Model: XicoC/midterm-finetuned-arctic
Author: XicoC
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:600, 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:600, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How can high compute resource utilization in training GAI models affect ecosystems?
sentences: should not be used in education, work, housing, or in other contexts where the use of such surveillance 
technologies is likely to limit rights, opportunities, or access. Whenever possible, you should have access to 
reporting that confirms your data decisions have been respected and provides an assessment of the 
potential impact of surveillance technologies on your rights, opportunities, or access. 
NOTICE AND EXPLANATION, 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, or stereotyping content . 
4. Data Privacy: Impacts due to l eakage and unauthorized use, disclosure , or de -anonymization of 
biometric, health, location , or other  personally identiﬁable information  or sensitive data .7 
5. Environmental Impacts: Impacts due to high compute resource utilization in training or 
operating GAI models, and related outcomes that may adversely impact ecosystems.  
6. Harmful Bias or Homogenization: Ampliﬁcation and exacerbation of  historical,  societal, and 
systemic  biases ; performance disparities8 between  sub- groups or languages , possibly due to 
non- representative training data , that result in discrimination, ampliﬁcation of biases, or, source_sentence: What are the potential risks associated with human-AI configuration in GAI systems?
sentences: establish approved GAI technology and service provider lists.  Value Chain and Component 
Integration  
GV-6.1-0 08 Maintain records of changes to content made by third parties to promote content 
provenance, including sources, timestamps, metadata . Information Integrity ; Value Chain 
and Component Integration; Intellectual Property  
GV-6.1-0 09 Update and integrate due diligence processes for GAI acquisition and 
procurement vendor assessments to include intellectual property, data privacy, security, and other risks. For example, update p rocesses 
to: Address solutions that 
may rely on embedded GAI technologies; Address ongoing monitoring , 
assessments, and alerting, dynamic risk assessments, and real -time reporting, could lead to homogenized outputs, including by amplifying any homogenization from the model used to 
generate the synthetic training data . 
Trustworthy AI Characteristics:  Fair with Harmful Bias Managed, Valid and Reliable  
2.7. Human -AI Conﬁguration  
GAI system  use can involve varying risks of misconﬁgurations  and poor interactions  between a system 
and a human who is interacti ng with it. Humans bring their  unique perspectives , experiences , or domain -
speciﬁc expertise to interactions with AI systems  but may not have detailed knowledge of AI systems and 
how they work.  As a result, h uman experts may be unnecessarily “averse ” to GAI systems , and thus 
deprive themselves  or others  of GAI’s beneﬁcial uses ., requests image features that are inconsistent with the  stereotypes.  Harmful b ias in GAI models , which 
may stem from their training data , can also  cause representational  harm s or perpetuate  or exacerbate  
bias based on race, gender, disability, or other protected classes .  
Harmful b ias in GAI systems can also lead to harms via disparities between how a model performs for 
diﬀerent subgroups or languages  (e.g., an LLM may perform  less well for non- English languages  or 
certain dialects ). Such disparities can contribute to discriminatory decision -making or ampliﬁcation of 
existing societal biases.  In addition,  GAI systems may be inappropriately trusted to perform similarly, source_sentence: What types of content are considered harmful biases in the context of information security?
sentences: MS-2.5-0 05 Verify GAI system training data and TEVV data provenance, and that ﬁne -tuning  
or retrieval- augmented generation data is grounded.  Information Integrity  
MS-2.5-0 06 Regularly review security and safety guardrails, especially if the GAI system is 
being operated in novel circumstances. This includes reviewing reasons why the 
GAI system was initially assessed as being safe to deploy.  Information Security ; Dangerous , 
Violent, or Hateful  Content  
AI Actor Tasks:  Domain Experts, TEVV, to diminished transparency or accountability for downstream users.  While  this is a risk for traditional AI 
systems  and some other digital technologies , the risk is exacerbated for GAI due to the scale of the 
training data, which may be too large for humans to vet; the  diﬃculty of training foundation models, 
which leads to extensive reuse of limited numbers of models; an d the extent to which GAI may be 
integrat ed into other  devices and services.  As GAI systems often involve many distinct  third -party 
components and data sources , it may be diﬃcult to attribute issues in a system’s behavior to any one of 
these sources.  
