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yinong333
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finetuned_MiniLM

Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:760
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
finetuned_MiniLM / README.md
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---
base_model: sentence-transformers/all-MiniLM-L6-v2
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:760
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Why is it important to establish clear timelines for data retention,
and what should happen to data once those timelines are reached?
sentences:
- "Technology \nDignari \nDouglas Goddard \nEdgar Dworsky \nElectronic Frontier\
\ Foundation \nElectronic Privacy Information \nCenter, Center for Digital \n\
Democracy, and Consumer \nFederation of America \nFaceTec \nFight for the Future\
\ \nGanesh Mani \nGeorgia Tech Research Institute \nGoogle \nHealth Information\
\ Technology \nResearch and Development \nInteragency Working Group \nHireVue\
\ \nHR Policy Association \nID.me \nIdentity and Data Sciences \nLaboratory at\
\ Science Applications \nInternational Corporation \nInformation Technology and\
\ \nInnovation Foundation \nInformation Technology Industry \nCouncil \nInnocence\
\ Project \nInstitute for Human-Centered \nArtificial Intelligence at Stanford\
\ \nUniversity \nIntegrated Justice Information \nSystems Institute \nInternational\
\ Association of Chiefs \nof Police \nInternational Biometrics + Identity \nAssociation\
\ \nInternational Business Machines \nCorporation \nInternational Committee of\
\ the Red \nCross \nInventionphysics \niProov \nJacob Boudreau \nJennifer K. Wagner,\
\ Dan Berger,"
- "new privacy risks and implementing appropriate mitigation measures, which may\
\ include express consent. \nClear timelines for data retention should be established,\
\ with data deleted as soon as possible in accordance \nwith legal or policy-based\
\ limitations. Determined data retention timelines should be documented and justi­\n\
fied. \nRisk identification and mitigation. Entities that collect, use, share,\
\ or store sensitive data should \nattempt to proactively identify harms and seek\
\ to manage them so as to avoid, mitigate, and respond appropri­\nately to identified\
\ risks. Appropriate responses include determining not to process data when the\
\ privacy risks \noutweigh the benefits or implementing measures to mitigate acceptable\
\ risks. Appropriate responses do not \ninclude sharing or transferring the privacy\
\ risks to users via notice or consent requests where users could not \nreasonably\
\ be expected to understand the risks without further support."
- '55. Data & Trust Alliance. Algorithmic Bias Safeguards for Workforce: Overview.
Jan. 2022. https://
dataandtrustalliance.org/Algorithmic_Bias_Safeguards_for_Workforce_Overview.pdf
56. Section 508.gov. IT Accessibility Laws and Policies. Access Board. https://www.section508.gov/
manage/laws-and-policies/
67'
- source_sentence: What is the purpose of the NIST AI Risk Management Framework?
