README.md

metadata

base_model: sentence-transformers/all-MiniLM-L6-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:128
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: >-
      What is the title of the publication released by NIST in July 2024
      regarding artificial intelligence?
    sentences:
      - |-
        NIST Trustworthy and Responsible AI  
        NIST AI 600-1 
        Artificial Intelligence Risk Management 
        Framework: Generative Artificial 
        Intelligence Profile 
         
         
         
        This publication is available free of charge from: 
        https://doi.org/10.6028/NIST.AI.600-1
      - >-
        NIST Trustworthy and Responsible AI  

        NIST AI 600-1 

        Artificial Intelligence Risk Management 

        Framework: Generative Artificial 

        Intelligence Profile 
         
         
         
        This publication is available free of charge from: 

        https://doi.org/10.6028/NIST.AI.600-1 
         
        July 2024 
         
         
         
         
        U.S. Department of Commerce  

        Gina M. Raimondo, Secretary 

        National Institute of Standards and Technology  

        Laurie E. Locascio, NIST Director and Under Secretary of Commerce for
        Standards and Technology
      - >-
        37 

        MS-2.11-005 

        Assess the proportion of synthetic to non-synthetic training data and
        verify 

        training data is not overly homogenous or GAI-produced to mitigate
        concerns of 

        model collapse. 

        Harmful Bias and Homogenization 

        AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals
        and Communities, Domain Experts, End-Users, 

        Operation and Monitoring, TEVV 
         
        MEASURE 2.12: Environmental impact and sustainability of AI model
        training and management activities – as identified in the MAP 

        function – are assessed and documented. 

        Action ID 

        Suggested Action 

        GAI Risks 

        MS-2.12-001 Assess safety to physical environments when deploying GAI
        systems. 

        Dangerous, Violent, or Hateful 

        Content 

        MS-2.12-002 Document anticipated environmental impacts of model
        development, 

        maintenance, and deployment in product design decisions. 

        Environmental 

        MS-2.12-003 

        Measure or estimate environmental impacts (e.g., energy and water 

        consumption) for training, fine tuning, and deploying models: Verify
        tradeoffs 

        between resources used at inference time versus additional resources
        required 

        at training time. 

        Environmental 

        MS-2.12-004 Verify effectiveness of carbon capture or offset programs for
        GAI training and 

        applications, and address green-washing concerns. 

        Environmental 

        AI Actor Tasks: AI Deployment, AI Impact Assessment, Domain Experts,
        Operation and Monitoring, TEVV
  - source_sentence: >-
      What are the four primary considerations relevant to Generative AI (GAI)
      that the GAI Public Working Group focused on?
    sentences:
      - >-
        23 

        MP-1.1-002 

        Determine and document the expected and acceptable GAI system context
        of 

        use in collaboration with socio-cultural and other domain experts, by
        assessing: 

        Assumptions and limitations; Direct value to the organization; Intended 

        operational environment and observed usage patterns; Potential positive
        and 

        negative impacts to individuals, public safety, groups, communities, 

        organizations, democratic institutions, and the physical environment;
        Social 

        norms and expectations. 

        Harmful Bias and Homogenization 

        MP-1.1-003 

        Document risk measurement plans to address identified risks. Plans may 

        include, as applicable: Individual and group cognitive biases (e.g.,
        confirmation 

        bias, funding bias, groupthink) for AI Actors involved in the design, 

        implementation, and use of GAI systems; Known past GAI system incidents
        and 

        failure modes; In-context use and foreseeable misuse, abuse, and
        off-label use; 

        Over reliance on quantitative metrics and methodologies without
        sufficient 

        awareness of their limitations in the context(s) of use; Standard
        measurement 

        and structured human feedback approaches; Anticipated human-AI 

        configurations. 

        Human-AI Configuration; Harmful 

        Bias and Homogenization; 

        Dangerous, Violent, or Hateful 

        Content 

        MP-1.1-004 

        Identify and document foreseeable illegal uses or applications of the
        GAI system 

        that surpass organizational risk tolerances. 

        CBRN Information or Capabilities; 

        Dangerous, Violent, or Hateful 

        Content; Obscene, Degrading, 

        and/or Abusive Content 

        AI Actor Tasks: AI Deployment 
         
        MAP 1.2: Interdisciplinary AI Actors, competencies, skills, and
        capacities for establishing context reflect demographic diversity and 

        broad domain and user experience expertise, and their participation is
        documented. Opportunities for interdisciplinary 

        collaboration are prioritized. 

        Action ID 

        Suggested Action 

        GAI Risks 

        MP-1.2-001 

        Establish and empower interdisciplinary teams that reflect a wide range
        of 

        capabilities, competencies, demographic groups, domain expertise,
        educational 

        backgrounds, lived experiences, professions, and skills across the
        enterprise to 

        inform and conduct risk measurement and management functions. 

        Human-AI Configuration; Harmful 

        Bias and Homogenization 

        MP-1.2-002 

        Verify that data or benchmarks used in risk measurement, and users, 

        participants, or subjects involved in structured GAI public feedback
        exercises 

        are representative of diverse in-context user populations. 

        Human-AI Configuration; Harmful 

        Bias and Homogenization 

        AI Actor Tasks: AI Deployment
      - >-
        2 

        This work was informed by public feedback and consultations with diverse
        stakeholder groups as part of NIST’s 

        Generative AI Public Working Group (GAI PWG). The GAI PWG was an open,
        transparent, and collaborative 

        process, facilitated via a virtual workspace, to obtain multistakeholder
        input on GAI risk management and to 

        inform NIST’s approach. 

        The focus of the GAI PWG was limited to four primary considerations
        relevant to GAI: Governance, Content 

        Provenance, Pre-deployment Testing, and Incident Disclosure (further
        described in Appendix A). As such, the 

        suggested actions in this document primarily address these
        considerations. 