Errors in t hird-party GAI components can also have downstream impacts  on accuracy and robustness ., biases in the generated content.  Information Security ; Harmful Bias 
and Homogenization  
MG-2.2-005 Engage in due diligence to analyze  GAI output for harmful content, potential 
misinformation , and CBRN -related or NCII content . CBRN Information or Capabilities ; 
Obscene, Degrading, and/or 
Abusive Content ; Harmful Bias and 
Homogenization ; Dangerous , 
Violent, or Hateful Content, source_sentence: What is the focus of the paper by Padmakumar et al (2024) regarding language models and content diversity?
sentences: Content  
MS-2.12- 002 Document anticipated environmental impacts of model development, 
maintenance, and deployment in product design decisions.  Environmental  
MS-2.12- 003 Measure or estimate environmental impacts (e.g., energy and water 
consumption) for training, ﬁne tuning, and deploying models: Verify tradeoﬀs 
between resources used at inference time versus additional resources required at training time.  Environmental  
MS-2.12- 004 Verify eﬀectiveness of carbon capture or oﬀset programs  for GAI training and 
applications , and address green -washing concerns . Environmental  
AI Actor Tasks:  AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV, opportunities, undermine their privac y, or pervasively track their activity—often without their knowledge or 
consent. 
These outcomes are deeply harmful—but they are not inevitable. Automated systems have brought about extraor-
dinary benefits, from technology that helps farmers grow food more efficiently and computers that predict storm 
paths, to algorithms that can identify diseases in patients. These tools now drive important decisions across 
sectors, while data is helping to revolutionize global industries. Fueled by the power of American innovation, 
these tools hold the potential to redefine every part of our society and make life better for everyone., Publishing, Paris . https://doi.org/10.1787/d1a8d965- en 
OpenAI  (2023) GPT-4 System Card . https://cdn.openai.com/papers/gpt -4-system -card.pdf  
OpenAI  (2024) GPT-4 Technical Report. https://arxiv.org/pdf/2303.08774  
Padmakumar, V. et al. (2024) Does writing with language models reduce content diversity?  ICLR . 
https://arxiv.org/pdf/2309.05196  
Park,  P.  et. al. (2024)  AI deception: A survey of  examples, risks, and potential solutions. Patterns, 5(5).  
arXiv . https://arxiv.org/pdf/2308.14752  
Partnership on AI  (2023) Building a Glossary for Synthetic Media Transparency Methods, Part 1: Indirect 
Disclosure . https://partnershiponai.org/glossary -for-synthetic -media- transparency -methods -part-1-
indirect -disclosure/, source_sentence: What are the key components involved in ensuring data quality and ethical considerations in AI systems?
sentences: (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, 30 MEASURE 2.2:  Evaluations involving human subjects meet applicable requirements (including human subject protection) and are 
representative of the relevant population.  
Action ID  Suggested Action  GAI Risks  
MS-2.2-001 Assess and manage statistical biases related to GAI content provenance through 
techniques such as re -sampling, re -weighting, or adversarial training.  Information Integrity ; Information 
Security ; Harmful Bias and 
Homogenization  
MS-2.2-002 Document how content provenance data  is tracked  and how that data interact s 
with  privacy and security . Consider : Anonymiz ing data to protect the privacy of 
human subjects; Leverag ing privacy output ﬁlters; Remov ing any personally, Data quality; Model architecture (e.g., convolutional neural network, transformers, etc.); Optimizatio n objectives; Training algorithms; RLHF 
approaches; Fine -tuning or retrieval- augmented generation approaches; 
Evaluation data; Ethical considerations; Legal and regulatory requirements.  Information Integrity ; Harmful Bias 
and Homogenization  
AI Actor Tasks:  AI Deployment, AI Impact Assessment, Domain Experts, End -Users, Operation and Monitoring, TEVV  
 
MEASURE 2.10:  Privacy risk of the AI system – as identiﬁed in the MAP function – is examined and documented.  
Action ID  Suggested Action  GAI Risks  
MS-2.10- 001 Conduct AI red -teaming to assess issues  such as: Outputting of training data
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: 0.8
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.99
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.99
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.33000000000000007
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19799999999999998
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.99
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.99
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9195108324425135
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.8916666666666667
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.8916666666666666
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.99
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.99
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.33000000000000007
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19799999999999998
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.99
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.99
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9195108324425135
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.8916666666666667
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.8916666666666666
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 8
Created: 2024-09-18 21:41:46