metadata
base_model: Alibaba-NLP/gte-large-en-v1.5
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:224
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: >-
What are some of the mental health impacts associated with the increased
use of surveillance technologies in schools and workplaces, as mentioned
in the context information?
sentences:
- >-
15 GV-1.3-004 Obtain input from stakeholder communities to identify
unacceptable use , in
accordance with activities in the AI RMF Map function . CBRN Information
or Capabilities ;
Obscene, Degrading, and/or
Abusive Content ; Harmful Bias
and Homogenization ; Dangerous,
Violent, or Hateful Content
GV-1.3-005 Maintain an updated hierarch y of identified and expected GAI
risks connected to
contexts of GAI model advancement and use, potentially including
specialized risk
levels for GAI systems that address issues such as model collapse and
algorithmic
monoculture. Harmful Bias and Homogenization
GV-1.3-006 Reevaluate organizational risk tolerances to account for
unacceptable negative risk
(such as where significant negative impacts are imminent, severe harms
are actually occurring, or large -scale risks could occur); and broad
GAI negative risks,
including: Immature safety or risk cultures related to AI and GAI
design, development and deployment, public information integrity risks,
including impacts on democratic processes, unknown long -term
performance characteristics of GAI. Information Integrity ; Dangerous
,
Violent, or Hateful Content ; CBRN
Information or Capabilities
GV-1.3-007 Devise a plan to halt development or deployment of a GAI
system that poses unacceptable negative risk. CBRN Information and
Capability ;
Information Security ; Information
Integrity
AI Actor Tasks: Governance and Oversight
GOVERN 1.4: The risk management process and its outcomes are established
through transparent policies, procedures, and other
controls based on organizational risk priorities.
Action ID Suggested Action GAI Risks
GV-1.4-001 Establish policies and mechanisms to prevent GAI systems from
generating
CSAM, NCII or content that violates the law. Obscene, Degrading,
and/or
Abusive Content ; Harmful Bias
and Homogenization ;
Dangerous, Violent, or Hateful Content
GV-1.4-002 Establish transparent acceptable use policies for GAI that
address illegal use or
applications of GAI. CBRN Information or
Capabilities ; Obscene,
Degrading, and/or Abusive Content ; Data Privacy ; Civil
Rights violations
AI Actor Tasks: AI Development, AI Deployment, Governance and Oversight
- >-
DATA PRIVACY
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the
principle seeks to address and protect
against, including illustrative examples.
Data privacy is a foundational and cross-cutting principle required for
achieving all others in this framework. Surveil -
lance and data collection, sharing, use, and reuse now sit at the
foundation of business models across many industries,
with more and more companies tracking the behavior of the American
public, building individual profiles based on this data, and using this
granular-level information as input into automated systems that further
track, profile, and impact the American public. Government agencies,
particularly law enforcement agencies, also use and help develop a
variety of technologies that enhance and expand surveillance
capabilities, which similarly collect data used as input into other
automated systems that directly impact people’s lives. Federal law has
not grown to address the expanding scale of private data collection, or
of the ability of governments at all levels to access that data and
leverage the means of private collection.
Meanwhile, members of the American public are often unable to access
their personal data or make critical decisions about its collection and
use. Data brokers frequently collect consumer data from numerous sources
without consumers’ permission or
knowledge.60 Moreover, there is a risk that inaccurate and faulty data
can be used to
make decisions about their lives, such as whether they will qualify for
a loan or get a job. Use of surveillance
technologies has increased in schools and workplaces, and, when coupled
with consequential management and
evaluation decisions, it is leading to mental health harms such as
lowered self-confidence, anxiet y, depression, and
a reduced ability to use analytical reasoning.61 Documented patterns
show that personal data is being aggregated by
data brokers to profile communities in harmful ways.62 The impact of all
this data harvesting is corrosive,
breeding distrust, anxiety, and other mental health problems; chilling
speech, protest, and worker organizing; and
threatening our democratic process.63 The American public should be
protected from these growing risks.
Increasingl y, some companies are taking these concerns seriously and
integrating mechanisms to protect consumer
privacy into their products by design and by default, including by
minimizing the data they collect, communicating collection and use
clearl y, and improving security practices. Federal government
surveillance and other collection and
use of data is governed by legal protections that help to protect civil
liberties and provide for limits on data retention in some cases. Many
states have also enacted consumer data privacy protection regimes to
address some of these harms.
Howeve r, these are not yet standard practices, and the United States
lacks a comprehensive statutory or regulatory
framework governing the rights of the public when it comes to personal
data. While a patchwork of laws exists to guide the collection and use
of personal data in specific contexts, including health, employment,
education, and credit, it can be unclear how these laws apply in other
contexts and in an increasingly automated societ y. Additional protec
-
tions would assure the American public that the automated systems they
use are not monitoring their activities, collecting information on their
lives, or otherwise surveilling them without context-specific consent or
legal authori
-
ty.
31
- >-
Applying The Blueprint for an AI Bill of Rights
SENSITIVE DATA: Data and metadata are sensitive if they pertain to an
individual in a sensitive domain
(defined below); are generated by technologies used in a sensitive
domain; can be used to infer data from a
sensitive domain or sensitive data about an individual (such as
disability-related data, genomic data, biometric data, behavioral data,
geolocation data, data related to interaction with the criminal justice
system, relationship history and legal status such as custody and
divorce information, and home, work, or school environmental data); or
have the reasonable potential to be used in ways that are likely to
expose individuals to meaningful harm, such as a loss of privacy or
financial harm due to identity theft. Data and metadata generated by or
about those who are not yet legal adults is also sensitive, even if not
related to a sensitive domain. Such data includes, but is not limited
to, numerical, text, image, audio, or video data.
