Model: Technocoloredgeek/midterm-finetuned-embedding
Author: Technocoloredgeek
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:1539, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, arxiv:1908.10084, arxiv:2205.13147, arxiv:1705.00652, base_model:Snowflake/snowflake-arctic-embed-m, base_model:finetune:Snowflake/snowflake-arctic-embed-m, model-index, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Snowflake/snowflake-arctic-embed-m
Card metadata: base_model: Snowflake/snowflake-arctic-embed-m
library_name: sentence-transformers
metrics: cosine_accuracy@1, cosine_accuracy@3, cosine_accuracy@5, cosine_accuracy@10, cosine_precision@1, cosine_precision@3, cosine_precision@5, cosine_precision@10, cosine_recall@1, cosine_recall@3, cosine_recall@5, cosine_recall@10, cosine_ndcg@10, cosine_mrr@10, cosine_map@100, dot_accuracy@1, dot_accuracy@3, dot_accuracy@5, dot_accuracy@10, dot_precision@1, dot_precision@3, dot_precision@5, dot_precision@10, dot_recall@1, dot_recall@3, dot_recall@5, dot_recall@10, dot_ndcg@10, dot_mrr@10, dot_map@100
pipeline_tag: sentence-similarity
tags: sentence-transformers, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:1539, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How do the models ensure the production of valid, reliable, and factually accurate outputs while assessing risks associated with content provenance and offensive cyber activities?
sentences: Information or Capabilities  
MS-1.1-0 05 Evaluate novel methods and technologies for the measurement of GAI-related 
risks in cluding in  content provenance , oﬀensive cy ber, and CBRN , while 
maintaining the models’ ability to produce valid, reliable, and factually accurate outputs.  Information Integrity ; CBRN 
Information or Capabilities ; 
Obscene, Degrading, and/or Abusive Content, Testing. Systems should undergo extensive testing before deployment. This testing should follow domain-specific best practices, when available, for ensuring the technology will work in its real-world context. Such testing should take into account both the specific technology used and the roles of any human operators or reviewers who impact system outcomes or effectiveness; testing should include both automated systems testing and human-led (manual) testing. Testing conditions should mirror as, 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-, source_sentence: How should automated systems handle user data in terms of collection and user consent according to the provided context?
sentences: Property Appraisal and Valuation Equity: Closing the Racial Wealth Gap by Addressing Mis-valuations for
Families and Communities of Color. March 2022. https://pave.hud.gov/sites/pave.hud.gov/files/
documents/PAVEActionPlan.pdf
53. U.S. Equal Employment Opportunity Commission. The Americans with Disabilities Act and the Use of
Software, Algorithms, and Artificial Intelligence to Assess Job Applicants and Employees . EEOC-, 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, privacy through design choices that ensure such protections are included by default, including ensuring that data collection conforms to reasonable expectations and that only data strictly necessary for the specific context is collected. Designers, developers, and deployers of automated systems should seek your permission 
and respect your decisions regarding collection, use, access, transfer, and deletion of your data in appropriate, source_sentence: How many participants attended the listening sessions organized for members of the public?
sentences: 37 MS-2.11-0 05 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, lenders who may be avoiding serving communities of color are conducting targeted marketing and advertising.51 
This initiative will draw upon strong partnerships across federal agencies, including the Consumer Financial 
Protection Bureau and prudential regulators. The Action Plan to Advance Property Appraisal and Valuation 
Equity includes a commitment from the agencies that oversee mortgage lending to include a, for members of the public. The listening sessions together drew upwards of 300 participants. The Science and
Technology Policy Institute produced a synopsis of both the RFI submissions and the feedback at the listeningsessions.
115
61, source_sentence: Why is it particularly important to monitor the risks of confabulated content when integrating Generative AI (GAI) into applications that involve consequential decision making?
sentences: of how and what the technologies are doing. Some panelists suggested that technology should be used to help people receive benefits, e.g., by pushing benefits to those in need and ensuring automated decision-making systems are only used to provide a positive outcome; technology shouldn't be used to take supports away from people who need them., 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, 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 p lans and actions include: 
• Accessibility and reasonable accommodations  
• AI actor credentials and qualiﬁcations  
• Alignment to organizational values  • Auditing and assessment, source_sentence: How does the framework address the concerns related to the rapid innovation and changing definitions of AI systems?
sentences: or inequality. Assessment could include both qualitative and quantitative evaluations of the system. This equity assessment should also be considered a core part of the goals of the consultation conducted as part of the safety and efficacy review., 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 r easons, workarounds, user 
access removal, alternative processes, contact information, etc.  Human -AI Conﬁguration, SECTION  TITLE
Applying The Blueprint for an AI Bill of Rights 
While many of the concerns addressed in this framework derive from the use of AI, the technical 
capabilities and specific definitions of such systems change with the speed of innovation, and the potential 
harms of their use occur even with less technologically sophisticated tools. Thus, this framework uses a two-
part test to determine what systems are in scope. This framework applies to (1) automated systems that (2)
model-index: name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results: task: type: information-retrieval
name: Information Retrieval
dataset: name: Unknown
type: unknown
metrics: type: cosine_accuracy@1
value: 0.9270833333333334
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9947916666666666
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: 0.9270833333333334
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.33159722222222227
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.9270833333333334
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9947916666666666
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: 0.969317939271961
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9587673611111113
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9587673611111112
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.9270833333333334
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9947916666666666
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: 0.9270833333333334
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.33159722222222227
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19999999999999998
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999999
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.9270833333333334
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9947916666666666
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: 0.969317939271961
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9587673611111113
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9587673611111112
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 12
Created: 2024-09-20 21:27:31