Model: deman539/nomic-embed-text-v1
Author: deman539
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, nomic_bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:2459, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, custom_code, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:nomic-ai/nomic-embed-text-v1, base_model:finetune:nomic-ai/nomic-embed-text-v1, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: nomic-ai/nomic-embed-text-v1
Card metadata: base_model: nomic-ai/nomic-embed-text-v1
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:2459, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: What types of applications may require confidentiality during their launch?
sentences: Taken together, the technical protections and practices laid out in the Blueprint for an AI Bill of Rights can help 
guard the American public against many of the potential and actual harms identified by researchers, technolo­
gists, advocates, journalists, policymakers, and communities in the United States and around the world. This 
technical companion is intended to be used as a reference by people across many circumstances – anyone, deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use. 
Action ID 
Suggested Action 
GAI Risks 
MG-2.4-001 
Establish and maintain communication plans to inform AI stakeholders as part of 
the deactivation or disengagement process of a speciﬁc GAI system (including for 
open-source models) or context of use, including reasons, workarounds, user 
access removal, alternative processes, contact information, etc. 
Human-AI Conﬁguration, launch may need to be confidential. Government applications, particularly law enforcement applications or 
applications that raise national security considerations, may require confidential or limited engagement based 
on system sensitivities and preexisting oversight laws and structures. Concerns raised in this consultation 
should be documented, and the automated system developers were proposing to create, use, or deploy should 
be reconsidered based on this feedback., source_sentence: What is the main focus of the paper by Chandra et al. (2023) regarding Chinese influence operations?
sentences: 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, 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, 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, source_sentence: What steps are being taken to enhance transparency and accountability in the GAI system?
sentences: 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, 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, content. Some well-known techniques for provenance data tracking include digital watermarking, 
metadata recording, digital ﬁngerprinting, and human authentication, among others. 
Provenance Data Tracking Approaches 
Provenance data tracking techniques for GAI systems can be used to track the history and origin of data 
inputs, metadata, and synthetic content. Provenance data tracking records the origin and history for, source_sentence: What are some examples of mechanisms for human consideration and fallback mentioned in the context?
sentences: consequences resulting from the utilization of content provenance approaches on users and 
communities. Furthermore, organizations can track and document the provenance of datasets to identify 
instances in which AI-generated data is a potential root cause of performance issues with the GAI 
system. 
A.1.8. Incident Disclosure 
Overview 
AI incidents can be deﬁned as an “event, circumstance, or series of events where the development, use,, fully impact rights, opportunities, or access. Automated systems that have greater control over outcomes, 
provide input to high-stakes decisions, relate to sensitive domains, or otherwise have a greater potential to 
meaningfully impact rights, opportunities, or access should have greater availability (e.g., staffing) and over­
sight of human consideration and fallback mechanisms. 
Accessible. Mechanisms for human consideration and fallback, whether in-person, on paper, by phone, or, •
Frida Polli, CEO, Pymetrics
•
Karen Levy, Assistant Professor, Department of Information Science, Cornell University
•
Natasha Duarte, Project Director, Upturn
•
Elana Zeide, Assistant Professor, University of Nebraska College of Law
•
Fabian Rogers, Constituent Advocate, Office of NY State Senator Jabari Brisport and Community
Advocate and Floor Captain, Atlantic Plaza Towers Tenants Association, source_sentence: What mental health issues are associated with the increased use of technologies in schools and workplaces?
sentences: but this approach may still produce harmful recommendations in response to other less-explicit, novel 
prompts (also relevant to CBRN Information or Capabilities, Data Privacy, Information Security, and 
Obscene, Degrading and/or Abusive Content). Crafting such prompts deliberately is known as 
“jailbreaking,” or, manipulating prompts to circumvent output controls. Limitations of GAI systems can be, external use, narrow vs. broad application scope, ﬁne-tuning, and varieties of 
data sources (e.g., grounding, retrieval-augmented generation). 
Data Privacy; Intellectual 
Property, 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, anxiety, 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,
model-index: name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.8584142394822006
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9838187702265372
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9951456310679612
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9991909385113269
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8584142394822006
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.32793959007551243
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.1990291262135922
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09991909385113268
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8584142394822006
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9838187702265372
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9951456310679612
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9991909385113269
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9417951214306157
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9220443571171728
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9221065926163013
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8584142394822006
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9838187702265372
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.9951456310679612
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.9991909385113269
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8584142394822006
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.32793959007551243
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.1990291262135922
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09991909385113268
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8584142394822006
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9838187702265372
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.9951456310679612
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.9991909385113269
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9417951214306157
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9220443571171728
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9221065926163013
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 255
Created: 2024-09-23 14:27:55