sentences:
- "TABLE OF CONTENTS\nFROM PRINCIPLES TO PRACTICE: A TECHNICAL COMPANION TO THE\
\ BLUEPRINT \nFOR AN AI BILL OF RIGHTS \n \nUSING THIS TECHNICAL COMPANION\n \n\
SAFE AND EFFECTIVE SYSTEMS\n \nALGORITHMIC DISCRIMINATION PROTECTIONS\n \nDATA\
\ PRIVACY\n \nNOTICE AND EXPLANATION\n \nHUMAN ALTERNATIVES, CONSIDERATION, AND\
\ FALLBACK\nAPPENDIX\n \nEXAMPLES OF AUTOMATED SYSTEMS\n \nLISTENING TO THE AMERICAN\
\ PEOPLE\nENDNOTES \n12\n14\n15\n23\n30\n40\n46\n53\n53\n55\n63\n13"
- "health diagnostic systems. \nThe Blueprint for an AI Bill of Rights recognizes\
\ that law enforcement activities require a balancing of \nequities, for example,\
\ between the protection of sensitive law enforcement information and the principle\
\ of \nnotice; as such, notice may not be appropriate, or may need to be adjusted\
\ to protect sources, methods, and \nother law enforcement equities. Even in contexts\
\ where these principles may not apply in whole or in part, \nfederal departments\
\ and agencies remain subject to judicial, privacy, and civil liberties oversight\
\ as well as \nexisting policies and safeguards that govern automated systems,\
\ including, for example, Executive Order 13960, \nPromoting the Use of Trustworthy\
\ Artificial Intelligence in the Federal Government (December 2020). \nThis 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"
- "mitigate risks posed by the use of AI to companies’ reputation, legal responsibilities,\
\ and other product safety \nand effectiveness concerns. \nThe Office of Management\
\ and Budget (OMB) has called for an expansion of opportunities \nfor meaningful\
\ stakeholder engagement in the design of programs and services. OMB also \npoints\
\ to numerous examples of effective and proactive stakeholder engagement, including\
\ the Community-\nBased Participatory Research Program developed by the National\
\ Institutes of Health and the participatory \ntechnology assessments developed\
\ by the National Oceanic and Atmospheric Administration.18\nThe National Institute\
\ of Standards and Technology (NIST) is developing a risk \nmanagement framework\
\ to better manage risks posed to individuals, organizations, and \nsociety by\
\ AI.19 The NIST AI Risk Management Framework, as mandated by Congress, is intended\
\ for \nvoluntary use to help incorporate trustworthiness considerations into\
\ the design, development, use, and"
- source_sentence: What were the main topics discussed in the panel focused on consumer
rights and protections in an automated society?
sentences:
- "context, or may be more speculative and therefore uncertain. \nAI risks can differ\
\ from or intensify traditional software risks. Likewise, GAI can exacerbate existing\
\ AI \nrisks, and creates unique risks. GAI risks can vary along many dimensions:\
\ \n• \nStage of the AI lifecycle: Risks can arise during design, development,\
\ deployment, operation, \nand/or decommissioning. \n• \nScope: Risks may exist\
\ at individual model or system levels, at the application or implementation \n\
levels (i.e., for a specific use case), or at the ecosystem level – that is, beyond\
\ a single system or \norganizational context. Examples of the latter include\
\ the expansion of “algorithmic \nmonocultures,3” resulting from repeated use\
\ of the same model, or impacts on access to \nopportunity, labor markets, and\
\ the creative economies.4 \n• \nSource of risk: Risks may emerge from factors\
\ related to the design, training, or operation of the"
- "specific and empirically well-substantiated negative risk to public safety (or\
\ has \nalready caused harm). \nCBRN Information or Capabilities; \nDangerous,\
\ Violent, or Hateful \nContent \nAI Actor Tasks: Governance and Oversight"
- "theme, exploring current challenges and concerns and considering what an automated\
\ society that \nrespects democratic values should look like. These discussions\
\ focused on the topics of consumer \nrights and protections, the criminal justice\
\ system, equal opportunities and civil justice, artificial \nintelligence and\
\ democratic values, social welfare and development, and the healthcare system.\
\ \nSummaries of Panel Discussions: \nPanel 1: Consumer Rights and Protections.\
\ This event explored the opportunities and challenges for \nindividual consumers\
\ and communities in the context of a growing ecosystem of AI-enabled consumer\
\ \nproducts, advanced platforms and services, “Internet of Things” (IoT) devices,\
\ and smart city products and \nservices. \nWelcome:\n•\nRashida Richardson, Senior\
\ Policy Advisor for Data and Democracy, White House Office of Science and\nTechnology\
\ Policy\n•\nKaren Kornbluh, Senior Fellow and Director of the Digital Innovation\
\ and Democracy Initiative, German\nMarshall Fund"
- source_sentence: How did the input from various stakeholders contribute to the development
of the Blueprint for an AI Bill of Rights?
