Instructions to use lw2134/policy_gte_large_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lw2134/policy_gte_large_5 with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("lw2134/policy_gte_large_5", trust_remote_code=True)

sentences = [
    "What are some of the mental health impacts associated with the increased use of surveillance technologies in schools and workplaces, as mentioned in the context information?",
    "15 GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use , in \naccordance with activities in the AI RMF Map function . CBRN Information or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; Dangerous, \nViolent, or Hateful Content  \nGV-1.3-005 Maintain an updated hierarch y of identiﬁed and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk \nlevels for GAI systems that address issues such as model collapse and algorithmic \nmonoculture.  Harmful Bias and Homogenization  \nGV-1.3-006 Reevaluate organizational risk tolerances to account for unacceptable negative risk \n(such as where signiﬁcant negative impacts are imminent, severe harms are actually occurring, or large -scale risks could occur); and broad GAI negative risks, \nincluding: 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 , \nViolent, or Hateful Content ; CBRN \nInformation or Capabilities  \nGV-1.3-007 Devise a plan to halt development or deployment of a GAI system that poses unacceptable negative risk.  CBRN Information and Capability ; \nInformation Security ; Information \nIntegrity  \nAI Actor Tasks: Governance and Oversight  \n \nGOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other \ncontrols based on organizational risk priorities.  \nAction ID  Suggested Action  GAI Risks  \nGV-1.4-001 Establish policies and mechanisms to prevent GAI systems from generating \nCSAM, NCII or content that violates the law.   Obscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; \nDangerous, Violent, or Hateful Content\n \nGV-1.4-002 Establish transparent acceptable use policies for GAI that address illegal use or \napplications of GAI.  CBRN Information or \nCapabilities ; Obscene, \nDegrading, and/or Abusive Content ; Data Privacy ; Civil \nRights violations\n \nAI Actor Tasks: AI Development, AI Deployment, Governance and Oversight",
    "DATA PRIVACY \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \nData privacy is a foundational and cross-cutting principle required for achieving all others in this framework. Surveil -\nlance and data collection, sharing, use, and reuse now sit at the foundation of business models across many industries, \nwith 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.  \nMeanwhile, members of the American public are often unable to access their personal data or make critical decisions about its collection and use. Data brokers frequently collect consumer data from numerous sources without consumers’ permission or \nknowledge.60 Moreover, there is a risk that inaccurate and faulty data can be used to \nmake decisions about their lives, such as whether they will qualify for a loan or get a job. Use of surveillance \ntechnologies has increased in schools and workplaces, and, when coupled with consequential management and \nevaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiet y, depression, and \na reduced ability to use analytical reasoning.61 Documented patterns show that personal data is being aggregated by \ndata brokers to profile communities in harmful ways.62 The impact of all this data harvesting is corrosive, \nbreeding distrust, anxiety, and other mental health problems; chilling speech, protest, and worker organizing; and \nthreatening our democratic process.63 The American public should be protected from these growing risks. \nIncreasingl y, some companies are taking these concerns seriously and integrating mechanisms to protect consumer \nprivacy into their products by design and by default, including by minimizing the data they collect, communicating collection and use clearl y, and improving security practices. Federal government surveillance and other collection and \nuse of data is governed by legal protections that help to protect civil liberties and provide for limits on data retention in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these harms. \nHoweve r, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory \nframework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to guide the collection and use of personal data in specific contexts, including health, employment, education, and credit, it can be unclear how these laws apply in other contexts and in an increasingly automated societ y. Additional protec\n-\ntions would assure the American public that the automated systems they use are not monitoring their activities, collecting information on their lives, or otherwise surveilling them without context-specific consent or legal authori\n-\nty. \n31",
    "Applying The Blueprint for an AI Bill of Rights \nSENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain \n(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a \nsensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship history and legal status such as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video data. \nSENSITIVE DOMAINS: “Sensitive domains” are those in which activities being conducted can cause material \nharms, including significant adverse effects on human rights such as autonomy and dignit y, as well as civil liber-\nties and civil rights. Domains that have historically been singled out as deserving of enhanced data protections \nor where such enhanced protections are reasonably expected by the public include, but are not limited to, health, family planning and care, employment, education, criminal justice, and personal finance. In the context of this framework, such domains are considered sensitive whether or not the specifics of a system context would necessitate coverage under existing la w, and domains and data that are considered sensitive are under-\nstood to change over time based on societal norms and context. \nSURVEILLANCE TECHNOLOGY : “Surveillance technology” refers to products or services marketed for \nor that can be lawfully used to detect, monitor, intercept, collect, exploit, preserve, protect, transmit, and/or \nretain data, identifying information, or communications concerning individuals or groups. This framework \nlimits its focus to both government and commercial use of surveillance technologies when juxtaposed with \nreal-time or subsequent automated analysis and when such systems have a potential for meaningful impact \non individuals’ or communities’ rights, opportunities, or access. UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have \nbeen systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as \nexemplified by the list in the preceding definition of “equit y.” \n11"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