SENSITIVE DOMAINS: “Sensitive domains” are those in which activities
being conducted can cause material
harms, including significant adverse effects on human rights such as
autonomy and dignit y, as well as civil liber-
ties and civil rights. Domains that have historically been singled out
as deserving of enhanced data protections
or where such enhanced protections are reasonably expected by the public
include, but are not limited to, health, family planning and care,
employment, education, criminal justice, and personal finance. In the
context of this framework, such domains are considered sensitive whether
or not the specifics of a system context would necessitate coverage
under existing la w, and domains and data that are considered sensitive
are under-
stood to change over time based on societal norms and context.
SURVEILLANCE TECHNOLOGY : “Surveillance technology” refers to products
or services marketed for
or that can be lawfully used to detect, monitor, intercept, collect,
exploit, preserve, protect, transmit, and/or
retain data, identifying information, or communications concerning
individuals or groups. This framework
limits its focus to both government and commercial use of surveillance
technologies when juxtaposed with
real-time or subsequent automated analysis and when such systems have a
potential for meaningful impact
on individuals’ or communities’ rights, opportunities, or access.
UNDERSERVED COMMUNITIES: The term “underserved communities” refers to
communities that have
been systematically denied a full opportunity to participate in aspects
of economic, social, and civic life, as
exemplified by the list in the preceding definition of “equit y.”
11
- source_sentence: >-
Discuss the implications of automatic signature verification software on
voter disenfranchisement in the United States, as highlighted in the
article by Kyle Wiggers. What are the potential risks associated with this
technology?
sentences:
- >-
ENDNOTES
96. National Science Foundation. NSF Program on Fairness in Artificial
Intelligence in Collaboration
with Amazon (FAI). Accessed July 20, 2022.
https://www.nsf.gov/pubs/2021/nsf21585/nsf21585.htm
97. Kyle Wiggers. Automatic signature verification software threatens to
disenfranchise U.S. voters.
VentureBeat. Oct. 25, 2020.
https://venturebeat.com/2020/10/25/automatic-signature-verification-software-threatens-to-disenfranchise-u-s-voters/
98. Ballotpedia. Cure period for absentee and mail-in ballots. Article
retrieved Apr 18, 2022.
https://ballotpedia.org/Cure_period_for_absentee_and_mail-in_ballots
99. Larry Buchanan and Alicia Parlapiano. Two of these Mail Ballot
Signatures are by the Same Person.
Which Ones? New York Times. Oct. 7, 2020.
https://www.nytimes.com/interactive/2020/10/07/upshot/mail-voting-ballots-signature-
matching.html
100. Rachel Orey and Owen Bacskai. The Low Down on Ballot Curing. Nov.
04, 2020.
https://bipartisanpolicy.org/blog/the-low-down-on-ballot-curing/101.
Andrew Kenney. 'I'm shocked that they need to have a smartphone': System
for unemployment
benefits exposes digital divide. USA Today. May 2, 2021.
https://www.usatoday.com/story/tech/news/2021/05/02/unemployment-benefits-system-leaving-
people-behind/4915248001/
102. Allie Gross. UIA lawsuit shows how the state criminalizes the
unemployed . Detroit Metro-Times.
Sep. 18, 2015.
https://www.metrotimes.com/news/uia-lawsuit-shows-how-the-state-criminalizes-the-unemployed-2369412
103. Maia Szalavitz. The Pain Was Unbearable. So Why Did Doctors Turn
Her Away? Wired. Aug. 11,
2021.
https://www.wired.com/story/opioid-drug-addiction-algorithm-chronic-pain/
104. Spencer Soper. Fired by Bot at Amazon: "It's You Against the
Machine" . Bloomberg, Jun. 28, 2021.
https://www.bloomberg.com/news/features/2021-06-28/fired-by-bot-amazon-turns-to-machine-
managers-and-workers-are-losing-out
105. Definitions of ‘equity’ and ‘underserved communities’ can be found
in the Definitions section of
this document as well as in Executive Order on Advancing Racial Equity
and Support for Underserved
Communities Through the Federal
Government:https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive-order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/
106. HealthCare.gov. Navigator - HealthCare.gov Glossary. Accessed May
2, 2022.
https://www.healthcare.gov/glossary/navigator/
72
- >-
SAFE AND EFFECTIVE
SYSTEMS
WHY THIS PRINCIPLE IS IMPORTANT
This section provides a brief summary of the problems which the
principle seeks to address and protect
against, including illustrative examples.
• AI-enabled “nudification” technology that creates images where people
appear to be nude—including apps that
enable non-technical users to create or alter images of individuals
without their consent—has proliferated at an
alarming rate. Such technology is becoming a common form of image-based
abuse that disproportionately
impacts women. As these tools become more sophisticated, they are
producing altered images that are increasing -
ly realistic and are difficult for both humans and AI to detect as
inauthentic. Regardless of authenticit y, the expe -
rience of harm to victims of non-consensual intimate images can be
devastatingly real—affecting their personal
and professional lives, and impacting their mental and physical
health.10
• A company installed AI-powered cameras in its delivery vans in order
to evaluate the road safety habits of its driv -
ers, but the system incorrectly penalized drivers when other cars cut
them off or when other events beyond
their control took place on the road. As a result, drivers were
incorrectly ineligible to receive a bonus.11
17
- >-
NOTICE &
EXPLANATION
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint
for the development of additional
technical standards and practices that are tailored for particular
sectors and contexts.