        Future revisions of this profile will include additional AI RMF
        subcategories, risks, and suggested actions based 

        on additional considerations of GAI as the space evolves and empirical
        evidence indicates additional risks. A 

        glossary of terms pertinent to GAI risk management will be developed and
        hosted on NIST’s Trustworthy & 

        Responsible AI Resource Center (AIRC), and added to The Language of
        Trustworthy AI: An In-Depth Glossary of 

        Terms. 

        This document was also informed by public comments and consultations
        from several Requests for Information. 
         
        2. 

        Overview of Risks Unique to or Exacerbated by GAI 

        In the context of the AI RMF, risk refers to the composite measure of an
        event’s probability (or 

        likelihood) of occurring and the magnitude or degree of the consequences
        of the corresponding event. 

        Some risks can be assessed as likely to materialize in a given context,
        particularly those that have been 

        empirically demonstrated in similar contexts. Other risks may be
        unlikely to materialize in a given 

        context, or may be more speculative and therefore uncertain. 

        AI risks can differ from or intensify traditional software risks.
        Likewise, GAI can exacerbate existing AI 

        risks, and creates unique risks. GAI risks can vary along many
        dimensions: 

        • 

        Stage of the AI lifecycle: Risks can arise during design, development,
        deployment, operation, 

        and/or decommissioning. 

        • 

        Scope: Risks may exist at individual model or system levels, at the
        application or implementation 

        levels (i.e., for a specific use case), or at the ecosystem level – that
        is, beyond a single system or 

        organizational context. Examples of the latter include the expansion of
        “algorithmic 

        monocultures,3” resulting from repeated use of the same model, or
        impacts on access to 

        opportunity, labor markets, and the creative economies.4 

        • 

        Source of risk: Risks may emerge from factors related to the design,
        training, or operation of the 

        GAI model itself, stemming in some cases from GAI model or system
        inputs, and in other cases, 

        from GAI system outputs. Many GAI risks, however, originate from human
        behavior, including 
         
         
        3 “Algorithmic monocultures” refers to the phenomenon in which repeated
        use of the same model or algorithm in 

        consequential decision-making settings like employment and lending can
        result in increased susceptibility by 

        systems to correlated failures (like unexpected shocks), due to multiple
        actors relying on the same algorithm.  

        4 Many studies have projected the impact of AI on the workforce and
        labor markets. Fewer studies have examined 

        the impact of GAI on the labor market, though some industry surveys
        indicate that that both employees and 

        employers are pondering this disruption.
      - >-
        44 

        MG-3.2-007 

        Leverage feedback and recommendations from organizational boards or 

        committees related to the deployment of GAI applications and content 

        provenance when using third-party pre-trained models. 

        Information Integrity; Value Chain 

        and Component Integration 

        MG-3.2-008 

        Use human moderation systems where appropriate to review generated
        content 

        in accordance with human-AI configuration policies established in the
        Govern 

        function, aligned with socio-cultural norms in the context of use, and
        for settings 

        where AI models are demonstrated to perform poorly. 

        Human-AI Configuration 

        MG-3.2-009 

        Use organizational risk tolerance to evaluate acceptable risks and
        performance 

        metrics and decommission or retrain pre-trained models that perform
        outside of 

        defined limits. 

        CBRN Information or Capabilities; 

        Confabulation 

        AI Actor Tasks: AI Deployment, Operation and Monitoring, Third-party
        entities 
         
        MANAGE 4.1: Post-deployment AI system monitoring plans are implemented,
        including mechanisms for capturing and evaluating 

        input from users and other relevant AI Actors, appeal and override,
        decommissioning, incident response, recovery, and change 

        management. 

        Action ID 

        Suggested Action 

        GAI Risks 

        MG-4.1-001 

        Collaborate with external researchers, industry experts, and community 

        representatives to maintain awareness of emerging best practices and 

        technologies in measuring and managing identified risks. 

        Information Integrity; Harmful Bias 

        and Homogenization 

        MG-4.1-002 

        Establish, maintain, and evaluate effectiveness of organizational
        processes and 

        procedures for post-deployment monitoring of GAI systems, particularly
        for 

        potential confabulation, CBRN, or cyber risks. 

        CBRN Information or Capabilities; 

        Confabulation; Information 

        Security 

        MG-4.1-003 

        Evaluate the use of sentiment analysis to gauge user sentiment regarding
        GAI 

        content performance and impact, and work in collaboration with AI
        Actors 

        experienced in user research and experience. 

        Human-AI Configuration 

        MG-4.1-004 Implement active learning techniques to identify instances
        where the model fails 

        or produces unexpected outputs. 

        Confabulation 

        MG-4.1-005 

        Share transparency reports with internal and external stakeholders that
        detail 

        steps taken to update the GAI system to enhance transparency and 

        accountability. 

        Human-AI Configuration; Harmful 

        Bias and Homogenization 

        MG-4.1-006 

        Track dataset modifications for provenance by monitoring data deletions, 

        rectification requests, and other changes that may impact the
        verifiability of 

        content origins. 

        Information Integrity
  - source_sentence: >-
      What techniques should be deployed to verify the accuracy and veracity of
      information generated by GAI systems?
    sentences:
      - >-
        10 

        GAI systems can ease the unintentional production or dissemination of
        false, inaccurate, or misleading 

        content (misinformation) at scale, particularly if the content stems
        from confabulations.  

        GAI systems can also ease the deliberate production or dissemination of
        false or misleading information 

        (disinformation) at scale, where an actor has the explicit intent to
        deceive or cause harm to others. Even 

        very subtle changes to text or images can manipulate human and machine
        perception. 

        Similarly, GAI systems could enable a higher degree of sophistication
        for malicious actors to produce 

        disinformation that is targeted towards specific demographics. Current
        and emerging multimodal models 

        make it possible to generate both text-based disinformation and highly
        realistic “deepfakes” – that is, 

        synthetic audiovisual content and photorealistic images.12 Additional
        disinformation threats could be 

        enabled by future GAI models trained on new data modalities. 