Notebooks
Google Colab
Kaggle

How to use from the

Use from the

sentence-transformers library

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("lw2134/policy_gte_large_5", trust_remote_code=True)

sentences = [
    "What are some of the mental health impacts associated with the increased use of surveillance technologies in schools and workplaces, as mentioned in the context information?",
    "15 GV-1.3-004 Obtain input from stakeholder communities to identify unacceptable use , in \naccordance with activities in the AI RMF Map function . CBRN Information or Capabilities ; \nObscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; Dangerous, \nViolent, or Hateful Content  \nGV-1.3-005 Maintain an updated hierarch y of identiﬁed and expected GAI risks connected to \ncontexts of GAI model advancement and use, potentially including specialized risk \nlevels for GAI systems that address issues such as model collapse and algorithmic \nmonoculture.  Harmful Bias and Homogenization  \nGV-1.3-006 Reevaluate organizational risk tolerances to account for unacceptable negative risk \n(such as where signiﬁcant negative impacts are imminent, severe harms are actually occurring, or large -scale risks could occur); and broad GAI negative risks, \nincluding: 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 , \nViolent, or Hateful Content ; CBRN \nInformation or Capabilities  \nGV-1.3-007 Devise a plan to halt development or deployment of a GAI system that poses unacceptable negative risk.  CBRN Information and Capability ; \nInformation Security ; Information \nIntegrity  \nAI Actor Tasks: Governance and Oversight  \n \nGOVERN 1.4: The risk management process and its outcomes are established through transparent policies, procedures, and other \ncontrols based on organizational risk priorities.  \nAction ID  Suggested Action  GAI Risks  \nGV-1.4-001 Establish policies and mechanisms to prevent GAI systems from generating \nCSAM, NCII or content that violates the law.   Obscene, Degrading, and/or \nAbusive Content ; Harmful Bias \nand Homogenization ; \nDangerous, Violent, or Hateful Content\n \nGV-1.4-002 Establish transparent acceptable use policies for GAI that address illegal use or \napplications of GAI.  CBRN Information or \nCapabilities ; Obscene, \nDegrading, and/or Abusive Content ; Data Privacy ; Civil \nRights violations\n \nAI Actor Tasks: AI Development, AI Deployment, Governance and Oversight",
    "DATA PRIVACY \nWHY THIS PRINCIPLE IS IMPORTANT\nThis section provides a brief summary of the problems which the principle seeks to address and protect \nagainst, including illustrative examples. \nData privacy is a foundational and cross-cutting principle required for achieving all others in this framework. Surveil -\nlance and data collection, sharing, use, and reuse now sit at the foundation of business models across many industries, \nwith 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.  \nMeanwhile, members of the American public are often unable to access their personal data or make critical decisions about its collection and use. Data brokers frequently collect consumer data from numerous sources without consumers’ permission or \nknowledge.60 Moreover, there is a risk that inaccurate and faulty data can be used to \nmake decisions about their lives, such as whether they will qualify for a loan or get a job. Use of surveillance \ntechnologies has increased in schools and workplaces, and, when coupled with consequential management and \nevaluation decisions, it is leading to mental health harms such as lowered self-confidence, anxiet y, depression, and \na reduced ability to use analytical reasoning.61 Documented patterns show that personal data is being aggregated by \ndata brokers to profile communities in harmful ways.62 The impact of all this data harvesting is corrosive, \nbreeding distrust, anxiety, and other mental health problems; chilling speech, protest, and worker organizing; and \nthreatening our democratic process.63 The American public should be protected from these growing risks. \nIncreasingl y, some companies