Model: rchrdgwr/finetuned-arctic-model
Author: rchrdgwr
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: sentence-transformers, safetensors, bert, sentence-similarity, feature-extraction, generated_from_trainer, dataset_size:522, 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:522, loss:MatryoshkaLoss, loss:MultipleNegativesRankingLoss
widget: source_sentence: How did the hiring tool's design contribute to the rejection of women applicants?
sentences: legal protections. Throughout this framework the term “algorithmic discrimination” takes this meaning (and 
not a technical understanding of discrimination as distinguishing between items). 
AUTOMATED SYSTEM: An "automated system" is any system, software, or process that uses computation as 
whole or part of a system to determine outcomes, make or aid decisions, inform policy implementation, collect 
data or observations, or otherwise interact with individuals and/or communities. Automated systems 
include, but are not limited to, systems derived from machine learning, statistics, or other data processing 
or artificial intelligence techniques, and exclude passive computing infrastructure. “Passive computing, communities. 
• An automated system using nontraditional factors such as educational attainment and employment history as
part of its loan underwriting and pricing model was found to be much more likely to charge an applicant whoattended a Historically Black College or University (HBCU) higher loan prices for refinancing a student loanthan an applicant who did not attend an HBCU. This was found to be true even when controlling for
other credit-related factors.32
•A hiring tool that learned the features of a company's employees (predominantly men) rejected women appli -
cants for spurious and discriminatory reasons; resumes with the word “women’s,” such as “women’s
chess club captain,” were penalized in the candidate ranking.33, dures before deploying the system, as well as responsibility of specific individuals or entities to oversee ongoing assessment and mitigation. Organizational stakeholders including those with oversight of the business process or operation being automated, as well as other organizational divisions that may be affected due to the use of the system, should be involved in establishing governance procedures. Responsibility should rest high enough in the organization that decisions about resources, mitigation, incident response, and potential rollback can be made promptly, with sufficient weight given to risk mitigation objectives against competing concerns. Those holding this responsibility should be made aware of any use cases with the, source_sentence: How are companies using individual profiles based on tracked behavior to impact the American public?
sentences: requests should be used so that users understand for what use contexts, time span, and entities they are providing data and metadata consent. User experience research should be performed to ensure these consent requests meet performance standards for readability and comprehension. This includes ensuring that consent requests are accessible to users with disabilities and are available in the language(s) and reading level appro
-
priate for the audience.  User experience design choices that intentionally obfuscate or manipulate user choice (i.e., “dark patterns”) should be not be used. 
34
      DATA PRIVACY 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS, with more and more companies tracking the behavior of the American public, building individual profiles based on this data, and using this granular-level information as input into automated systems that further track, profile, and impact the American public. Government agencies, particularly law enforcement agencies, also use and help develop a variety of technologies that enhance and expand surveillance capabilities, which similarly collect data used as input into other automated systems that directly impact people’s lives. Federal law has not grown to address the expanding scale of private data collection, or of the ability of governments at all levels to access that data and leverage the means of private collection., ways that threaten the rights of the American public. Too often, these tools are used to limit our opportunities and 
prevent our access to critical resources or services. These problems are well documented. In America and around 
the world, systems supposed to help with patient care have proven unsafe, ineffective, or biased. Algorithms used 
in hiring and credit decisions have been found to reflect and reproduce existing unwanted inequities or embed 
new harmful bias and discrimination. Unchecked social media data collection has been used to threaten people’s 
opportunities, undermine their privac y, or pervasively track their activity—often without their knowledge or 
consent., source_sentence: What should entities developing technologies related to sensitive data regularly report on?
sentences: concerns that may limit their effectiveness. The results of assessments of the efficacy and potential bias of such human-based systems should be overseen by governance structures that have the potential to update the operation of the human-based system in order to mitigate these effects. 
50
      
 HUMAN ALTERNATIVES, 
CONSIDERATION, AND 
FALLBACK 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Implement additional human oversight and safeguards for automated systems related to 
sensitive domains, performance testing including, but not limited to, accuracy, differential demographic impact, resulting 
error rates (overall and per demographic group), and comparisons to previously deployed systems; 
ongoing monitoring procedures and regular performance testing reports, including monitoring frequency, 
results, and actions taken; and the procedures for and results from independent evaluations. Reporting 
should be provided in a plain language and machine-readable manner. 
20
       
 
 
 
 
 
  SAFE AND EFFECTIVE 
SYSTEMS 
HOW THESE PRINCIPLES CAN MOVE INTO PRACTICE
Real-life examples of how these principles can become reality, through laws, policies, and practical, 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 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: What are the expectations for automated systems intended to serve as a blueprint for?
sentences: Clear organizational oversight. Entities responsible for the development or use of automated systems should lay out clear governance structures and procedures.  This includes clearly-stated governance proce
-, critical resources or services. These rights, opportunities, and access to critical resources of services should 
be enjoyed equally and be fully protected, regardless of the changing role that automated systems may play in 
our lives. 
This framework describes protections that should be applied with respect to all automated systems that 
have the potential to meaningfully impact individuals' or communities' exercise of: 
RIGHTS, OPPORTUNITIES, OR ACCESS
Civil rights, civil liberties, and privacy, including freedom of speech, voting, and protections from discrimi -
nation, excessive punishment, unlawful surveillance, and violations of privacy and other freedoms in both 
public and private sector contexts;, 19
       