        Disinformation and misinformation – both of which may be facilitated by
        GAI – may erode public trust in 

        true or valid evidence and information, with downstream effects. For
        example, a synthetic image of a 

        Pentagon blast went viral and briefly caused a drop in the stock market.
        Generative AI models can also 

        assist malicious actors in creating compelling imagery and propaganda to
        support disinformation 

        campaigns, which may not be photorealistic, but could enable these
        campaigns to gain more reach and 

        engagement on social media platforms. Additionally, generative AI models
        can assist malicious actors in 

        creating fraudulent content intended to impersonate others. 

        Trustworthy AI Characteristics: Accountable and Transparent, Safe, Valid
        and Reliable, Interpretable and 

        Explainable 

        2.9. Information Security 

        Information security for computer systems and data is a mature field with
        widely accepted and 

        standardized practices for offensive and defensive cyber capabilities.
        GAI-based systems present two 

        primary information security risks: GAI could potentially discover or
        enable new cybersecurity risks by 

        lowering the barriers for or easing automated exercise of offensive
        capabilities; simultaneously, it 

        expands the available attack surface, as GAI itself is vulnerable to
        attacks like prompt injection or data 

        poisoning.  

        Offensive cyber capabilities advanced by GAI systems may augment
        cybersecurity attacks such as 

        hacking, malware, and phishing. Reports have indicated that LLMs are
        already able to discover some 

        vulnerabilities in systems (hardware, software, data) and write code to
        exploit them. Sophisticated threat 

        actors might further these risks by developing GAI-powered security
        co-pilots for use in several parts of 

        the attack chain, including informing attackers on how to proactively
        evade threat detection and escalate 

        privileges after gaining system access. 

        Information security for GAI models and systems also includes
        maintaining availability of the GAI system 

        and the integrity and (when applicable) the confidentiality of the GAI
        code, training data, and model 

        weights. To identify and secure potential attack points in AI systems or
        specific components of the AI 
         
         
        12 See also https://doi.org/10.6028/NIST.AI.100-4, to be published.
      - >-
        25 

        MP-2.3-002 Review and document accuracy, representativeness, relevance,
        suitability of data 

        used at different stages of AI life cycle. 

        Harmful Bias and Homogenization; 

        Intellectual Property 

        MP-2.3-003 

        Deploy and document fact-checking techniques to verify the accuracy and 

        veracity of information generated by GAI systems, especially when the 

        information comes from multiple (or unknown) sources. 

        Information Integrity  

        MP-2.3-004 Develop and implement testing techniques to identify GAI
        produced content (e.g., 

        synthetic media) that might be indistinguishable from human-generated
        content. Information Integrity 

        MP-2.3-005 Implement plans for GAI systems to undergo regular
        adversarial testing to identify 

        vulnerabilities and potential manipulation or misuse. 

        Information Security 

        AI Actor Tasks: AI Development, Domain Experts, TEVV 
         
        MAP 3.4: Processes for operator and practitioner proficiency with AI
        system performance and trustworthiness – and relevant 

        technical standards and certifications – are defined, assessed, and
        documented. 

        Action ID 

        Suggested Action 

        GAI Risks 

        MP-3.4-001 

        Evaluate whether GAI operators and end-users can accurately understand 

        content lineage and origin. 

        Human-AI Configuration; 

        Information Integrity 

        MP-3.4-002 Adapt existing training programs to include modules on
        digital content 

        transparency. 

        Information Integrity 

        MP-3.4-003 Develop certification programs that test proficiency in
        managing GAI risks and 

        interpreting content provenance, relevant to specific industry and
        context. 

        Information Integrity 

        MP-3.4-004 Delineate human proficiency tests from tests of GAI
        capabilities. 

        Human-AI Configuration 

        MP-3.4-005 Implement systems to continually monitor and track the
        outcomes of human-GAI 

        configurations for future refinement and improvements. 

        Human-AI Configuration; 

        Information Integrity 

        MP-3.4-006 

        Involve the end-users, practitioners, and operators in GAI system in
        prototyping 

        and testing activities. Make sure these tests cover various scenarios,
        such as crisis 

        situations or ethically sensitive contexts. 

        Human-AI Configuration; 

        Information Integrity; Harmful Bias 

        and Homogenization; Dangerous, 

        Violent, or Hateful Content 

        AI Actor Tasks: AI Design, AI Development, Domain Experts, End-Users,
        Human Factors, Operation and Monitoring
      - >-
        27 

        MP-4.1-010 

        Conduct appropriate diligence on training data use to assess
        intellectual property, 

        and privacy, risks, including to examine whether use of proprietary or
        sensitive 

        training data is consistent with applicable laws.  

        Intellectual Property; Data Privacy 

        AI Actor Tasks: Governance and Oversight, Operation and Monitoring,
        Procurement, Third-party entities 
         
        MAP 5.1: Likelihood and magnitude of each identified impact (both
        potentially beneficial and harmful) based on expected use, past 

        uses of AI systems in similar contexts, public incident reports,
        feedback from those external to the team that developed or deployed 

        the AI system, or other data are identified and documented. 

        Action ID 

        Suggested Action 

        GAI Risks 

        MP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a
        system's synthetic 

        data generation capabilities for potential misuse or vulnerabilities. 

        Information Integrity; Information 

        Security 

        MP-5.1-002 

        Identify potential content provenance harms of GAI, such as
        misinformation or 

        disinformation, deepfakes, including NCII, or tampered content.
        Enumerate and 

        rank risks based on their likelihood and potential impact, and determine
        how well 

        provenance solutions address specific risks and/or harms. 

        Information Integrity; Dangerous, 

        Violent, or Hateful Content; 

        Obscene, Degrading, and/or 

        Abusive Content 

        MP-5.1-003 

        Consider disclosing use of GAI to end users in relevant contexts, while
        considering 

        the objective of disclosure, the context of use, the likelihood and
        magnitude of the 

        risk posed, the audience of the disclosure, as well as the frequency of
        the 

        disclosures. 

        Human-AI Configuration 

        MP-5.1-004 Prioritize GAI structured public feedback processes based on
        risk assessment 

        estimates. 