are taking these concerns seriously and integrating mechanisms to protect consumer \nprivacy into their products by design and by default, including by minimizing the data they collect, communicating collection and use clearl y, and improving security practices. Federal government surveillance and other collection and \nuse of data is governed by legal protections that help to protect civil liberties and provide for limits on data retention in some cases. Many states have also enacted consumer data privacy protection regimes to address some of these harms. \nHoweve r, these are not yet standard practices, and the United States lacks a comprehensive statutory or regulatory \nframework governing the rights of the public when it comes to personal data. While a patchwork of laws exists to guide the collection and use of personal data in specific contexts, including health, employment, education, and credit, it can be unclear how these laws apply in other contexts and in an increasingly automated societ y. Additional protec\n-\ntions would assure the American public that the automated systems they use are not monitoring their activities, collecting information on their lives, or otherwise surveilling them without context-specific consent or legal authori\n-\nty. \n31",
    "Applying The Blueprint for an AI Bill of Rights \nSENSITIVE DATA: Data and metadata are sensitive if they pertain to an individual in a sensitive domain \n(defined below); are generated by technologies used in a sensitive domain; can be used to infer data from a \nsensitive domain or sensitive data about an individual (such as disability-related data, genomic data, biometric data, behavioral data, geolocation data, data related to interaction with the criminal justice system, relationship history and legal status such as custody and divorce information, and home, work, or school environmental data); or have the reasonable potential to be used in ways that are likely to expose individuals to meaningful harm, such as a loss of privacy or financial harm due to identity theft. Data and metadata generated by or about those who are not yet legal adults is also sensitive, even if not related to a sensitive domain. Such data includes, but is not limited to, numerical, text, image, audio, or video data. \nSENSITIVE DOMAINS: “Sensitive domains” are those in which activities being conducted can cause material \nharms, including significant adverse effects on human rights such as autonomy and dignit y, as well as civil liber-\nties and civil rights. Domains that have historically been singled out as deserving of enhanced data protections \nor where such enhanced protections are reasonably expected by the public include, but are not limited to, health, family planning and care, employment, education, criminal justice, and personal finance. In the context of this framework, such domains are considered sensitive whether or not the specifics of a system context would necessitate coverage under existing la w, and domains and data that are considered sensitive are under-\nstood to change over time based on societal norms and context. \nSURVEILLANCE TECHNOLOGY : “Surveillance technology” refers to products or services marketed for \nor that can be lawfully used to detect, monitor, intercept, collect, exploit, preserve, protect, transmit, and/or \nretain data, identifying information, or communications concerning individuals or groups. This framework \nlimits its focus to both government and commercial use of surveillance technologies when juxtaposed with \nreal-time or subsequent automated analysis and when such systems have a potential for meaningful impact \non individuals’ or communities’ rights, opportunities, or access. UNDERSERVED COMMUNITIES: The term “underserved communities” refers to communities that have \nbeen systematically denied a full opportunity to participate in aspects of economic, social, and civic life, as \nexemplified by the list in the preceding definition of “equit y.” \n11"
]
embeddings = model.encode(sentences)

similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4]

