Model: Sleem247/snowflake_finetuned_semantic-Q8_0-GGUF
Author: Sleem247
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, gguf, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:714, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, llama-cpp, gguf-my-repo, base_model:jet-taekyo/snowflake_finetuned_semantic, base_model:quantized:jet-taekyo/snowflake_finetuned_semantic, model-index, endpoints_compatible, region:us
Base models (quantized): id: jet-taekyo/snowflake_finetuned_semantic
Card metadata: base_model: jet-taekyo/snowflake_finetuned_semantic
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:714, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss, llama-cpp, gguf-my-repo
widget: source_sentence: What are some examples of data privacy issues mentioned in the context?
sentences: 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 number, 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, DATA PRIVACY 
HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE
Real-life examples of how these principles can become reality, through laws, policies, and practical 
technical and sociotechnical approaches to protecting rights, opportunities, and access. 
The Privacy Act of 1974 requires privacy protections for personal information in federal 
records systems, including limits on data retention, and also provides individuals a general 
right to access and correct their data. Among other things, the Privacy Act limits the storage of individual 
information in federal systems of records, illustrating the principle of limiting the scope of data retention. Under 
the Privacy Act, federal agencies may only retain data about an individual that is “relevant and necessary” to 
accomplish an agency’s statutory purpose or to comply with an Executive Order of the President. The law allows, 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. 
•
An insurer might collect data from a person's social media presence as part of deciding what life
insurance rates they should be offered.64
•
A data broker harvested large amounts of personal data and then suffered a breach, exposing hundreds of
thousands of people to potential identity theft. 65
•
A local public housing authority installed a facial recognition system at the entrance to housing complexes to
assist law enforcement with identifying individuals viewed via camera when police reports are filed, leading
the community, both those living in the housing complex and not, to have videos of them sent to the local
police department and made available for scanning by its facial recognition software.66
•, source_sentence: What are the main topics covered in the National Institute of Standards and Technology's AI Risk Management Framework?
sentences: https://www.rand.org/pubs/research_reports/RRA2977-2.html. 
Nicoletti, L. et al. (2023) Humans Are Biased. Generative Ai Is Even Worse. Bloomberg. 
https://www.bloomberg.com/graphics/2023-generative-ai-bias/. 
National Institute of Standards and Technology (2024) Adversarial Machine Learning: A Taxonomy and 
Terminology of Attacks and Mitigations https://csrc.nist.gov/pubs/ai/100/2/e2023/ﬁnal 
National Institute of Standards and Technology (2023) AI Risk Management Framework. 
https://www.nist.gov/itl/ai-risk-management-framework 
National Institute of Standards and Technology (2023) AI Risk Management Framework, Chapter 3: AI 
Risks and Trustworthiness. 
https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Foundational_Information/3-sec-characteristics 
National Institute of Standards and Technology (2023) AI Risk Management Framework, Chapter 6: AI 
RMF Proﬁles. https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Core_And_Proﬁles/6-sec-proﬁle, (e.g., via red-teaming, ﬁeld testing, participatory engagements, performance 
assessments, user feedback mechanisms). 
Human-AI Conﬁguration 
AI Actor Tasks: AI Development, AI Deployment, AI Impact Assessment, Operation and Monitoring 
 
MANAGE 2.2: Mechanisms are in place and applied to sustain the value of deployed AI systems. 
Action ID 
Suggested Action 
GAI Risks 
MG-2.2-001 
Compare GAI system outputs against pre-deﬁned organization risk tolerance, 
guidelines, and principles, and review and test AI-generated content against 
these guidelines. 
CBRN Information or Capabilities; 
Obscene, Degrading, and/or 
Abusive Content; Harmful Bias and 
Homogenization; Dangerous, 
Violent, or Hateful Content 
MG-2.2-002 
Document training data sources to trace the origin and provenance of AI-
generated content. 
Information Integrity 
MG-2.2-003 
Evaluate feedback loops between GAI system content provenance and human, 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, source_sentence: How can organizations leverage user feedback to enhance content provenance and risk management efforts?
sentences: tested, there will always be situations for which the system fails. The American public deserves protection via human 
review against these outlying or unexpected scenarios. In the case of time-critical systems, the public should not have 
to wait—immediate human consideration and fallback should be available. In many time-critical systems, such a 
remedy is already immediately available, such as a building manager who can open a door in the case an automated 
card access system fails. 
In the criminal justice system, employment, education, healthcare, and other sensitive domains, automated systems 
are used for many purposes, from pre-trial risk assessments and parole decisions to technologies that help doctors 
diagnose disease. Absent appropriate safeguards, these technologies can lead to unfair, inaccurate, or dangerous 
outcomes. These sensitive domains require extra protections. It is critically important that there is extensive human 
oversight in such settings., enable organizations to maximize the utility of provenance data and risk management eﬀorts. 