        Information Integrity; CBRN 

        Information or Capabilities; 

        Dangerous, Violent, or Hateful 

        Content; Harmful Bias and 

        Homogenization 

        MP-5.1-005 Conduct adversarial role-playing exercises, GAI red-teaming,
        or chaos testing to 

        identify anomalous or unforeseen failure modes. 

        Information Security 

        MP-5.1-006 

        Profile threats and negative impacts arising from GAI systems interacting
        with, 

        manipulating, or generating content, and outlining known and potential 

        vulnerabilities and the likelihood of their occurrence. 

        Information Security 

        AI Actor Tasks: AI Deployment, AI Design, AI Development, AI Impact
        Assessment, Affected Individuals and Communities, End-

        Users, Operation and Monitoring
  - source_sentence: What is the phenomenon referred to as "confabulation" in GAI systems?
    sentences:
      - >-
        50 

        Participatory Engagement Methods 

        On an ad hoc or more structured basis, organizations can design and use
        a variety of channels to engage 

        external stakeholders in product development or review. Focus groups
        with select experts can provide 

        feedback on a range of issues. Small user studies can provide feedback
        from representative groups or 

        populations. Anonymous surveys can be used to poll or gauge reactions to
        specific features. Participatory 

        engagement methods are often less structured than field testing or red
        teaming, and are more 

        commonly used in early stages of AI or product development.  

        Field Testing 

        Field testing involves structured settings to evaluate risks and impacts
        and to simulate the conditions 

        under which the GAI system will be deployed. Field style tests can be
        adapted from a focus on user 

        preferences and experiences towards AI risks and impacts – both negative
        and positive. When carried 

        out with large groups of users, these tests can provide estimations of
        the likelihood of risks and impacts 

        in real world interactions. 

        Organizations may also collect feedback on outcomes, harms, and user
        experience directly from users in 

        the production environment after a model has been released, in
        accordance with human subject 

        standards such as informed consent and compensation. Organizations
        should follow applicable human 

        subjects research requirements, and best practices such as informed
        consent and subject compensation, 

        when implementing feedback activities. 

        AI Red-teaming 

        AI red-teaming is an evolving practice that references exercises often
        conducted in a controlled 

        environment and in collaboration with AI developers building AI models
        to identify potential adverse 

        behavior or outcomes of a GAI model or system, how they could occur, and
        stress test safeguards”. AI 

        red-teaming can be performed before or after AI models or systems are
        made available to the broader 

        public; this section focuses on red-teaming in pre-deployment
        contexts.  

        The quality of AI red-teaming outputs is related to the background and
        expertise of the AI red team 

        itself. Demographically and interdisciplinarily diverse AI red teams can
        be used to identify flaws in the 

        varying contexts where GAI will be used. For best results, AI red teams
        should demonstrate domain 

        expertise, and awareness of socio-cultural aspects within the deployment
        context. AI red-teaming results 

        should be given additional analysis before they are incorporated into
        organizational governance and 

        decision making, policy and procedural updates, and AI risk management
        efforts. 

        Various types of AI red-teaming may be appropriate, depending on the use
        case: 

        • 

        General Public: Performed by general users (not necessarily AI or
        technical experts) who are 

        expected to use the model or interact with its outputs, and who bring
        their own lived 

        experiences and perspectives to the task of AI red-teaming. These
        individuals may have been 

        provided instructions and material to complete tasks which may elicit
        harmful model behaviors. 

        This type of exercise can be more effective with large groups of AI
        red-teamers. 

        • 

        Expert: Performed by specialists with expertise in the domain or specific
        AI red-teaming context 

        of use (e.g., medicine, biotech, cybersecurity).  

        • 

        Combination: In scenarios when it is difficult to identify and recruit
        specialists with sufficient 

        domain and contextual expertise, AI red-teaming exercises may leverage
        both expert and
      - >-
        54 

        Appendix B. References 

        Acemoglu, D. (2024) The Simple Macroeconomics of AI
        https://www.nber.org/papers/w32487 

        AI Incident Database. https://incidentdatabase.ai/ 

        Atherton, D. (2024) Deepfakes and Child Safety: A Survey and Analysis of
        2023 Incidents and Responses. 

        AI Incident Database.
        https://incidentdatabase.ai/blog/deepfakes-and-child-safety/ 

        Badyal, N. et al. (2023) Intentional Biases in LLM Responses. arXiv.
        https://arxiv.org/pdf/2311.07611 

        Bing Chat: Data Exfiltration Exploit Explained. Embrace The Red. 

        https://embracethered.com/blog/posts/2023/bing-chat-data-exfiltration-poc-and-fix/ 

        Bommasani, R. et al. (2022) Picking on the Same Person: Does Algorithmic
        Monoculture lead to Outcome 

        Homogenization? arXiv. https://arxiv.org/pdf/2211.13972 

        Boyarskaya, M. et al. (2020) Overcoming Failures of Imagination in AI
        Infused System Development and 

        Deployment. arXiv. https://arxiv.org/pdf/2011.13416 

        Browne, D. et al. (2023) Securing the AI Pipeline. Mandiant. 

        https://www.mandiant.com/resources/blog/securing-ai-pipeline 

        Burgess, M. (2024) Generative AI’s Biggest Security Flaw Is Not Easy to
        Fix. WIRED. 

        https://www.wired.com/story/generative-ai-prompt-injection-hacking/ 

        Burtell, M. et al. (2024) The Surprising Power of Next Word Prediction:
        Large Language Models 

        Explained, Part 1. Georgetown Center for Security and Emerging
        Technology. 

        https://cset.georgetown.edu/article/the-surprising-power-of-next-word-prediction-large-language-

        models-explained-part-1/ 

        Canadian Centre for Cyber Security (2023) Generative artificial
        intelligence (AI) - ITSAP.00.041. 

        https://www.cyber.gc.ca/en/guidance/generative-artificial-intelligence-ai-itsap00041 

        Carlini, N., et al. (2021) Extracting Training Data from Large Language
        Models. Usenix. 

        https://www.usenix.org/conference/usenixsecurity21/presentation/carlini-extracting 

        Carlini, N. et al. (2023) Quantifying Memorization Across Neural
        Language Models. ICLR 2023. 

        https://arxiv.org/pdf/2202.07646 

        Carlini, N. et al. (2024) Stealing Part of a Production Language Model.
        arXiv. 

        https://arxiv.org/abs/2403.06634 

        Chandra, B. et al. (2023) Dismantling the Disinformation Business of
        Chinese Influence Operations. 