SentenceTransformer based on Alibaba-NLP/gte-large-en-v1.5

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-large-en-v1.5. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Alibaba-NLP/gte-large-en-v1.5
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 tokens
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NewModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'What are the key principles and frameworks mentioned in the white paper that govern the implementation of AI in national security and defense activities?',
    'This white paper recognizes that national security (which includes certain law enforcement and \nhomeland security activities) and defense activities are of increased sensitivity and interest to our nation’s \nadversaries and are often subject to special requirements, such as those governing classified information and \nother protected data. Such activities require alternative, compatible safeguards through existing policies that \ngovern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and \nResponsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and \nFramework. The implementation of these policies to national security and defense activities can be informed by \nthe Blueprint for an AI Bill of Rights where feasible. \nThe Blueprint for an AI Bill of Rights is not intended to, and does not, create any legal right, benefit, or \ndefense, substantive or procedural, enforceable at law or in equity by any party against the United States, its \ndepartments, agencies, or entities, its officers, employees, or agents, or any other person, nor does it constitute a \nwaiver of sovereign immunity. \nCopyright Information \nThis document is a work of the United States Government and is in the public domain (see 17 U.S.C. §105). \n2',
    "APPENDIX\n• OSTP conducted meetings with a variety of stakeholders in the private sector and civil society. Some of these\nmeetings were specifically focused on providing ideas related to the development of the Blueprint for an AI\nBill of Rights while others provided useful general context on the positive use cases, potential harms, and/or\noversight possibilities for these technologies. Participants in these conversations from the private sector and\ncivil society included:\nAdobe \nAmerican Civil Liberties Union (ACLU) The Aspen Commission on Information Disorder The Awood Center The Australian Human Rights Commission Biometrics Institute The Brookings Institute BSA | The Software Alliance Cantellus Group Center for American Progress Center for Democracy and Technology Center on Privacy and Technology at Georgetown Law Christiana Care Color of Change Coworker Data Robot Data Trust Alliance Data and Society Research Institute Deepmind EdSAFE AI Alliance Electronic Privacy Information Center (EPIC) Encode Justice Equal AI Google Hitachi's AI Policy Committee The Innocence Project Institute of Electrical and Electronics Engineers (IEEE) Intuit Lawyers Committee for Civil Rights Under Law Legal Aid Society The Leadership Conference on Civil and Human Rights Meta Microsoft The MIT AI Policy Forum Movement Alliance Project The National Association of Criminal Defense Lawyers O’Neil Risk Consulting & Algorithmic Auditing The Partnership on AI Pinterest The Plaintext Group pymetrics SAP The Security Industry Association Software and Information Industry Association (SIIA) Special Competitive Studies Project Thorn United for Respect University of California at Berkeley Citris Policy Lab University of California at Berkeley Labor Center Unfinished/Project Liberty Upturn US Chamber of Commerce US Chamber of Commerce Technology Engagement Center \nA.I. Working Group\nVibrent HealthWarehouse Worker ResourceCenterWaymap\n62",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.7222
cosine_accuracy@3	0.9815
cosine_accuracy@5	1.0
cosine_accuracy@10	1.0
cosine_precision@1	0.7222
cosine_precision@3	0.3272
cosine_precision@5	0.2
cosine_precision@10	0.1
cosine_recall@1	0.7222
cosine_recall@3	0.9815
cosine_recall@5	1.0
cosine_recall@10	1.0
cosine_ndcg@10	0.8816
cosine_mrr@10	0.841
cosine_map@100	0.841
dot_accuracy@1	0.7037
dot_accuracy@3	0.9815
dot_accuracy@5	1.0
dot_accuracy@10	1.0
dot_precision@1	0.7037
dot_precision@3	0.3272
dot_precision@5	0.2
dot_precision@10	0.1
dot_recall@1	0.7037
dot_recall@3	0.9815
dot_recall@5	1.0
dot_recall@10	1.0
dot_ndcg@10	0.8748
dot_mrr@10	0.8318
dot_map@100	0.8318

Training Details

Training Dataset

Unnamed Dataset

Size: 224 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 224 samples:
sentence_0 sentence_1
type string string
details
min: 23 tokens
mean: 36.01 tokens
max: 55 tokens

min: 22 tokens
mean: 569.67 tokens
max: 1018 tokens

	sentence_0	sentence_1
type	string	string
details	min: 23 tokens mean: 36.01 tokens max: 55 tokens	min: 22 tokens mean: 569.67 tokens max: 1018 tokens