Tailored to the level of risk. An assessment should be done to determine
the level of risk of the auto -
mated system. In settings where the consequences are high as determined
by a risk assessment, or extensive
oversight is expected (e.g., in criminal justice or some public sector
settings), explanatory mechanisms should be built into the system design
so that the system’s full behavior can be explained in advance (i.e.,
only fully transparent models should be used), rather than as an
after-the-decision interpretation. In other settings, the extent of
explanation provided should be tailored to the risk level.
Valid. The explanation provided by a system should accurately reflect
the factors and the influences that led
to a particular decision, and should be meaningful for the particular
customization based on purpose, target, and level of risk. While
approximation and simplification may be necessary for the system to
succeed based on the explanatory purpose and target of the explanation,
or to account for the risk of fraud or other concerns related to
revealing decision-making information, such simplifications should be
done in a scientifically supportable way. Where appropriate based on the
explanatory system, error ranges for the explanation should be
calculated and included in the explanation, with the choice of
presentation of such information balanced with usability and overall
interface complexity concerns.
Demonstrate protections for notice and explanation
Reporting. Summary reporting should document the determinations made
based on the above consider -
ations, including: the responsible entities for accountability purposes;
the goal and use cases for the system, identified users, and impacted
populations; the assessment of notice clarity and timeliness; the
assessment of the explanation's validity and accessibility; the
assessment of the level of risk; and the account and assessment of how
explanations are tailored, including to the purpose, the recipient of
the explanation, and the level of risk. Individualized profile
information should be made readily available to the greatest extent
possible that includes explanations for any system impacts or
inferences. Reporting should be provided in a clear plain language and
machine-readable manner.
44
- source_sentence: >-
How does the document aim to bridge the gap between theoretical principles
and practical applications in the context of AI rights?
sentences:
- |-
FROM
PRINCIPLES
TO PRACTICE
A T ECHINCAL COMPANION TO
THE Blueprint for an
AI B ILL OF RIGHTS
12
- >-
3 the abuse, misuse, and unsafe repurposing by humans (adversarial or
not ), and others result
from interactions between a human and an AI system.
• Time scale: GAI risks may materialize abruptly or across extended
periods . Example s include
immediate (and/or prolonged) emotional harm and potential risks to
physical safety due to the
distribution of harmful deepfake images , or the lo ng-term effect of
disinformation on soci etal
trust in public institutions .
The presence of risks and where they fall along the dimensions above
will vary depending on the
characteristics of the GAI model , system, or use case at hand. These
characteristics include but are not
limited to GAI model or system architecture, training mechanisms and
libraries , data types used for
training or fine -tuning , levels of model access or availability of
model weights, and application or use
case context.
Organizations may choose to tailor how they measure GAI risks based
on these characteristics . They may
additionally wish to allocate risk management resources relative to the
severity and likelihood of
negative impact s, including where and how these risks manifest , and
their direct and material impacts
harms in the context of GAI use. Mitigations for model or system level
risks may differ from mitigations
for use-case or ecosystem level risks.
Importantly, some GAI risks are un known , and are therefore difficult to
properly scope or evaluate given
the uncertaint y about potential GAI scale, complexity, and
capabilities. Other risks may be known but
difficult to estimate given the wide range of GAI stakeholders, uses,
inputs, and outputs . Challenges with
risk estimation are aggravated by a lack of visibility into GAI training
data, and the generally immature
state of the science of AI measurement and safety today . This document
focuses on risks for which there
is an existing empirical evidence base at the time this profile was
written ; for example, speculative risks
that may potentially arise in more advanced, future GAI systems are not
considered . Future updates may
incorporate additional risks or provide further details on the risks
identified below.
To guide organizations in identifying and managing GAI risks, a set of
risks unique to or exacerbated by
the development and use of GAI are defined below.5 Each risk is labeled
according to the outcome ,
object, or source of the risk (i.e., some are risks “to ” a subject
or domain and others are risks “of” or
“from” an issue or theme ). These risks provide a lens through which
organizations can frame and execute
risk management efforts. To help streamline risk management efforts, each
risk is mapped in Section 3
(as well as in tables in Appendix B) to relevant Trustworthy AI
Characteristics identified in the AI RMF .
5 These risks can be further categorized by organizations depending on
their unique approaches to risk definition
and management. One possible way to further categorize these risks,
derived in part from the UK’s International
Scientific Report on the Safety of Advanced AI , could be: 1 ) Technical
/ Model risks (or risk from malfunction):
Confabulation; Dangerous or Violent Recommendations; Data Privacy; Value
Chain and Component Integration;
Harmful Bias, and Homogenization ; 2) Misuse by humans (or malicious
use): CBRN Information or Capabilities ;
Data Privacy; Human -AI Configuration; Obscene, Degrading, and/or Abusive
Content; Information Integrity;
Information Security; 3) Ecosystem / societal risks (or systemic risks)
: Data Privacy; Environmental; Intellectual
Property . We also note that some risks are cross -cutting between these
categories.
- >-
5 operations , or other cyberattacks ; increas ed attack surface for
targeted cyberattacks , which may
compromise a system’s availability or the confidentiality or integrity of
training data, code, or
model weights.
