart city projects \nhelped\
    \ to reshape power and give more voice to those lacking the financial or political\
    \ power to effect change. \nIn discussion of technical and governance interventions\
    \ that that are needed to protect against the harms"
  - "any mechanism that allows the recipient to build the necessary understanding\
    \ and intuitions to achieve the \nstated purpose. Tailoring should be assessed\
    \ (e.g., via user experience research). \nTailored to the target of the explanation.\
    \ Explanations should be targeted to specific audiences and \nclearly state that\
    \ audience. An explanation provided to the subject of a decision might differ\
    \ from one provided \nto an advocate, or to a domain expert or decision maker.\
    \ Tailoring should be assessed (e.g., via user experience \nresearch). \n43\n\
    \ \n \n \n \n \n \nNOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED\
    \ SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint\
    \ for the development of additional \ntechnical standards and practices that are\
    \ tailored for particular sectors and contexts. \nTailored to the level of risk.\
    \ An assessment should be done to determine the level of risk of the auto\nmated\
    \ system. In settings where the consequences are high as determined by a risk\
    \ assessment, or extensive \noversight is expected (e.g., in criminal justice\
    \ or some public sector settings), explanatory mechanisms should \nbe built into\
    \ the system design so that the system’s full behavior can be explained in advance\
    \ (i.e., only fully \ntransparent models should be used), rather than as an after-the-decision\
    \ interpretation. In other settings, the"
  - "research on rigorous and reproducible methodologies for developing software systems\
    \ with legal and regulatory \ncompliance in mind. \nSome state legislatures have\
    \ placed strong transparency and validity requirements on \nthe use of pretrial\
    \ risk assessments. The use of algorithmic pretrial risk assessments has been\
    \ a \ncause of concern for civil rights groups.28 Idaho Code Section 19-1910,\
    \ enacted in 2019,29 requires that any \npretrial risk assessment, before use\
    \ in the state, first be \"shown to be free of bias against any class of \nindividuals\
    \ protected from discrimination by state or federal law\", that any locality using\
    \ a pretrial risk \nassessment must first formally validate the claim of its being\
    \ free of bias, that \"all documents, records, and \ninformation used to build\
    \ or validate the risk assessment shall be open to public inspection,\" and that\
    \ assertions \nof trade secrets cannot be used \"to quash discovery in a criminal\
    \ matter by a party to a criminal case.\" \n22\n \nALGORITHMIC DISCRIMINATION\
    \ Protections\nYou should not face discrimination by algorithms \nand systems\
    \ should be used and designed in an \nequitable \nway. \nAlgorithmic \ndiscrimination\
    \ \noccurs when \nautomated systems contribute to unjustified different treatment\
    \ or \nimpacts disfavoring people based on their race, color, ethnicity, \nsex"
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: Unknown
      type: unknown
    metrics:
    - type: cosine_accuracy@1
      value: 0.75
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.9
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.96
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.97
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.75
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.3
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.19199999999999995
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09699999999999998
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.75
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.9
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.96
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.97
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.8673712763276756
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.8336111111111113
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.8360959595959596
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.75
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.9
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.96
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.97
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.75
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.3
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.19199999999999995
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09699999999999998
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.75
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.9
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.96
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.97
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.8673712763276756
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.8336111111111113
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.8360959595959596
      name: Dot Map@100
---

# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m) <!-- at revision e2b128b9fa60c82b4585512b33e1544224ffff42 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Mdean77/finetuned_arctic")
# Run inference
sentences = [
    'What are some key lessons learned from technological diffusion in urban planning that could inform the integration of AI technologies in communities?',
    'State University\n•\nCarl Holshouser, Senior Vice President for Operations and Strategic Initiatives, TechNet\n•\nSurya Mattu, Senior Data Engineer and Investigative Data Journalist, The Markup\n•\nMariah Montgomery, National Campaign Director, Partnership for Working Families\n55\n \n \n \n \nAPPENDIX\nPanelists discussed the benefits of AI-enabled systems and their potential to build better and more \ninnovative infrastructure. They individually noted that while AI technologies may be new, the process of \ntechnological diffusion is not, and that it was critical to have thoughtful and responsible development and \nintegration of technology within communities. Some panelists suggested that the integration of technology \ncould benefit from examining how technological diffusion has worked in the realm of urban planning: \nlessons learned from successes and failures there include the importance of balancing ownership rights, use \nrights, and community health, safety and welfare, as well ensuring better representation of all voices, \nespecially those traditionally marginalized by technological advances. Some panelists also raised the issue of \npower structures – providing examples of how strong transparency requirements in smart city projects \nhelped to reshape power and give more voice to those lacking the financial or political power to effect change. \nIn discussion of technical and governance interventions that that are needed to protect against the harms',
    'any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the \nstated purpose. Tailoring should be assessed (e.g., via user experience research). \nTailored to the target of the explanation. Explanations should be targeted to specific audiences and \nclearly state that audience. An explanation provided to the subject of a decision might differ from one provided \nto an advocate, or to a domain expert or decision maker. Tailoring should be assessed (e.g., via user experience \nresearch). \n43\n \n \n \n \n \n \nNOTICE & \nEXPLANATION \nWHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS\nThe expectations for automated systems are meant to serve as a blueprint for the development of additional \ntechnical standards and practices that are tailored for particular sectors and contexts. \nTailored to the level of risk. An assessment should be done to determine the level of risk of the auto\xad\nmated system. In settings where the consequences are high as determined by a risk assessment, or extensive \noversight is expected (e.g., in criminal justice or some public sector settings), explanatory mechanisms should \nbe built into the system design so that the system’s full behavior can be explained in advance (i.e., only fully \ntransparent models should be used), rather than as an after-the-decision interpretation. In other settings, the',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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## Evaluation

