Model: Cheselle/finetuned-arctic-sentence
Author: Cheselle
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 organizations tailor their measurement of GAI risks based on specific characteristics?
sentences: 3 
the abuse, misuse, and unsafe repurposing by humans (adversarial or not), and others result 
from interactions between a human and an AI system.  
• 
Time scale: GAI risks may materialize abruptly or across extended periods. Examples include 
immediate (and/or prolonged) emotional harm and potential risks to physical safety due to the 
distribution of harmful deepfake images, or the long-term eﬀect of disinformation on societal 
trust in public institutions., 12 
CSAM. Even when trained on “clean” data, increasingly capable GAI models can synthesize or produce 
synthetic NCII and CSAM. Websites, mobile apps, and custom-built models that generate synthetic NCII 
have moved from niche internet forums to mainstream, automated, and scaled online businesses.  
Trustworthy AI Characteristics: Fair with Harmful Bias Managed, Safe, Privacy Enhanced 
2.12. 
Value Chain and Component Integration, 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 
negative impacts, including where and how these risks manifest, and their direct and material impacts 
harms in the context of GAI use. Mitigations for model or system level risks may diﬀer from mitigations 
for use-case or ecosystem level risks., source_sentence: What methods are suggested for measuring the reliability of content authentication techniques in the context of content provenance?
sentences: updates. 
Information Integrity; Data Privacy 
MG-3.2-003 
Document sources and types of training data and their origins, potential biases 
present in the data related to the GAI application and its content provenance, 
architecture, training process of the pre-trained model including information on 
hyperparameters, training duration, and any ﬁne-tuning or retrieval-augmented 
generation processes applied. 
Information Integrity; Harmful Bias 
and Homogenization; Intellectual 
Property, Security 
MS-2.7-005 
Measure reliability of content authentication methods, such as watermarking, 
cryptographic signatures, digital ﬁngerprints, as well as access controls, 
conformity assessment, and model integrity veriﬁcation, which can help support 
the eﬀective implementation of content provenance techniques. Evaluate the 
rate of false positives and false negatives in content provenance, as well as true 
positives and true negatives for veriﬁcation. 
Information Integrity 
MS-2.7-006, GV-1.6-003 
In addition to general model, governance, and risk information, consider the 
following items in GAI system inventory entries: Data provenance information 
(e.g., source, signatures, versioning, watermarks); Known issues reported from 
internal bug tracking or external information sharing resources (e.g., AI incident 
database, AVID, CVE, NVD, or OECD AI incident monitor); Human oversight roles 
and responsibilities; Special rights and considerations for intellectual property,, source_sentence: What are the suggested actions an organization can take to manage GAI risks?
sentences: 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 
 
GOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other, match the statistical properties of real-world data without disclosing personally 
identiﬁable information or contributing to homogenization. 
Data Privacy; Intellectual Property; 
Information Integrity; 
Confabulation; Harmful Bias and 
Homogenization 
AI Actor Tasks: AI Deployment, AI Impact Assessment, Governance and Oversight, Operation and Monitoring 
 
MANAGE 2.3: Procedures are followed to respond to and recover from a previously unknown risk when it is identiﬁed. 
Action ID, • 
Suggested Action: Steps an organization or AI actor can take to manage GAI risks.  
• 
GAI Risks: Tags linking suggested actions with relevant GAI risks.  
• 
AI Actor Tasks: Pertinent AI Actor Tasks for each subcategory. Not every AI Actor Task listed will 
apply to every suggested action in the subcategory (i.e., some apply to AI development and 
others apply to AI deployment).  
The tables below begin with the AI RMF subcategory, shaded in blue, followed by suggested actions., source_sentence: How can harmful bias and homogenization be addressed in the context of human-AI configuration?
sentences: on GAI, apply general fairness metrics (e.g., demographic parity, equalized odds, 
equal opportunity, statistical hypothesis tests), to the pipeline or business 
outcome where appropriate; Custom, context-speciﬁc metrics developed in 
collaboration with domain experts and aﬀected communities; Measurements of 
the prevalence of denigration in generated content in deployment (e.g., sub-
sampling a fraction of traﬃc and manually annotating denigrating content). 
Harmful Bias and Homogenization;, MP-5.1-001 Apply TEVV practices for content provenance (e.g., probing a system's synthetic 
data generation capabilities for potential misuse or vulnerabilities. 
Information Integrity; Information 
Security 
MP-5.1-002 
Identify potential content provenance harms of GAI, such as misinformation or 
disinformation, deepfakes, including NCII, or tampered content. Enumerate and 
rank risks based on their likelihood and potential impact, and determine how well, MS-1.3-002 
Engage in internal and external evaluations, GAI red-teaming, impact 
assessments, or other structured human feedback exercises in consultation 
with representative AI Actors with expertise and familiarity in the context of 
use, and/or who are representative of the populations associated with the 
context of use. 
Human-AI Conﬁguration; Harmful 
Bias and Homogenization; CBRN 
Information or Capabilities 
MS-1.3-003, source_sentence: How can structured human feedback exercises, such as GAI red-teaming, contribute to GAI risk measurement and management?
sentences: rank risks based on their likelihood and potential impact, and determine how well 
provenance solutions address speciﬁc risks and/or harms. 
Information Integrity; Dangerous, 
Violent, or Hateful Content; 
Obscene, Degrading, and/or 
Abusive Content 
MP-5.1-003 
Consider disclosing use of GAI to end users in relevant contexts, while considering 
the objective of disclosure, the context of use, the likelihood and magnitude of the, 15 
GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use, in 
accordance with activities in the AI RMF Map function. 
CBRN Information or Capabilities; 
Obscene, Degrading, and/or 
Abusive Content; Harmful Bias 
and Homogenization; Dangerous, 
Violent, or Hateful Content 
GV-1.3-005 
Maintain an updated hierarchy of identiﬁed and expected GAI risks connected to 
contexts of GAI model advancement and use, potentially including specialized risk, AI-generated content, for example by employing techniques like chaos 
engineering and seeking stakeholder feedback. 
Information Integrity 
MS-1.1-008 
Deﬁne use cases, contexts of use, capabilities, and negative impacts where 
structured human feedback exercises, e.g., GAI red-teaming, would be most 
beneﬁcial for GAI risk measurement and management based on the context of 
use. 
Harmful Bias and 
Homogenization; CBRN 
Information or Capabilities 
MS-1.1-009
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: 0.85
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.96
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.98
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 1
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.85
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.31999999999999995
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19599999999999995
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09999999999999998
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.85
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.96
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.98
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 1
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.9342942871848772
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.9124166666666668
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9124166666666668
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.85
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.96
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 1
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.85
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.31999999999999995
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19599999999999995
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09999999999999998
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.85
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.96
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.98
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 1
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.9342942871848772
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.9124166666666668
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9124166666666668
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 9
Created: 2024-09-23 22:57:09