10. Intellectual Property: Eased production or replication of alleged
copyrighted, trademarked, or
licensed content without authorization (possibly in situations which do
not fall under fair use );
eased exposure of trade secrets; or plagiari sm or illegal replication
.
11. Obscen e, Degrading, and/or A busive Content : Eased production of
and access to obscene ,
degrading, and/or abusive imagery which can cause harm , including
synthetic child sexual abuse
material (CSAM) , and nonconsensual intimate images (NCII) of adults .
12. Value Chain and Component Integration : Non-transparent or
untraceable integration of
upstream third- party components, including data that has been
improperly obtained or not
processed and cleaned due to increased automation from GAI; improper
supplier vetting across
the AI lifecycle ; or other issues that diminish transparency or
accountability for downstream
users.
2.1. CBRN Information or Capabilities
In the future, GAI may enable malicious actors to more easily access
CBRN weapons and/or relevant
knowledge, information , materials, tools, or technologies that could be
misused to assist in the design,
development, production, or use of CBRN weapons or other dangerous
materials or agents . While
relevant biological and chemical threat knowledge and information is
often publicly accessible , LLMs
could facilitate its analysis or synthesis , particularly by
individuals without formal scientific training or
expertise.
Recent research on this topic found that LLM outputs regarding
biological threat creation and attack
planning pr ovided minima l assistance beyond traditional search
engine queries, suggesting that state -of-
the-art LLMs at the time these studies were conducted do not
substantially increase the operational
likelihood of such an attack. The physical synthesis development,
production, and use of chemical or
biological agents will continue to require both applicable expertise and
supporting materials and
infrastructure . The impact of GAI on chemical or biological agent
misuse will depend on what the key
barriers for malicious actors are (e.g., whether information access is
one such barrier ), and how well GAI
can help actors address those barriers .
Furthermore , chemical and biological design tools (BDTs) – highly
specialized AI systems trained on
scientific data that aid in chemical and biological design – may augment
design capabilities in chemistry
and biology beyond what text -based LLMs are able to provide . As these
models become more
efficacious , including for beneficial uses, it will be important to assess
their potential to be used for
harm, such as the ideation and design of novel harmful chemical or
biological agents .
While some of these described capabilities lie beyond the reach of
existing GAI tools, ongoing
assessments of this risk would be enhanced by monitoring both the
ability of AI tools to facilitate CBRN
weapons planning and GAI systems’ connection or access to relevant data
and tools .
Trustworthy AI Characteristic : Safe , Explainable and Interpretable
- source_sentence: >-
What are the key components that should be included in the ongoing
monitoring procedures for automated systems to ensure their performance
remains acceptable over time?
sentences:
- >-
AI B ILL OF RIGHTS
FFECTIVE SYSTEMS
ineffective systems. Automated systems should be
communities, stakeholders, and domain experts to identify
Systems should undergo pre-deployment testing, risk
that demonstrate they are safe and effective based on
including those beyond the intended use, and adherence to
protective measures should include the possibility of not
Automated systems should not be designed with an intent
reasonably foreseeable possibility of endangering your safety or the
safety of your communit y. They should
stemming from unintended, yet foreseeable, uses or
SECTION TITLE
BLUEPRINT FOR AN
SAFE AND E
You should be protected from unsafe or
developed with consultation from diverse
concerns, risks, and potential impacts of the system.
identification and mitigation, and ongoing monitoring
their intended use, mitigation of unsafe outcomes
domain-specific standards. Outcomes of these
deploying the system or removing a system from use.
or
be designed to proactively protect you from harms
impacts of automated systems. You should be protected from inappropriate
or irrelevant data use in the
design, development, and deployment of automated systems, and from the
compounded harm of its reuse.
Independent evaluation and reporting that confirms that the system is
safe and effective, including reporting of
steps taken to mitigate potential harms, should be performed and the
results made public whenever possible.
ALGORITHMIC DISCRIMINATION P ROTECTIONS
You should not face discrimination by algorithms and systems should be
used and designed in
an equitable way. Algorithmic discrimination occurs when automated
systems contribute to unjustified
different treatment or impacts disfavoring people based on their race,
color, ethnicity, sex (including
pregnancy, childbirth, and related medical conditions, gender identity,
intersex status, and sexual
orientation), religion, age, national origin, disability, veteran
status, genetic information, or any other
classification protected by law. Depending on the specific
circumstances, such algorithmic discrimination
may violate legal protections. Designers, developers, and deployers of
automated systems should take
proactive and continuous measures to protect individuals and communities
from algorithmic
discrimination and to use and design systems in an equitable way. This
protection should include proactive
equity assessments as part of the system design, use of representative
data and protection against proxies
for demographic features, ensuring accessibility for people with
disabilities in design and development,
pre-deployment and ongoing disparity testing and mitigation, and clear
organizational oversight. Independent
evaluation and plain language reporting in the form of an algorithmic
impact assessment, including
disparity testing results and mitigation information, should be
performed and made public whenever
possible to confirm these protections.
5
- >-
DATA PRIVACY
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint
for the development of additional
technical standards and practices that are tailored for particular
sectors and contexts.
In addition to the privacy expectations above for general non-sensitive
data, any system collecting, using, shar-
ing, or storing sensitive data should meet the expectations belo w.
Depending on the technological use case and
based on an ethical assessment, consent for sensitive data may need to
be acquired from a guardian and/or child.