        RAND.
        https://www.rand.org/pubs/commentary/2023/10/dismantling-the-disinformation-business-of-

        chinese.html 

        Ciriello, R. et al. (2024) Ethical Tensions in Human-AI Companionship: A
        Dialectical Inquiry into Replika. 

        ResearchGate.
        https://www.researchgate.net/publication/374505266_Ethical_Tensions_in_Human-

        AI_Companionship_A_Dialectical_Inquiry_into_Replika 

        Dahl, M. et al. (2024) Large Legal Fictions: Profiling Legal
        Hallucinations in Large Language Models. arXiv. 

        https://arxiv.org/abs/2401.01301
      - >-
        6 

        2.2. Confabulation 

        “Confabulation” refers to a phenomenon in which GAI systems generate and
        confidently present 

        erroneous or false content in response to prompts. Confabulations also
        include generated outputs that 

        diverge from the prompts or other input or that contradict previously
        generated statements in the same 

        context. These phenomena are colloquially also referred to as
        “hallucinations” or “fabrications.” 

        Confabulations can occur across GAI outputs and contexts.9,10
        Confabulations are a natural result of the 

        way generative models are designed: they generate outputs that
        approximate the statistical distribution 

        of their training data; for example, LLMs predict the next token or word
        in a sentence or phrase. While 

        such statistical prediction can produce factually accurate and
        consistent outputs, it can also produce 

        outputs that are factually inaccurate or internally inconsistent. This
        dynamic is particularly relevant when 

        it comes to open-ended prompts for long-form responses and in domains
        which require highly 

        contextual and/or domain expertise.  

        Risks from confabulations may arise when users believe false content –
        often due to the confident nature 

        of the response – leading users to act upon or promote the false
        information. This poses a challenge for 

        many real-world applications, such as in healthcare, where a
        confabulated summary of patient 

        information reports could cause doctors to make incorrect diagnoses
        and/or recommend the wrong 

        treatments. Risks of confabulated content may be especially important to
        monitor when integrating GAI 

        into applications involving consequential decision making. 

        GAI outputs may also include confabulated logic or citations that
        purport to justify or explain the 

        system’s answer, which may further mislead humans into inappropriately
        trusting the system’s output. 

        For instance, LLMs sometimes provide logical steps for how they arrived
        at an answer even when the 

        answer itself is incorrect. Similarly, an LLM could falsely assert that
        it is human or has human traits, 

        potentially deceiving humans into believing they are speaking with
        another human. 

        The extent to which humans can be deceived by LLMs, the mechanisms by
        which this may occur, and the 

        potential risks from adversarial prompting of such behavior are emerging
        areas of study. Given the wide 

        range of downstream impacts of GAI, it is difficult to estimate the
        downstream scale and impact of 

        confabulations. 

        Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe,
        Valid and Reliable, Explainable 

        and Interpretable 

        2.3. Dangerous, Violent, or Hateful Content 

        GAI systems can produce content that is inciting, radicalizing, or
        threatening, or that glorifies violence, 

        with greater ease and scale than other technologies. LLMs have been
        reported to generate dangerous or 

        violent recommendations, and some models have generated actionable
        instructions for dangerous or 
         
         
        9 Confabulations of falsehoods are most commonly a problem for
        text-based outputs; for audio, image, or video 

        content, creative generation of non-factual content can be a desired
        behavior.  

        10 For example, legal confabulations have been shown to be pervasive in
        current state-of-the-art LLMs. See also, 

        e.g.,
  - source_sentence: >-
      How can organizations address risks associated with the use of third-party
      data for GAI model inputs?
    sentences:
      - >-
        48 

        • Data protection 

        • Data retention  

        • Consistency in use of defining key terms 

        • Decommissioning 

        • Discouraging anonymous use 

        • Education  

        • Impact assessments  

        • Incident response 

        • Monitoring 

        • Opt-outs  

        • Risk-based controls 

        • Risk mapping and measurement 

        • Science-backed TEVV practices 

        • Secure software development practices 

        • Stakeholder engagement 

        • Synthetic content detection and 

        labeling tools and techniques 

        • Whistleblower protections 

        • Workforce diversity and 

        interdisciplinary teams

        Establishing acceptable use policies and guidance for the use of GAI in
        formal human-AI teaming settings 

        as well as different levels of human-AI configurations can help to
        decrease risks arising from misuse, 

        abuse, inappropriate repurpose, and misalignment between systems and
        users. These practices are just 

        one example of adapting existing governance protocols for GAI
        contexts.  

        A.1.3. Third-Party Considerations 

        Organizations may seek to acquire, embed, incorporate, or use
        open-source or proprietary third-party 

        GAI models, systems, or generated data for various applications across
        an enterprise. Use of these GAI 

        tools and inputs has implications for all functions of the organization
        – including but not limited to 

        acquisition, human resources, legal, compliance, and IT services –
        regardless of whether they are carried 

        out by employees or third parties. Many of the actions cited above are
        relevant and options for 

        addressing third-party considerations. 

        Third party GAI integrations may give rise to increased intellectual
        property, data privacy, or information 

        security risks, pointing to the need for clear guidelines for
        transparency and risk management regarding 

        the collection and use of third-party data for model inputs.
        Organizations may consider varying risk 

        controls for foundation models, fine-tuned models, and embedded tools,
        enhanced processes for 

        interacting with external GAI technologies or service providers.
        Organizations can apply standard or 

        existing risk controls and processes to proprietary or open-source GAI
        technologies, data, and third-party 

        service providers, including acquisition and procurement due diligence,
        requests for software bills of 

        materials (SBOMs), application of service level agreements (SLAs), and
        statement on standards for 

        attestation engagement (SSAE) reports to help with third-party
        transparency and risk management for 

        GAI systems. 