A.1.7. Enhancing Content Provenance through Structured Public Feedback 
While indirect feedback methods such as automated error collection systems are useful, they often lack 
the context and depth that direct input from end users can provide. Organizations can leverage feedback 
approaches described in the Pre-Deployment Testing section to capture input from external sources such 
as through AI red-teaming.  
Integrating pre- and post-deployment external feedback into the monitoring process for GAI models and 
corresponding applications can help enhance awareness of performance changes and mitigate potential 
risks and harms from outputs. There are many ways to capture and make use of user feedback – before 
and after GAI systems and digital content transparency approaches are deployed – to gain insights about, 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, source_sentence: What should be ensured for users who have trouble with the automated system?
sentences: 32 
MEASURE 2.6: The AI system is evaluated regularly for safety risks – as identiﬁed in the MAP function. The AI system to be 
deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if 
made to operate beyond its knowledge limits. Safety metrics reﬂect system reliability and robustness, real-time monitoring, and 
response times for AI system failures. 
Action ID 
Suggested Action 
GAI Risks 
MS-2.6-001 
Assess adverse impacts, including health and wellbeing impacts for value chain 
or other AI Actors that are exposed to sexually explicit, oﬀensive, or violent 
information during GAI training and maintenance. 
Human-AI Conﬁguration; Obscene, 
Degrading, and/or Abusive 
Content; Value Chain and 
Component Integration; 
Dangerous, Violent, or Hateful 
Content 
MS-2.6-002 
Assess existence or levels of harmful bias, intellectual property infringement,, APPENDIX
Systems that impact the safety of communities such as automated traffic control systems, elec 
-ctrical grid controls, smart city technologies, and industrial emissions and environmental
impact control algorithms; and
Systems related to access to benefits or services or assignment of penalties such as systems that
support decision-makers who adjudicate benefits such as collating or analyzing information or
matching records, systems which similarly assist in the adjudication of administrative or criminal
penalties, fraud detection algorithms, services or benefits access control algorithms, biometric
systems used as access control, and systems which make benefits or services related decisions on a
fully or partially autonomous basis (such as a determination to revoke benefits).
54, 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 
otherwise provided, should be easy to find and use. These mechanisms should be tested to ensure that users 
who have trouble with the automated system are able to use human consideration and fallback, with the under­
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 must lenders provide to consumers who are denied credit under the Fair Credit Reporting Act?
sentences: 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)., that consumers who are denied credit receive "adverse action" notices. Anyone who relies on the information in a 
credit report to deny a consumer credit must, under the Fair Credit Reporting Act, provide an "adverse action" 
notice to the consumer, which includes "notice of the reasons a creditor took adverse action on the application 
or on an existing credit account."90 In addition, under the risk-based pricing rule,91 lenders must either inform 
borrowers of their credit score, or else tell consumers when "they are getting worse terms because of 
information in their credit report." The CFPB has also asserted that "[t]he law gives every applicant the right to 
a specific explanation if their application for credit was denied, and that right is not diminished simply because 
a company uses a complex algorithm that it doesn't understand."92 Such explanations illustrate a shared value 
that certain decisions need to be explained., measures to prevent, ﬂag, or take other action in response to outputs that 
reproduce particular training data (e.g., plagiarized, trademarked, patented, 
licensed content or trade secret material). 
Intellectual Property; CBRN 
Information or Capabilities
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: 0.875
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9671052631578947
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9868421052631579
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.993421052631579
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.875
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3223684210526316
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19736842105263155
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09934210526315788
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.875
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9671052631578947
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9868421052631579
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.993421052631579
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9420758802321664
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9248903508771928
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.925488437001595
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.875
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9671052631578947
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.9868421052631579
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.993421052631579
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.875
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3223684210526316
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19736842105263155
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09934210526315788
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.875
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9671052631578947
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.9868421052631579
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.993421052631579
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9420758802321664
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9248903508771928
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.925488437001595
name: Dot Map@100
verified: no, type: cosine_accuracy@1
value: 0.890625
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.96875
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.96875
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9765625
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.890625
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.32291666666666663
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19375000000000003
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09765625000000003
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.890625
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.96875
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.96875
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9765625
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9391060398540476
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9265625
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9282275883838385
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.890625
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.96875
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.96875
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.9765625
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.890625
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.32291666666666663
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19375000000000003
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09765625000000003
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.890625
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.96875
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.96875
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.9765625
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9391060398540476
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9265625
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9282275883838385
name: Dot Map@100
verified: no
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Created: 2025-12-13 14:24:26