Provide enhanced protections for data related to sensitive domains
Necessar y function s only . Sensitive data should only be used for
functions strictly necessary for that
domain or for functions that are required for administrative reasons
(e.g., school attendance records), unless
consent is acquired, if appropriate, and the additional expectations in
this section are met. Consent for non-
necessary functions should be optional, i.e., should not be required,
incentivized, or coerced in order to
receive opportunities or access to services. In cases where data is
provided to an entity (e.g., health insurance
company) in order to facilitate payment for such a need, that data
should only be used for that purpose.
Ethical review and use prohibitions. Any use of sensitive data or
decision process based in part on sensi-
tive data that might limit rights, opportunities, or access, whether the
decision is automated or not, should go
through a thorough ethical review and monitoring, both in advance and by
periodic review (e.g., via an indepen-
dent ethics committee or similarly robust process). In some cases, this
ethical review may determine that data
should not be used or shared for specific uses even with consent. Some
novel uses of automated systems in this
context, where the algorithm is dynamically developing and where the
science behind the use case is not well
established, may also count as human subject experimentation, and
require special review under organizational
compliance bodies applying medical, scientific, and academic human
subject experimentation ethics rules and
governance procedures.
Data quality. In sensitive domains, entities should be especially
careful to maintain the quality of data to
avoid adverse consequences arising from decision-making based on flawed
or inaccurate data. Such care is
necessary in a fragmented, complex data ecosystem and for datasets that
have limited access such as for fraud
prevention and law enforcement. It should be not left solely to
individuals to carry the burden of reviewing and
correcting data. Entities should conduct regula r, independent audits
and take prompt corrective measures to
maintain accurate, timel y, and complete data.
Limit access to sensitive data and derived data. Sensitive data and
derived data should not be sold,
shared, or made public as part of data brokerage or other agreements.
Sensitive data includes data that can be
used to infer sensitive information; even systems that are not directly
marketed as sensitive domain technologies
are expected to keep sensitive data private. Access to such data should
be limited based on necessity and based
on a principle of local control, such that those individuals closest to
the data subject have more access while
those who are less proximate do not (e.g., a teacher has access to their
students’ daily progress data while a
superintendent does not).
Reporting. In addition to the reporting on data privacy (as listed
above for non-sensitive data), entities devel-
oping technologies related to a sensitive domain and those collecting,
using, storing, or sharing sensitive data
should, whenever appropriate, regularly provide public reports
describing: any data security lapses or breaches
that resulted in sensitive data leaks; the numbe r, type, and outcomes
of ethical pre-reviews undertaken; a
description of any data sold, shared, or made public, and how that data
was assessed to determine it did not pres-
ent a sensitive data risk; and ongoing risk identification and
management procedures, and any mitigation added
based on these procedures. Reporting should be provided in a clear and
machine-readable manne r.
38
- >-
SAFE AND EFFECTIVE
SYSTEMS
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint
for the development of additional
technical standards and practices that are tailored for particular
sectors and contexts.
Ongoing monitoring. Automated systems should have ongoing monitoring
procedures, including recalibra -
tion procedures, in place to ensure that their performance does not fall
below an acceptable level over time,
based on changing real-world conditions or deployment contexts,
post-deployment modification, or unexpect -
ed conditions. This ongoing monitoring should include continuous
evaluation of performance metrics and harm assessments, updates of any
systems, and retraining of any machine learning models as necessary, as
well as ensuring that fallback mechanisms are in place to allow
reversion to a previously working system. Monitor
-
ing should take into account the performance of both technical system
components (the algorithm as well as any hardware components, data
inputs, etc.) and human operators. It should include mechanisms for
testing the actual accuracy of any predictions or recommendations
generated by a system, not just a human operator’s determination of
their accuracy. Ongoing monitoring procedures should include manual,
human-led monitor
-
ing as a check in the event there are shortcomings in automated
monitoring systems. These monitoring proce -
dures should be in place for the lifespan of the deployed automated
system.
Clear organizational oversight. Entities responsible for the development
or use of automated systems should lay out clear governance structures
and procedures. This includes clearly-stated governance proce
-
dures before deploying the system, as well as responsibility of specific
individuals or entities to oversee ongoing assessment and mitigation.
Organizational stakeholders including those with oversight of the
business process or operation being automated, as well as other
organizational divisions that may be affected due to the use of the
system, should be involved in establishing governance procedures.
Responsibility should rest high enough in the organization that
decisions about resources, mitigation, incident response, and potential
rollback can be made promptly, with sufficient weight given to risk
mitigation objectives against competing concerns. Those holding this
responsibility should be made aware of any use cases with the potential
for meaningful impact on people’s rights, opportunities, or access as
determined based on risk identification procedures. In some cases, it
may be appropriate for an independent ethics review to be conducted
before deployment.
Avoid inappropriate, low-quality, or irrelevant data use and the
compounded harm of its reuse
Relevant and high-quality data. Data used as part of any automated
system’s creation, evaluation, or
deployment should be relevant, of high quality, and tailored to the task
at hand. Relevancy should be
established based on research-backed demonstration of the causal
influence of the data to the specific use case
or justified more generally based on a reasonable expectation of
usefulness in the domain and/or for the
system design or ongoing development. Relevance of data should not be
established solely by appealing to
its historical connection to the outcome. High quality and tailored data
should be representative of the task at
hand and errors from data entry or other sources should be measured and
limited. Any data used as the target
of a prediction process should receive particular attention to the
quality and validity of the predicted outcome
or label to ensure the goal of the automated system is appropriately
identified and measured. Additionally ,
justification should be documented for each data attribute and source to
explain why it is appropriate to use
that data to inform the results of the automated system and why such use
will not violate any applicable laws.