        A.1.4. Pre-Deployment Testing 

        Overview 

        The diverse ways and contexts in which GAI systems may be developed,
        used, and repurposed 

        complicates risk mapping and pre-deployment measurement efforts. Robust
        test, evaluation, validation, 

        and verification (TEVV) processes can be iteratively applied – and
        documented – in early stages of the AI 

        lifecycle and informed by representative AI Actors (see Figure 3 of the
        AI RMF). Until new and rigorous
      - >-
        About AI at NIST: The National Institute of Standards and Technology
        (NIST) develops measurements, 

        technology, tools, and standards to advance reliable, safe, transparent,
        explainable, privacy-enhanced, 

        and fair artificial intelligence (AI) so that its full commercial and
        societal benefits can be realized without 

        harm to people or the planet. NIST, which has conducted both fundamental
        and applied work on AI for 

        more than a decade, is also helping to fulfill the 2023 Executive Order
        on Safe, Secure, and Trustworthy 

        AI. NIST established the U.S. AI Safety Institute and the companion AI
        Safety Institute Consortium to 

        continue the efforts set in motion by the E.O. to build the science
        necessary for safe, secure, and 

        trustworthy development and use of AI. 

        Acknowledgments: This report was accomplished with the many helpful
        comments and contributions 

        from the community, including the NIST Generative AI Public Working
        Group, and NIST staff and guest 

        researchers: Chloe Autio, Jesse Dunietz, Patrick Hall, Shomik Jain,
        Kamie Roberts, Reva Schwartz, Martin 

        Stanley, and Elham Tabassi. 

        NIST Technical Series Policies 

        Copyright, Use, and Licensing Statements 

        NIST Technical Series Publication Identifier Syntax 

        Publication History 

        Approved by the NIST Editorial Review Board on 07-25-2024 

        Contact Information 

        [email protected] 

        National Institute of Standards and Technology 

        Attn: NIST AI Innovation Lab, Information Technology Laboratory 

        100 Bureau Drive (Mail Stop 8900) Gaithersburg, MD 20899-8900 

        Additional Information 

        Additional information about this publication and other NIST AI
        publications are available at 

        https://airc.nist.gov/Home. 
         
        Disclaimer: Certain commercial entities, equipment, or materials may be
        identified in this document in 

        order to adequately describe an experimental procedure or concept. Such
        identification is not intended to 

        imply recommendation or endorsement by the National Institute of
        Standards and Technology, nor is it 

        intended to imply that the entities, materials, or equipment are
        necessarily the best available for the 

        purpose. Any mention of commercial, non-profit, academic partners, or
        their products, or references is 

        for information only; it is not intended to imply endorsement or
        recommendation by any U.S. 

        Government agency.
      - >-
        8 

        Trustworthy AI Characteristics: Accountable and Transparent, Privacy
        Enhanced, Safe, Secure and 

        Resilient 

        2.5. Environmental Impacts 

        Training, maintaining, and operating (running inference on) GAI systems
        are resource-intensive activities, 

        with potentially large energy and environmental footprints. Energy and
        carbon emissions vary based on 

        what is being done with the GAI model (i.e., pre-training, fine-tuning,
        inference), the modality of the 

        content, hardware used, and type of task or application. 

        Current estimates suggest that training a single transformer LLM can
        emit as much carbon as 300 round-

        trip flights between San Francisco and New York. In a study comparing
        energy consumption and carbon 

        emissions for LLM inference, generative tasks (e.g., text summarization)
        were found to be more energy- 

        and carbon-intensive than discriminative or non-generative tasks (e.g.,
        text classification).  

        Methods for creating smaller versions of trained models, such as model
        distillation or compression, 

        could reduce environmental impacts at inference time, but training and
        tuning such models may still 

        contribute to their environmental impacts. Currently there is no agreed
        upon method to estimate 

        environmental impacts from GAI.  

        Trustworthy AI Characteristics: Accountable and Transparent, Safe 

        2.6. Harmful Bias and Homogenization 

        Bias exists in many forms and can become ingrained in automated systems.
        AI systems, including GAI 

        systems, can increase the speed and scale at which harmful biases
        manifest and are acted upon, 

        potentially perpetuating and amplifying harms to individuals, groups,
        communities, organizations, and 

        society. For example, when prompted to generate images of CEOs, doctors,
        lawyers, and judges, current 

        text-to-image models underrepresent women and/or racial minorities, and
        people with disabilities. 

        Image generator models have also produced biased or stereotyped output
        for various demographic 

        groups and have difficulty producing non-stereotyped content even when the
        prompt specifically 

        requests image features that are inconsistent with the stereotypes.
        Harmful bias in GAI models, which 

        may stem from their training data, can also cause representational harms
        or perpetuate or exacerbate 

        bias based on race, gender, disability, or other protected classes.  

        Harmful bias in GAI systems can also lead to harms via disparities
        between how a model performs for 

        different subgroups or languages (e.g., an LLM may perform less well for
        non-English languages or 

        certain dialects). Such disparities can contribute to discriminatory
        decision-making or amplification of 

        existing societal biases. In addition, GAI systems may be
        inappropriately trusted to perform similarly 

        across all subgroups, which could leave the groups facing
        underperformance with worse outcomes than 

        if no GAI system were used. Disparate or reduced performance for
        lower-resource languages also 

        presents challenges to model adoption, inclusion, and accessibility, and
        may make preservation of 

        endangered languages more difficult if GAI systems become embedded in
        everyday processes that would 

        otherwise have been opportunities to use these languages.  

