Model: lw2134/policy_gte_large_7
Author: lw2134
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, onnx, safetensors, new, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:700, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, custom_code, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Alibaba-NLP/gte-large-en-v1.5, base_model:quantized:Alibaba-NLP/gte-large-en-v1.5, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (quantized): id: Alibaba-NLP/gte-large-en-v1.5
Card 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:700, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What are the expectations for automated systems in relation to data privacy?
sentences: https://beta.nsf.gov/funding/opportunities/designing-accountable-software-systems-dass
28. The Leadership Conference Education Fund. The Use Of Pretrial “Risk Assessment” Instruments: A
Shared Statement Of Civil Rights Concerns. Jul. 30, 2018. http://civilrightsdocs.info/pdf/criminal-justice/
Pretrial-Risk-Assessment-Short.pdf; https://civilrights.org/edfund/pretrial-risk-assessments/, 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-, standing that it may be these users who are most likely to need the human assistance. Similarly, it should be 
tested to ensure that users with disabilities are able to find and use human consideration and fallback and also 
request reasonable accommodations or modifications. 
Convenient. Mechanisms for human consideration and fallback should not be unreasonably burdensome as 
compared to the automated system’s equivalent. 
49, source_sentence: What is the purpose of the U.S. AI Safety Institute and the AI Safety Institute Consortium established by NIST?
sentences: 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, 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, differ from an explanation provided to allow for the possibility of recourse, an appeal, or one provided in the 
context of a dispute or contestation process. For the purposes of this framework, 'explanation' should be 
construed broadly. An explanation need not be a plain-language statement about causality but could consist of 
any mechanism that allows the recipient to build the necessary understanding and intuitions to achieve the, source_sentence: What are the consequences faced by individuals when they are unable to reach a human decision-maker in automated systems?
sentences: ENDNOTES
85. Mick Dumke and Frank Main. A look inside the watch list Chicago police fought to keep secret. The
Chicago Sun Times. May 18, 2017.
https://chicago.suntimes.com/2017/5/18/18386116/a-look-inside-the-watch-list-chicago-police-fought­
to-keep-secret, presented with no alternative, or are forced to endure a cumbersome process to reach a human decision-maker once 
they decide they no longer want to deal exclusively with the automated system or be impacted by its results. As a result 
of this lack of human reconsideration, many receive delayed access, or lose access, to rights, opportunities, benefits, 
and critical services. The American public deserves the assurance that, when rights, opportunities, or access are, compliance in mind. 
Some state legislatures have placed strong transparency and validity requirements on 
the use of pretrial risk assessments. The use of algorithmic pretrial risk assessments has been a 
cause of concern for civil rights groups.28 Idaho Code Section 19-1910, enacted in 2019,29 requires that any 
pretrial risk assessment, before use in the state, first be "shown to be free of bias against any class of, source_sentence: What organizations are mentioned in the appendix alongside individuals such as Lisa Feldman Barrett and Madeline Owens?
sentences: APPENDIX
Lisa Feldman Barrett 
Madeline Owens 
Marsha Tudor 
Microsoft Corporation 
MITRE Corporation 
National Association for the 
Advancement of Colored People 
Legal Defense and Educational 
Fund 
National Association of Criminal 
Defense Lawyers 
National Center for Missing & 
Exploited Children 
National Fair Housing Alliance 
National Immigration Law Center 
NEC Corporation of America, 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, ers and other experts across fields and sectors, as well as policymakers throughout the Federal government—on 
the issue of algorithmic and data-driven harms and potential remedies. Through panel discussions, public listen-
ing sessions, meetings, a formal request for information, and input to a publicly accessible and widely-publicized 
email address, people throughout the United States, public servants across Federal agencies, and members of the, source_sentence: What should individuals or organizations provide to ensure that people impacted by an automated system are informed about significant changes in use cases or key functionalities?
sentences: with an intent or reasonably foreseeable possibility of endangering 
your safety or the safety of your community. They should be designed 
to proactively protect you from harms stemming from unintended, 
yet foreseeable, uses or impacts of automated systems. You should be 
protected from inappropriate or irrelevant data use in the design, de­
velopment, and deployment of automated systems, and from the 
compounded harm of its reuse. Independent evaluation and report­, use, the individual or organization responsible for the system, and ex­
planations of outcomes that are clear, timely, and accessible. Such 
notice should be kept up-to-date and people impacted by the system 
should be notified of significant use case or key functionality chang­
es. You should know how and why an outcome impacting you was de­
termined by an automated system, including when the automated, software-algorithms-and-artificial-intelligence; U.S. Department of Justice. Algorithms, Artificial
Intelligence, and Disability Discrimination in Hiring. May 12, 2022. https://beta.ada.gov/resources/ai­
guidance/
54. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. Dissecting racial bias in
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: 0.8666666666666667
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9866666666666667
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 1
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8666666666666667
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3288888888888888
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19999999999999996
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8666666666666667
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9866666666666667
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 1
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9481205912028868
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.93
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.93
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8666666666666667
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 1
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 1
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8666666666666667
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.33333333333333326
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19999999999999996
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8666666666666667
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 1
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 1
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9490449037619082
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9311111111111112
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.931111111111111
name: Dot Map@100
verified: no
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Created: 2024-10-04 02:41:14