In cases of high-dimensional and/or derived attributes, such
justifications can be provided as overall
descriptions of the attribute generation process and appropriateness.
19
- source_sentence: >-
What are the key principles and frameworks mentioned in the white paper
that govern the implementation of AI in national security and defense
activities?
sentences:
- >-
APPENDIX
• OSTP conducted meetings with a variety of stakeholders in the private
sector and civil society. Some of these
meetings were specifically focused on providing ideas related to the
development of the Blueprint for an AI
Bill of Rights while others provided useful general context on the
positive use cases, potential harms, and/or
oversight possibilities for these technologies. Participants in these
conversations from the private sector and
civil society included:
Adobe
American Civil Liberties Union (ACLU) The Aspen Commission on
Information Disorder The Awood Center The Australian Human Rights
Commission Biometrics Institute The Brookings Institute BSA | The
Software Alliance Cantellus Group Center for American Progress Center
for Democracy and Technology Center on Privacy and Technology at
Georgetown Law Christiana Care Color of Change Coworker Data Robot Data
Trust Alliance Data and Society Research Institute Deepmind EdSAFE AI
Alliance Electronic Privacy Information Center (EPIC) Encode Justice
Equal AI Google Hitachi's AI Policy Committee The Innocence Project
Institute of Electrical and Electronics Engineers (IEEE) Intuit Lawyers
Committee for Civil Rights Under Law Legal Aid Society The Leadership
Conference on Civil and Human Rights Meta Microsoft The MIT AI Policy
Forum Movement Alliance Project The National Association of Criminal
Defense Lawyers O’Neil Risk Consulting & Algorithmic Auditing The
Partnership on AI Pinterest The Plaintext Group pymetrics SAP The
Security Industry Association Software and Information Industry
Association (SIIA) Special Competitive Studies Project Thorn United for
Respect University of California at Berkeley Citris Policy Lab
University of California at Berkeley Labor Center Unfinished/Project
Liberty Upturn US Chamber of Commerce US Chamber of Commerce Technology
Engagement Center
A.I. Working Group
Vibrent HealthWarehouse Worker ResourceCenterWaymap
62
- >-
This white paper recognizes that national security (which includes
certain law enforcement and
homeland security activities) and defense activities are of increased
sensitivity and interest to our nation’s
adversaries and are often subject to special requirements, such as those
governing classified information and
other protected data. Such activities require alternative, compatible
safeguards through existing policies that
govern automated systems and AI, such as the Department of Defense (DOD)
AI Ethical Principles and
Responsible AI Implementation Pathway and the Intelligence Community
(IC) AI Ethics Principles and
Framework. The implementation of these policies to national security and
defense activities can be informed by
the Blueprint for an AI Bill of Rights where feasible.
The Blueprint for an AI Bill of Rights is not intended to, and does not,
create any legal right, benefit, or
defense, substantive or procedural, enforceable at law or in equity by
any party against the United States, its
departments, agencies, or entities, its officers, employees, or agents,
or any other person, nor does it constitute a
waiver of sovereign immunity.
Copyright Information
This document is a work of the United States Government and is in the
public domain (see 17 U.S.C. §105).
2
- >-
This white paper recognizes that national security (which includes
certain law enforcement and
homeland security activities) and defense activities are of increased
sensitivity and interest to our nation’s
adversaries and are often subject to special requirements, such as those
governing classified information and
other protected data. Such activities require alternative, compatible
safeguards through existing policies that
govern automated systems and AI, such as the Department of Defense (DOD)
AI Ethical Principles and
Responsible AI Implementation Pathway and the Intelligence Community
(IC) AI Ethics Principles and
Framework. The implementation of these policies to national security and
defense activities can be informed by
the Blueprint for an AI Bill of Rights where feasible.
The Blueprint for an AI Bill of Rights is not intended to, and does not,
create any legal right, benefit, or
defense, substantive or procedural, enforceable at law or in equity by
any party against the United States, its
departments, agencies, or entities, its officers, employees, or agents,
or any other person, nor does it constitute a
waiver of sovereign immunity.
Copyright Information
This document is a work of the United States Government and is in the
public domain (see 17 U.S.C. §105).