        Bias is mutually reinforcing with the problem of undesired
        homogenization, in which GAI systems 

        produce skewed distributions of outputs that are overly uniform (for
        example, repetitive aesthetic styles

SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2

This is a sentence-transformers model finetuned from sentence-transformers/all-MiniLM-L6-v2. It maps sentences & paragraphs to a 384-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: sentence-transformers/all-MiniLM-L6-v2
• Maximum Sequence Length: 256 tokens
• Output Dimensionality: 384 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("danicafisher/dfisher-base-sentence-transformer")
# Run inference
sentences = [
    'How can organizations address risks associated with the use of third-party data for GAI model inputs?',
    '48 \n• Data protection \n• Data retention  \n• Consistency in use of defining key terms \n• Decommissioning \n• Discouraging anonymous use \n• Education  \n• Impact assessments  \n• Incident response \n• Monitoring \n• Opt-outs  \n• Risk-based controls \n• Risk mapping and measurement \n• Science-backed TEVV practices \n• Secure software development practices \n• Stakeholder engagement \n• Synthetic content detection and \nlabeling tools and techniques \n• Whistleblower protections \n• Workforce diversity and \ninterdisciplinary teams\nEstablishing acceptable use policies and guidance for the use of GAI in formal human-AI teaming settings \nas well as different levels of human-AI configurations can help to decrease risks arising from misuse, \nabuse, inappropriate repurpose, and misalignment between systems and users. These practices are just \none example of adapting existing governance protocols for GAI contexts.  \nA.1.3. Third-Party Considerations \nOrganizations may seek to acquire, embed, incorporate, or use open-source or proprietary third-party \nGAI models, systems, or generated data for various applications across an enterprise. Use of these GAI \ntools and inputs has implications for all functions of the organization – including but not limited to \nacquisition, human resources, legal, compliance, and IT services – regardless of whether they are carried \nout by employees or third parties. Many of the actions cited above are relevant and options for \naddressing third-party considerations. \nThird party GAI integrations may give rise to increased intellectual property, data privacy, or information \nsecurity risks, pointing to the need for clear guidelines for transparency and risk management regarding \nthe collection and use of third-party data for model inputs. Organizations may consider varying risk \ncontrols for foundation models, fine-tuned models, and embedded tools, enhanced processes for \ninteracting with external GAI technologies or service providers. Organizations can apply standard or \nexisting risk controls and processes to proprietary or open-source GAI technologies, data, and third-party \nservice providers, including acquisition and procurement due diligence, requests for software bills of \nmaterials (SBOMs), application of service level agreements (SLAs), and statement on standards for \nattestation engagement (SSAE) reports to help with third-party transparency and risk management for \nGAI systems. \nA.1.4. Pre-Deployment Testing \nOverview \nThe diverse ways and contexts in which GAI systems may be developed, used, and repurposed \ncomplicates risk mapping and pre-deployment measurement efforts. Robust test, evaluation, validation, \nand verification (TEVV) processes can be iteratively applied – and documented – in early stages of the AI \nlifecycle and informed by representative AI Actors (see Figure 3 of the AI RMF). Until new and rigorous',
    '8 \nTrustworthy AI Characteristics: Accountable and Transparent, Privacy Enhanced, Safe, Secure and \nResilient \n2.5. Environmental Impacts \nTraining, maintaining, and operating (running inference on) GAI systems are resource-intensive activities, \nwith potentially large energy and environmental footprints. Energy and carbon emissions vary based on \nwhat is being done with the GAI model (i.e., pre-training, fine-tuning, inference), the modality of the \ncontent, hardware used, and type of task or application. \nCurrent estimates suggest that training a single transformer LLM can emit as much carbon as 300 round-\ntrip flights between San Francisco and New York. In a study comparing energy consumption and carbon \nemissions for LLM inference, generative tasks (e.g., text summarization) were found to be more energy- \nand carbon-intensive than discriminative or non-generative tasks (e.g., text classification).  \nMethods for creating smaller versions of trained models, such as model distillation or compression, \ncould reduce environmental impacts at inference time, but training and tuning such models may still \ncontribute to their environmental impacts. Currently there is no agreed upon method to estimate \nenvironmental impacts from GAI.  \nTrustworthy AI Characteristics: Accountable and Transparent, Safe \n2.6. Harmful Bias and Homogenization \nBias exists in many forms and can become ingrained in automated systems. AI systems, including GAI \nsystems, can increase the speed and scale at which harmful biases manifest and are acted upon, \npotentially perpetuating and amplifying harms to individuals, groups, communities, organizations, and \nsociety. For example, when prompted to generate images of CEOs, doctors, lawyers, and judges, current \ntext-to-image models underrepresent women and/or racial minorities, and people with disabilities. \nImage generator models have also produced biased or stereotyped output for various demographic \ngroups and have difficulty producing non-stereotyped content even when the prompt specifically \nrequests image features that are inconsistent with the stereotypes. Harmful bias in GAI models, which \nmay stem from their training data, can also cause representational harms or perpetuate or exacerbate \nbias based on race, gender, disability, or other protected classes.  \nHarmful bias in GAI systems can also lead to harms via disparities between how a model performs for \ndifferent subgroups or languages (e.g., an LLM may perform less well for non-English languages or \ncertain dialects). Such disparities can contribute to discriminatory decision-making or amplification of \nexisting societal biases. In addition, GAI systems may be inappropriately trusted to perform similarly \nacross all subgroups, which could leave the groups facing underperformance with worse outcomes than \nif no GAI system were used. Disparate or reduced performance for lower-resource languages also \npresents challenges to model adoption, inclusion, and accessibility, and may make preservation of \nendangered languages more difficult if GAI systems become embedded in everyday processes that would \notherwise have been opportunities to use these languages.  \nBias is mutually reinforcing with the problem of undesired homogenization, in which GAI systems \nproduce skewed distributions of outputs that are overly uniform (for example, repetitive aesthetic styles',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

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

Training Details

Training Dataset

Unnamed Dataset

• Size: 128 training samples
• Columns: sentence_0 and sentence_1
• Approximate statistics based on the first 128 samples:

  	sentence_0	sentence_1
  type	string	string
  details	min: 17 tokens mean: 23.14 tokens max: 38 tokens	min: 56 tokens mean: 247.42 tokens max: 256 tokens