sentences:
- "SECTION TITLE\nAPPENDIX\nListening to the American People \nThe White House Office\
\ of Science and Technology Policy (OSTP) led a yearlong process to seek and distill\
\ \ninput from people across the country – from impacted communities to industry\
\ stakeholders to \ntechnology developers to other experts across fields and sectors,\
\ as well as policymakers across the Federal \ngovernment – on the issue of algorithmic\
\ and data-driven harms and potential remedies. Through panel \ndiscussions, public\
\ listening sessions, private meetings, a formal request for information, and\
\ input to a \npublicly accessible and widely-publicized email address, people\
\ across the United States spoke up about \nboth the promises and potential harms\
\ of these technologies, and played a central role in shaping the \nBlueprint\
\ for an AI Bill of Rights. \nPanel Discussions to Inform the Blueprint for An\
\ AI Bill of Rights"
- "About this Document \nThe Blueprint for an AI Bill of Rights: Making Automated\
\ Systems Work for the American People was \npublished by the White House Office\
\ of Science and Technology Policy in October 2022. This framework was \nreleased\
\ one year after OSTP announced the launch of a process to develop “a bill of\
\ rights for an AI-powered \nworld.” Its release follows a year of public engagement\
\ to inform this initiative. The framework is available \nonline at: https://www.whitehouse.gov/ostp/ai-bill-of-rights\
\ \nAbout the Office of Science and Technology Policy \nThe Office of Science\
\ and Technology Policy (OSTP) was established by the National Science and Technology\
\ \nPolicy, Organization, and Priorities Act of 1976 to provide the President\
\ and others within the Executive Office \nof the President with advice on the\
\ scientific, engineering, and technological aspects of the economy, national"
- "Technology Policy\n•\nKaren Kornbluh, Senior Fellow and Director of the Digital\
\ Innovation and Democracy Initiative, German\nMarshall Fund\nModerator: \nDevin\
\ E. Willis, Attorney, Division of Privacy and Identity Protection, Bureau of\
\ Consumer Protection, Federal \nTrade Commission \nPanelists: \n•\nTamika L.\
\ Butler, Principal, Tamika L. Butler Consulting\n•\nJennifer Clark, Professor\
\ and Head of City and Regional Planning, Knowlton School of Engineering, Ohio\n\
State University\n•\nCarl Holshouser, Senior Vice President for Operations and\
\ Strategic Initiatives, TechNet\n•\nSurya Mattu, Senior Data Engineer and Investigative\
\ Data Journalist, The Markup\n•\nMariah Montgomery, National Campaign Director,\
\ Partnership for Working Families\n55"
- source_sentence: What legal action did the Federal Trade Commission take against
Kochava regarding data tracking?
sentences:
- "DATA PRIVACY \nEXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE\nDOMAINS\n•\n\
Continuous positive airway pressure machines gather data for medical purposes,\
\ such as diagnosing sleep\napnea, and send usage data to a patient’s insurance\
\ company, which may subsequently deny coverage for the\ndevice based on usage\
\ data. Patients were not aware that the data would be used in this way or monitored\n\
by anyone other than their doctor.70 \n•\nA department store company used predictive\
\ analytics applied to collected consumer data to determine that a\nteenage girl\
\ was pregnant, and sent maternity clothing ads and other baby-related advertisements\
\ to her\nhouse, revealing to her father that she was pregnant.71\n•\nSchool audio\
\ surveillance systems monitor student conversations to detect potential \"stress\
\ indicators\" as\na warning of potential violence.72 Online proctoring systems\
\ claim to detect if a student is cheating on an"
- 'ENDNOTES
75. See., e.g., Sam Sabin. Digital surveillance in a post-Roe world. Politico.
May 5, 2022. https://
www.politico.com/newsletters/digital-future-daily/2022/05/05/digital-surveillance-in-a-post-roe­
world-00030459; Federal Trade Commission. FTC Sues Kochava for Selling Data that
Tracks People at
Reproductive Health Clinics, Places of Worship, and Other Sensitive Locations.