2
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7222222222222222
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9444444444444444
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7222222222222222
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.31481481481481477
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999993
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999996
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7222222222222222
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9444444444444444
name: Cosine Recall@3
- type: cosine_recall@5
value: 1
name: Cosine Recall@5
- type: cosine_recall@10
value: 1
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.87665680931096
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8348765432098766
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8348765432098766
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.7222222222222222
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.9444444444444444
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.9814814814814815
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7222222222222222
name: Dot Precision@1
- type: dot_precision@3
value: 0.31481481481481477
name: Dot Precision@3
- type: dot_precision@5
value: 0.1962962962962962
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999996
name: Dot Precision@10
- type: dot_recall@1
value: 0.7222222222222222
name: Dot Recall@1
- type: dot_recall@3
value: 0.9444444444444444
name: Dot Recall@3
- type: dot_recall@5
value: 0.9814814814814815
name: Dot Recall@5
- type: dot_recall@10
value: 1
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8752777468856755
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8333333333333334
name: Dot Mrr@10
- type: dot_map@100
value: 0.8333333333333334
name: Dot Map@100
SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5
This is a sentence-transformers model finetuned from Alibaba-NLP/gte-large-en-v1.5. It maps sentences & paragraphs to a 1024-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: Alibaba-NLP/gte-large-en-v1.5
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1024 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': 8192, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 1024, '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})
)
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("sentence_transformers_model_id")
# Run inference
sentences = [
'What are the key principles and frameworks mentioned in the white paper that govern the implementation of AI in national security and defense activities?',
'This white paper recognizes that national security (which includes certain law enforcement and \nhomeland security activities) and defense activities are of increased sensitivity and interest to our nation’s \nadversaries and are often subject to special requirements, such as those governing classified information and \nother protected data. Such activities require alternative, compatible safeguards through existing policies that \ngovern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and \nResponsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and \nFramework. The implementation of these policies to national security and defense activities can be informed by \nthe Blueprint for an AI Bill of Rights where feasible. \nThe Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or \ndefense, substantive or procedural, enforceable at law or in equity by any party against the United States, its \ndepartments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a \nwaiver of sovereign immunity. \nCopyright Information \nThis document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). \n2',
"APPENDIX\n• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these\nmeetings were specifically focused on providing ideas related to the development of the Blueprint for an AI\nBill of Rights while others provided useful general context on the positive use cases, potential harms, and/or\noversight possibilities for these technologies. Participants in these conversations from the private sector and\ncivil society included:\nAdobe \nAmerican Civil Liberties Union (ACLU) The Aspen Commission on Information Disorder The Awood Center The Australian Human Rights Commission Biometrics Institute The Brookings Institute BSA | The Software Alliance Cantellus Group Center for American Progress Center for Democracy and Technology Center on Privacy and Technology at Georgetown Law Christiana Care Color of Change Coworker Data Robot Data Trust Alliance Data and Society Research Institute Deepmind EdSAFE AI Alliance Electronic Privacy Information Center (EPIC) Encode Justice Equal AI Google Hitachi's AI Policy Committee The Innocence Project Institute of Electrical and Electronics Engineers (IEEE) Intuit Lawyers Committee for Civil Rights Under Law Legal Aid Society The Leadership Conference on Civil and Human Rights Meta Microsoft The MIT AI Policy Forum Movement Alliance Project The National Association of Criminal Defense Lawyers O’Neil Risk Consulting & Algorithmic Auditing The Partnership on AI Pinterest The Plaintext Group pymetrics SAP The Security Industry Association Software and Information Industry Association (SIIA) Special Competitive Studies Project Thorn United for Respect University of California at Berkeley Citris Policy Lab University of California at Berkeley Labor Center Unfinished/Project Liberty Upturn US Chamber of Commerce US Chamber of Commerce Technology Engagement Center \nA.I. Working Group\nVibrent HealthWarehouse Worker ResourceCenterWaymap\n62",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Evaluation
Metrics
Information Retrieval
- Evaluated with
InformationRetrievalEvaluator
| Metric | Value |
|---|---|
| cosine_accuracy@1 | 0.7222 |
| cosine_accuracy@3 | 0.9444 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.7222 |
| cosine_precision@3 | 0.3148 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.7222 |
| cosine_recall@3 | 0.9444 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.8767 |
| cosine_mrr@10 | 0.8349 |
| cosine_map@100 | 0.8349 |
| dot_accuracy@1 | 0.7222 |
| dot_accuracy@3 | 0.9444 |
| dot_accuracy@5 | 0.9815 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.7222 |
| dot_precision@3 | 0.3148 |
| dot_precision@5 | 0.1963 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.7222 |
| dot_recall@3 | 0.9444 |
| dot_recall@5 | 0.9815 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.8753 |
| dot_mrr@10 | 0.8333 |
| dot_map@100 | 0.8333 |
Training Details
Training Dataset
Unnamed Dataset
- Size: 224 training samples
- Columns:
sentence_0andsentence_1 - Approximate statistics based on the first 224 samples:
sentence_0 sentence_1 type string string details - min: 23 tokens
- mean: 36.01 tokens
- max: 55 tokens
- min: 22 tokens
- mean: 569.67 tokens
- max: 1018 tokens
- Samples:
sentence_0 sentence_1 What are the primary objectives outlined in the "Blueprint for an AI Bill of Rights" as it pertains to the American people?BLUEPRINT FOR AN
AI B ILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022In what ways does the document propose to ensure that automated systems are designed to work effectively for the benefit of society?BLUEPRINT FOR AN
AI B ILL OF
RIGHTS
MAKING AUTOMATED
SYSTEMS WORK FOR
THE AMERICAN PEOPLE
OCTOBER 2022What is the primary purpose of the Blueprint for an AI Bill of Rights as outlined by the White House Office of Science and Technology Policy?About this Document
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was
published by the White House Office of Science and Technology Policy in October 2022. This framework was
released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered
world.” Its release follows a year of public engagement to inform this initiative. The framework is available
online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights
About the Office of Science and Technology Policy
The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology
Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office
of the President with advice on the scientific, engineering, and technological aspects of the economy, national
security, health, foreign relations, the environment, and the technological recovery and use of resources, among
other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of
Management and Budget (OMB) with an annual review and analysis of Federal research and development in
budgets, and serves as a source of scientific and technological analysis and judgment for the President with
respect to major policies, plans, and programs of the Federal Government.
Legal Disclaimer
The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People is a white paper
published by the White House Office of Science and Technology Policy. It is intended to support the
development of policies and practices that protect civil rights and promote democratic values in the building,
deployment, and governance of automated systems.