• Samples:

  sentence_0	sentence_1
  What measures are suggested to assess the environmental impact of AI model training and management activities?	37 MS-2.11-005 Assess the proportion of synthetic to non-synthetic training data and verify training data is not overly homogenous or GAI-produced to mitigate concerns of model collapse. Harmful Bias and Homogenization AI Actor Tasks: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End-Users, Operation and Monitoring, TEVV MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP function – are assessed and documented. Action ID Suggested Action GAI Risks MS-2.12-001 Assess safety to physical environments when deploying GAI systems. Dangerous, Violent, or Hateful Content MS-2.12-002 Document anticipated environmental impacts of model development, maintenance, and deployment in product design decisions. Environmental MS-2.12-003 Measure or estimate environmental impacts (e.g., energy and water consumption) for training, fine tuning, and deploying models: Verify tradeoffs between resources used at inference time versus additional resources required at training time. Environmental MS-2.12-004 Verify effectiveness of carbon capture or offset programs for GAI training and applications, and address green-washing concerns. Environmental AI Actor Tasks: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV
  What are some limitations of current pre-deployment testing approaches for GAI applications?	49 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use recommended “pre-deployment testing” practices to measure performance, capabilities, limits, risks, and impacts. This section describes risk measurement and estimation as part of pre-deployment TEVV, and examines the state of play for pre-deployment testing methodologies. Limitations of Current Pre-deployment Test Approaches Currently available pre-deployment TEVV processes used for GAI applications may be inadequate, non- systematically applied, or fail to reflect or mismatched to deployment contexts. For example, the anecdotal testing of GAI system capabilities through video games or standardized tests designed for humans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or reliability in those domains. Similarly, jailbreaking or prompt engineering tests may not systematically assess validity or reliability risks. Measurement gaps can arise from mismatches between laboratory and real-world settings. Current testing approaches often remain focused on laboratory conditions or restricted to benchmark test datasets and in silico techniques that may not extrapolate well to—or directly assess GAI impacts in real- world conditions. For example, current measurement gaps for GAI make it difficult to precisely estimate its potential ecosystem-level or longitudinal risks and related political, social, and economic impacts. Gaps between benchmarks and real-world use of GAI systems may likely be exacerbated due to prompt sensitivity and broad heterogeneity of contexts of use. A.1.5. Structured Public Feedback Structured public feedback can be used to evaluate whether GAI systems are performing as intended and to calibrate and verify traditional measurement methods. Examples of structured feedback include, but are not limited to: • Participatory Engagement Methods: Methods used to solicit feedback from civil society groups, affected communities, and users, including focus groups, small user studies, and surveys. • Field Testing: Methods used to determine how people interact with, consume, use, and make sense of AI-generated information, and subsequent actions and effects, including UX, usability, and other structured, randomized experiments. • AI Red-teaming: A structured testing exercise used to probe an AI system to find flaws and vulnerabilities such as inaccurate, harmful, or discriminatory outputs, often in a controlled environment and in collaboration with system developers. Information gathered from structured public feedback can inform design, implementation, deployment approval, maintenance, or decommissioning decisions. Results and insights gleaned from these exercises can serve multiple purposes, including improving data quality and preprocessing, bolstering governance decision making, and enhancing system documentation and debugging practices. When implementing feedback activities, organizations should follow human subjects research requirements and best practices such as informed consent and subject compensation.
  How can organizations adjust their governance regimes to effectively manage the unique risks associated with generative AI?	47 Appendix A. Primary GAI Considerations The following primary considerations were derived as overarching themes from the GAI PWG consultation process. These considerations (Governance, Pre-Deployment Testing, Content Provenance, and Incident Disclosure) are relevant for voluntary use by any organization designing, developing, and using GAI and also inform the Actions to Manage GAI risks. Information included about the primary considerations is not exhaustive, but highlights the most relevant topics derived from the GAI PWG. Acknowledgments: These considerations could not have been surfaced without the helpful analysis and contributions from the community and NIST staff GAI PWG leads: George Awad, Luca Belli, Harold Booth, Mat Heyman, Yooyoung Lee, Mark Pryzbocki, Reva Schwartz, Martin Stanley, and Kyra Yee. A.1. Governance A.1.1. Overview Like any other technology system, governance principles and techniques can be used to manage risks related to generative AI models, capabilities, and applications. Organizations may choose to apply their existing risk tiering to GAI systems, or they may opt to revise or update AI system risk levels to address these unique GAI risks. This section describes how organizational governance regimes may be re- evaluated and adjusted for GAI contexts. It also addresses third-party considerations for governing across the AI value chain. A.1.2. Organizational Governance GAI opportunities, risks and long-term performance characteristics are typically less well-understood than non-generative AI tools and may be perceived and acted upon by humans in ways that vary greatly. Accordingly, GAI may call for different levels of oversight from AI Actors or different human-AI configurations in order to manage their risks effectively. Organizations’ use of GAI systems may also warrant additional human review, tracking and documentation, and greater management oversight. AI technology can produce varied outputs in multiple modalities and present many classes of user interfaces. This leads to a broader set of AI Actors interacting with GAI systems for widely differing applications and contexts of use. These can include data labeling and preparation, development of GAI models, content moderation, code generation and review, text generation and editing, image and video generation, summarization, search, and chat. These activities can take place within organizational settings or in the public domain. Organizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict with their tolerances or values. Governance tools and protocols that are applied to other types of AI systems can be applied to GAI systems. These plans and actions include: • Accessibility and reasonable accommodations • AI actor credentials and qualifications • Alignment to organizational values • Auditing and assessment • Change-management controls • Commercial use • Data provenance

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• per_device_train_batch_size: 20
• per_device_eval_batch_size: 20
• num_train_epochs: 10
• multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: no
• 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: 10
• 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

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.1.1
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@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",
}

MultipleNegativesRankingLoss

@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}
}