Samples:

sentence_0	sentence_1
`What are the primary objectives outlined in the "Blueprint for an AI Bill of Rights" as it pertains to the American people?`	`BLUEPRINT FOR AN AI B ILL OF RIGHTS MAKING AUTOMATED SYSTEMS WORK FOR THE AMERICAN PEOPLE OCTOBER 2022`
`In what ways does the document propose to ensure that automated systems are designed to work effectively for the benefit of society?`	`BLUEPRINT FOR AN AI B ILL OF RIGHTS MAKING AUTOMATED SYSTEMS WORK FOR THE AMERICAN PEOPLE OCTOBER 2022`
`What is the primary purpose of the Blueprint for an AI Bill of Rights as outlined by the White House Office of Science and Technology Policy?`	About this Document The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was published by the White House Office of Science and Technology Policy in October 2022. This framework was released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered world.” Its release follows a year of public engagement to inform this initiative. The framework is available online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights About the Office of Science and Technology Policy The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office of the President with advice on the scientific, engineering, and technological aspects of the economy, national security, health, foreign relations, the environment, and the technological recovery and use of resources, among other topics. OSTP leads interagency science and technology policy coordination efforts, assists the Office of Management and Budget (OMB) with an annual review and analysis of Federal research and development in budgets, and serves as a source of scientific and technological analysis and judgment for the President with respect to major policies, plans, and programs of the Federal Government. 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 therefore does not require compliance with the principles described herein. It also is not determinative of what the U.S. government’s position will be in any international negotiation. Adoption of these principles may not meet the requirements of existing statutes, regulations, policies, or international instruments, or the requirements of the Federal agencies that enforce them. These principles are not intended to, and do not, prohibit or limit any lawful activity of a government agency, including law enforcement, national security, or intelligence activities. The appropriate application of the principles set forth in this white paper depends significantly on the context in which automated systems are being utilized. In some circumstances, application of these principles in whole or in part may not be appropriate given the intended use of automated systems to achieve government agency missions. Future sector-specific guidance will likely be necessary and important for guiding the use of automated systems in certain settings such as AI systems used as part of school building security or automated health diagnostic systems. The Blueprint for an AI Bill of Rights recognizes that law enforcement activities require a balancing of equities, for example, between the protection of sensitive law enforcement information and the principle of notice; as such, notice may not be appropriate, or may need to be adjusted to protect sources, methods, and other law enforcement equities. Even in contexts where these principles may not apply in whole or in part, federal departments and agencies remain subject to judicial, privacy, and civil liberties oversight as well as existing policies and safeguards that govern automated systems, including, for example, Executive Order 13960, Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government (December 2020). This white paper recognizes that national security (which includes certain law enforcement and homeland security activities) and defense activities are of increased sensitivity and interest to our nation’s adversaries and are often subject to special requirements, such as those governing classified information and other protected data. Such activities require alternative, compatible safeguards through existing policies that govern automated systems and AI, such as the Department of Defense (DOD) AI Ethical Principles and Responsible AI Implementation Pathway and the Intelligence Community (IC) AI Ethics Principles and Framework. The implementation of these policies to national security and defense activities can be informed by the Blueprint for an AI Bill of Rights where feasible.

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 5
per_device_eval_batch_size: 5
num_train_epochs: 5
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 5
per_device_eval_batch_size: 5
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: False
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: False
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
eval_use_gather_object: False
batch_sampler: batch_sampler
multi_dataset_batch_sampler: round_robin

Training Logs

Epoch	Step	cosine_map@100
1.0	45	0.8410

Framework Versions

Python: 3.10.12
Sentence Transformers: 3.1.1
Transformers: 4.44.2
PyTorch: 2.4.1+cu121
Accelerate: 0.34.2
Datasets: 3.0.1
Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for lw2134/policy_gte_large_5

Base model

Alibaba-NLP/gte-large-en-v1.5

Quantized

(9)

this model

Papers for lw2134/policy_gte_large_5

Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Paper • 1908.10084 • Published Aug 27, 2019 • 17

Efficient Natural Language Response Suggestion for Smart Reply

Paper • 1705.00652 • Published May 1, 2017

Evaluation results

Cosine Accuracy@1 on Unknown
self-reported

0.722
Cosine Accuracy@3 on Unknown
self-reported

0.981
Cosine Accuracy@5 on Unknown
self-reported

1.000
Cosine Accuracy@10 on Unknown
self-reported

1.000
Cosine Precision@1 on Unknown
self-reported

0.722
Cosine Precision@3 on Unknown
self-reported

0.327
Cosine Precision@5 on Unknown
self-reported

0.200
Cosine Precision@10 on Unknown
self-reported

0.100