Aug. 29, 2022. https://
www.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-kochava-selling-data-tracks-people­
reproductive-health-clinics-places-worship-other
76. Todd Feathers. This Private Equity Firm Is Amassing Companies That Collect
Data on America’s
Children. The Markup. Jan. 11, 2022.
https://themarkup.org/machine-learning/2022/01/11/this-private-equity-firm-is-amassing-companies­
that-collect-data-on-americas-children
77. Reed Albergotti. Every employee who leaves Apple becomes an ‘associate’: In
job databases used by'
- 'ENDNOTES
1.The Executive Order On Advancing Racial Equity and Support for Underserved Communities
Through the
Federal Government. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/20/executive
order-advancing-racial-equity-and-support-for-underserved-communities-through-the-federal-government/
2. The White House. Remarks by President Biden on the Supreme Court Decision to
Overturn Roe v. Wade. Jun.
24, 2022. https://www.whitehouse.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president­
biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/
3. The White House. Join the Effort to Create A Bill of Rights for an Automated
Society. Nov. 10, 2021. https://
www.whitehouse.gov/ostp/news-updates/2021/11/10/join-the-effort-to-create-a-bill-of-rights-for-an­
automated-society/
4. U.S. Dept. of Health, Educ. & Welfare, Report of the Sec’y’s Advisory Comm.
on Automated Pers. Data Sys.,'
model-index:
- name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7214285714285714
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8785714285714286
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.95
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9714285714285714
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7214285714285714
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2928571428571428
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18999999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09714285714285713
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7214285714285714
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8785714285714286
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.95
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9714285714285714
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8514639427234363
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8122108843537416
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8142292826221397
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.7214285714285714
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.8785714285714286
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.95
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.9714285714285714
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.7214285714285714
name: Dot Precision@1
- type: dot_precision@3
value: 0.2928571428571428
name: Dot Precision@3
- type: dot_precision@5
value: 0.18999999999999995
name: Dot Precision@5
- type: dot_precision@10
value: 0.09714285714285713
name: Dot Precision@10
- type: dot_recall@1
value: 0.7214285714285714
name: Dot Recall@1
- type: dot_recall@3
value: 0.8785714285714286
name: Dot Recall@3
- type: dot_recall@5
value: 0.95
name: Dot Recall@5
- type: dot_recall@10
value: 0.9714285714285714
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8514639427234363
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8122108843537416
name: Dot Mrr@10
- type: dot_map@100
value: 0.8142292826221397
name: Dot Map@100
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision 8b3219a92973c328a8e22fadcfa821b5dc75636a -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("yinong333/finetuned_MiniLM")
# Run inference
sentences = [
'What legal action did the Federal Trade Commission take against Kochava regarding data tracking?',
'ENDNOTES\n75. See., e.g., Sam Sabin. Digital surveillance in a post-Roe world. Politico. May 5, 2022. https://\nwww.politico.com/newsletters/digital-future-daily/2022/05/05/digital-surveillance-in-a-post-roe\xad\nworld-00030459; Federal Trade Commission. FTC Sues Kochava for Selling Data that Tracks People at\nReproductive Health Clinics, Places of Worship, and Other Sensitive Locations. Aug. 29, 2022. https://\nwww.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-kochava-selling-data-tracks-people\xad\nreproductive-health-clinics-places-worship-other\n76. Todd Feathers. This Private Equity Firm Is Amassing Companies That Collect Data on America’s\nChildren. The Markup. Jan. 11, 2022.\nhttps://themarkup.org/machine-learning/2022/01/11/this-private-equity-firm-is-amassing-companies\xad\nthat-collect-data-on-americas-children\n77. Reed Albergotti. Every employee who leaves Apple becomes an ‘associate’: In job databases used by',