### Metrics

#### Information Retrieval

* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.75       |
| cosine_accuracy@3   | 0.9        |
| cosine_accuracy@5   | 0.96       |
| cosine_accuracy@10  | 0.97       |
| cosine_precision@1  | 0.75       |
| cosine_precision@3  | 0.3        |
| cosine_precision@5  | 0.192      |
| cosine_precision@10 | 0.097      |
| cosine_recall@1     | 0.75       |
| cosine_recall@3     | 0.9        |
| cosine_recall@5     | 0.96       |
| cosine_recall@10    | 0.97       |
| cosine_ndcg@10      | 0.8674     |
| cosine_mrr@10       | 0.8336     |
| **cosine_map@100**  | **0.8361** |
| dot_accuracy@1      | 0.75       |
| dot_accuracy@3      | 0.9        |
| dot_accuracy@5      | 0.96       |
| dot_accuracy@10     | 0.97       |
| dot_precision@1     | 0.75       |
| dot_precision@3     | 0.3        |
| dot_precision@5     | 0.192      |
| dot_precision@10    | 0.097      |
| dot_recall@1        | 0.75       |
| dot_recall@3        | 0.9        |
| dot_recall@5        | 0.96       |
| dot_recall@10       | 0.97       |
| dot_ndcg@10         | 0.8674     |
| dot_mrr@10          | 0.8336     |
| dot_map@100         | 0.8361     |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 502 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 502 samples:
  |         | sentence_0                                                                        | sentence_1                                                                            |
  |:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
  | type    | string                                                                            | string                                                                                |
  | details | <ul><li>min: 2 tokens</li><li>mean: 21.89 tokens</li><li>max: 38 tokens</li></ul> | <ul><li>min: 158 tokens</li><li>mean: 263.58 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
  | sentence_0                                                                                                                                               | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What is the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?</code>         | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022 <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology <br>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national</code>                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>When was the Office of Science and Technology Policy established, and what is its primary function?</code>                                         | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022 <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br> <br>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology <br>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national</code>                                                                                                                                                                                                                                                                                                                                                                                    |
  | <code>What is the primary purpose of the Policy, Organization, and Priorities Act of 1976 as it relates to the Executive Office of the President?</code> | <code>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national <br>security, health, foreign relations, the environment, and the technological recovery and use of resources, among <br>other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of <br>Management and Budget (OMB) with an annual review and analysis of Federal research and development in <br>budgets, and serves as a source of scientific and technological analysis and judgment for the President with <br>respect to major policies, plans, and programs of the Federal Government. <br>Legal Disclaimer <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper <br>published by the White House Office of Science and Technology Policy. It is intended to support the <br>development of policies and practices that protect civil rights and promote democratic values in the building, <br>deployment, and governance of automated systems. <br>The Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It <br>does not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or <br>international instrument. It does not constitute binding guidance for the public or Federal agencies and</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: steps
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`: 
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin

</details>

### Training Logs
| Epoch  | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 1.0    | 26   | 0.7610         |
| 1.9231 | 50   | 0.8249         |
| 2.0    | 52   | 0.8317         |
| 3.0    | 78   | 0.8295         |
| 3.8462 | 100  | 0.8361         |


### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.1.1
- Transformers: 4.44.2
- PyTorch: 2.4.1
- Accelerate: 0.34.2
- Datasets: 3.0.0
- Tokenizers: 0.19.1

## Citation

### BibTeX

#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}
```

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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