The Blueprint for an AI Bill of Rights is non-binding and does not constitute U.S. government policy. It
does not supersede, modify, or direct an interpretation of any existing statute, regulation, policy, or
international instrument. It does not constitute binding guidance for the public or Federal agencies and
therefore does not require compliance with the principles described herein. It also is not determinative of what
the U.S. government’s position will be in any international negotiation. Adoption of these principles may not
meet the requirements of existing statutes, regulations, policies, or international instruments, or the
requirements of the Federal agencies that enforce them. These principles are not intended to, and do not,
prohibit or limit any lawful activity of a government agency, including law enforcement, national security, or
intelligence activities.
The appropriate application of the principles set forth in this white paper depends significantly on the
context in which automated systems are being utilized. In some circumstances, application of these principles
in whole or in part may not be appropriate given the intended use of automated systems to achieve government
agency missions. Future sector-specific guidance will likely be necessary and important for guiding the use of
automated systems in certain settings such as AI systems used as part of school building security or automated
health diagnostic systems.
The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of
equities, for example, between the protection of sensitive law enforcement information and the principle of
notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and
other law enforcement equities. Even in contexts where these principles may not apply in whole or in part,
federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as
existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960,
Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020).
This white paper recognizes that national security (which includes certain law enforcement and
homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s
adversaries and are often subject to special requirements, such as those governing classified information and
other protected data. Such activities require alternative, compatible safeguards through existing policies that
govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and
Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and
Framework. The implementation of these policies to national security and defense activities can be informed by
the Blueprint for an AI Bill of Rights where feasible. - Loss:
MultipleNegativesRankingLosswith these parameters:{ "scale": 20.0, "similarity_fct": "cos_sim" }
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy: stepsper_device_train_batch_size: 5per_device_eval_batch_size: 5num_train_epochs: 2multi_dataset_batch_sampler: round_robin
All Hyperparameters
Click to expand
overwrite_output_dir: Falsedo_predict: Falseeval_strategy: stepsprediction_loss_only: Trueper_device_train_batch_size: 5per_device_eval_batch_size: 5per_gpu_train_batch_size: Noneper_gpu_eval_batch_size: Nonegradient_accumulation_steps: 1eval_accumulation_steps: Nonetorch_empty_cache_steps: Nonelearning_rate: 5e-05weight_decay: 0.0adam_beta1: 0.9adam_beta2: 0.999adam_epsilon: 1e-08max_grad_norm: 1num_train_epochs: 2max_steps: -1lr_scheduler_type: linearlr_scheduler_kwargs: {}warmup_ratio: 0.0warmup_steps: 0log_level: passivelog_level_replica: warninglog_on_each_node: Truelogging_nan_inf_filter: Truesave_safetensors: Truesave_on_each_node: Falsesave_only_model: Falserestore_callback_states_from_checkpoint: Falseno_cuda: Falseuse_cpu: Falseuse_mps_device: Falseseed: 42data_seed: Nonejit_mode_eval: Falseuse_ipex: Falsebf16: Falsefp16: Falsefp16_opt_level: O1half_precision_backend: autobf16_full_eval: Falsefp16_full_eval: Falsetf32: Nonelocal_rank: 0ddp_backend: Nonetpu_num_cores: Nonetpu_metrics_debug: Falsedebug: []dataloader_drop_last: Falsedataloader_num_workers: 0dataloader_prefetch_factor: Nonepast_index: -1disable_tqdm: Falseremove_unused_columns: Truelabel_names: Noneload_best_model_at_end: Falseignore_data_skip: Falsefsdp: []fsdp_min_num_params: 0fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}fsdp_transformer_layer_cls_to_wrap: Noneaccelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}deepspeed: Nonelabel_smoothing_factor: 0.0optim: adamw_torchoptim_args: Noneadafactor: Falsegroup_by_length: Falselength_column_name: lengthddp_find_unused_parameters: Noneddp_bucket_cap_mb: Noneddp_broadcast_buffers: Falsedataloader_pin_memory: Truedataloader_persistent_workers: Falseskip_memory_metrics: Trueuse_legacy_prediction_loop: Falsepush_to_hub: Falseresume_from_checkpoint: Nonehub_model_id: Nonehub_strategy: every_savehub_private_repo: Falsehub_always_push: Falsegradient_checkpointing: Falsegradient_checkpointing_kwargs: Noneinclude_inputs_for_metrics: Falseeval_do_concat_batches: Truefp16_backend: autopush_to_hub_model_id: Nonepush_to_hub_organization: Nonemp_parameters:auto_find_batch_size: Falsefull_determinism: Falsetorchdynamo: Noneray_scope: lastddp_timeout: 1800torch_compile: Falsetorch_compile_backend: Nonetorch_compile_mode: Nonedispatch_batches: Nonesplit_batches: Noneinclude_tokens_per_second: Falseinclude_num_input_tokens_seen: Falseneftune_noise_alpha: Noneoptim_target_modules: Nonebatch_eval_metrics: Falseeval_on_start: Falseeval_use_gather_object: Falsebatch_sampler: batch_samplermulti_dataset_batch_sampler: round_robin
Training Logs
| Epoch | Step | cosine_map@100 |
|---|---|---|
| 1.0 | 45 | 0.8179 |
| 1.1111 | 50 | 0.8318 |
| 2.0 | 90 | 0.8349 |
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: 3.0.1
- 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}
}