'DATA PRIVACY \nEXTRA PROTECTIONS FOR DATA RELATED TO SENSITIVE\nDOMAINS\n•\nContinuous positive airway pressure machines gather data for medical purposes, such as diagnosing sleep\napnea, and send usage data to a patient’s insurance company, which may subsequently deny coverage for the\ndevice based on usage data. Patients were not aware that the data would be used in this way or monitored\nby anyone other than their doctor.70 \n•\nA department store company used predictive analytics applied to collected consumer data to determine that a\nteenage girl was pregnant, and sent maternity clothing ads and other baby-related advertisements to her\nhouse, revealing to her father that she was pregnant.71\n•\nSchool audio surveillance systems monitor student conversations to detect potential "stress indicators" as\na warning of potential violence.72 Online proctoring systems claim to detect if a student is cheating on an',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7214 |
| cosine_accuracy@3 | 0.8786 |
| cosine_accuracy@5 | 0.95 |
| cosine_accuracy@10 | 0.9714 |
| cosine_precision@1 | 0.7214 |
| cosine_precision@3 | 0.2929 |
| cosine_precision@5 | 0.19 |
| cosine_precision@10 | 0.0971 |
| cosine_recall@1 | 0.7214 |
| cosine_recall@3 | 0.8786 |
| cosine_recall@5 | 0.95 |
| cosine_recall@10 | 0.9714 |
| cosine_ndcg@10 | 0.8515 |
| cosine_mrr@10 | 0.8122 |
| **cosine_map@100** | **0.8142** |
| dot_accuracy@1 | 0.7214 |
| dot_accuracy@3 | 0.8786 |
| dot_accuracy@5 | 0.95 |
| dot_accuracy@10 | 0.9714 |
| dot_precision@1 | 0.7214 |
| dot_precision@3 | 0.2929 |
| dot_precision@5 | 0.19 |
| dot_precision@10 | 0.0971 |
| dot_recall@1 | 0.7214 |
| dot_recall@3 | 0.8786 |
| dot_recall@5 | 0.95 |
| dot_recall@10 | 0.9714 |
| dot_ndcg@10 | 0.8515 |
| dot_mrr@10 | 0.8122 |
| dot_map@100 | 0.8142 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 760 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 760 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 20.96 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 167.91 tokens</li><li>max: 256 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the purpose of the AI Bill of Rights mentioned in the context?</code> | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> |
| <code>When was the Blueprint for an AI Bill of Rights published?</code> | <code>BLUEPRINT FOR AN <br>AI BILL OF <br>RIGHTS <br>MAKING AUTOMATED <br>SYSTEMS WORK FOR <br>THE AMERICAN PEOPLE <br>OCTOBER 2022</code> |
| <code>What was the purpose of the Blueprint for an AI Bill of Rights published by the White House Office of Science and Technology Policy?</code> | <code>About this Document <br>The Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People was <br>published by the White House Office of Science and Technology Policy in October 2022. This framework was <br>released one year after OSTP announced the launch of a process to develop “a bill of rights for an AI-powered <br>world.” Its release follows a year of public engagement to inform this initiative. The framework is available <br>online at: https://www.whitehouse.gov/ostp/ai-bill-of-rights <br>About the Office of Science and Technology Policy <br>The Office of Science and Technology Policy (OSTP) was established by the National Science and Technology <br>Policy, Organization, and Priorities Act of 1976 to provide the President and others within the Executive Office <br>of the President with advice on the scientific, engineering, and technological aspects of the economy, national</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
128
],
"matryoshka_weights": [
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 30
- `per_device_eval_batch_size`: 30
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 30
- `per_device_eval_batch_size`: 30
- `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
</details>
### Training Logs
| Epoch | Step | cosine_map@100 |
|:------:|:----:|:--------------:|
| 1.0 | 26 | 0.7610 |
| 1.9231 | 50 | 0.8047 |
| 2.0 | 52 | 0.8051 |
| 3.0 | 78 | 0.8116 |
| 3.8462 | 100 | 0.8142 |
### 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: 2.19.2
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@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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