 
  SAFE AND EFFECTIVE 
SYSTEMS 
WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Derived data sources tracked and reviewed carefully. Data that is derived from other data through 
the use of algorithms, such as data derived or inferred from prior model outputs, should be identified and tracked, e.g., via a specialized type in a data schema. Derived data should be viewed as potentially high-risk inputs that may lead to feedback loops, compounded harm, or inaccurate results. Such sources should be care
-, source_sentence: What types of systems are considered time-critical according to the context?
sentences: Equity includes a commitment from the agencies that oversee mortgage lending to include a 
nondiscrimination standard in the proposed rules for Automated Valuation Models.52
The Equal Employment Opportunity Commission and the Department of Justice have clearly 
laid out how employers’ use of AI and other automated systems can result in discrimination 
against job applicants and employees with disabilities.53 The documents explain 
how employers’ use of software that relies on algorithmic decision-making may violate existing requirements 
under Title I of the Americans with Disabilities Act (“ADA”). This technical assistance also provides practical, Discrimination 
Protections  
      
 WHAT SHOULD BE EXPECTED OF AUTOMATED SYSTEMS
The expectations for automated systems are meant to serve as a blueprint for the development of additional 
technical standards and practices that are tailored for particular sectors and contexts. 
Demonstrate that the system protects against algorithmic discrimination 
Independent evaluation. As described in the section on Safe and Effective Systems, entities should allow 
independent evaluation of potential algorithmic discrimination caused by automated systems they use or, where possible, available before the harm occurs. Time-critical systems include, but are not limited to, 
voting-related systems, automated building access and other access systems, systems that form a critical 
component of healthcare, and systems that have the ability to withhold wages or otherwise cause 
immediate financial penalties. 
Effective. The organizational structure surrounding processes for consideration and fallback should 
be designed so that if the human decision-maker charged with reassessing a decision determines that it 
should be overruled, the new decision will be effectively enacted. This includes ensuring that the new
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: 0.8448275862068966
name: Cosine Accuracy@1
verified: no, type: cosine_accuracy@3
value: 0.9482758620689655
name: Cosine Accuracy@3
verified: no, type: cosine_accuracy@5
value: 0.9770114942528736
name: Cosine Accuracy@5
verified: no, type: cosine_accuracy@10
value: 0.9942528735632183
name: Cosine Accuracy@10
verified: no, type: cosine_precision@1
value: 0.8448275862068966
name: Cosine Precision@1
verified: no, type: cosine_precision@3
value: 0.3160919540229885
name: Cosine Precision@3
verified: no, type: cosine_precision@5
value: 0.19540229885057464
name: Cosine Precision@5
verified: no, type: cosine_precision@10
value: 0.09942528735632182
name: Cosine Precision@10
verified: no, type: cosine_recall@1
value: 0.8448275862068966
name: Cosine Recall@1
verified: no, type: cosine_recall@3
value: 0.9482758620689655
name: Cosine Recall@3
verified: no, type: cosine_recall@5
value: 0.9770114942528736
name: Cosine Recall@5
verified: no, type: cosine_recall@10
value: 0.9942528735632183
name: Cosine Recall@10
verified: no, type: cosine_ndcg@10
value: 0.924865695917767
name: Cosine Ndcg@10
verified: no, type: cosine_mrr@10
value: 0.901963601532567
name: Cosine Mrr@10
verified: no, type: cosine_map@100
value: 0.9021617783062492
name: Cosine Map@100
verified: no, type: dot_accuracy@1
value: 0.8448275862068966
name: Dot Accuracy@1
verified: no, type: dot_accuracy@3
value: 0.9482758620689655
name: Dot Accuracy@3
verified: no, type: dot_accuracy@5
value: 0.9770114942528736
name: Dot Accuracy@5
verified: no, type: dot_accuracy@10
value: 0.9942528735632183
name: Dot Accuracy@10
verified: no, type: dot_precision@1
value: 0.8448275862068966
name: Dot Precision@1
verified: no, type: dot_precision@3
value: 0.3160919540229885
name: Dot Precision@3
verified: no, type: dot_precision@5
value: 0.19540229885057464
name: Dot Precision@5
verified: no, type: dot_precision@10
value: 0.09942528735632182
name: Dot Precision@10
verified: no, type: dot_recall@1
value: 0.8448275862068966
name: Dot Recall@1
verified: no, type: dot_recall@3
value: 0.9482758620689655
name: Dot Recall@3
verified: no, type: dot_recall@5
value: 0.9770114942528736
name: Dot Recall@5
verified: no, type: dot_recall@10
value: 0.9942528735632183
name: Dot Recall@10
verified: no, type: dot_ndcg@10
value: 0.924865695917767
name: Dot Ndcg@10
verified: no, type: dot_mrr@10
value: 0.901963601532567
name: Dot Mrr@10
verified: no, type: dot_map@100
value: 0.9021617783062492
name: Dot Map@100
verified: no
Likes: 0 | All-time downloads: 178
Created: 2024-09-22 23:05:45