sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:900 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
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
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:900
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'ographics, analyst reports and more.


    Knowledge Center


    Learn about the data privacy, security and governance landscape.


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    Company


    About Us


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    Knowledge Center » Data Privacy Automation


    # What is Irish Data Protection Act of 2018


    By Securiti Research Team


    Published February 2, 2021 / Updated September 28, 2023


    The Irish Data Protection Act, 2018 (Irish DPA) implements the General Data Protection
    Regulation (GDPR) and transposes the European Union Law Enforcement Directive
    in Ireland. Since it incorporates most of the provisions from the GDPR and the
    Law Enforcement Directive with limited additions and deletions as per the national
    law, it is considered to be the principal data protection legislation in Ireland.


    Table of contents


    Rights of Data Subjects


    Responsibilities of data controllers


    Irish DPC Cookie Consent Guidelines


    Automating Compliance


    ### Rights of Data Subjects


    The Irish DPA provides the same rights to data subjects with respect to their
    personal data as that of the GDPR. These rights give data subjects control over
    their data and may be processed under particular conditions and limitations.


    ## Right to be informed


    Data subjects have the right to be informed of when and how their data is being
    used and collected. This refers to the obligation of the data controller to inform
    and notify any relevant details to the data subjects for any important action
    taken on their data.


    ## Right to access


    On a request of the data subject, an organization must provide data subject access
    to his/her personal data and information about the ways personal data has been
    or may have been used, disclosed, or processed by the organization.


    ## Right to restriction of processing


    This right applies when the accuracy of data is contested by the data subject
    and when processing is unlawful and the data subject opposes the deletion of the
    data. Data subjects need to be informed before any such restriction is lifted.


    ## Right to data port'
  sentences:
  - What is the PDPA Act in Malaysia and how does it regulate the processing of personal
    data?
  - What is the purpose of the European Union GDPR?
  - What does the Irish Data Protection Act of 2018 implement in relation to the Law
    Enforcement Directive?
- source_sentence: '

    Learn all about Securiti, our mission and history


    Partner Program


    Join our Partner Program


    Contact Us


    Contact us to learn more or schedule a demo


    News Coverage


    Read about Securiti in the news


    Press Releases


    Find our latest press releases


    Careers


    Join the talented Securiti team


    Blog » Data Consent Automation


    # Irish Guidance on Consent & Cookies – Grace Period ends on 5 October


    By Securiti Research Team


    Published October 1, 2020 / Updated October 6, 2023


    On 6 April, the Data Protection Commission of Ireland (DPC) released a substantive
    Guidance Note on cookies (Guidance) and provided organizations a grace period
    of six months to ensure compliance. After the end of the six- month window, which
    is 5 October 2020, the Irish DPC may act to enforce the Guidance and can hold
    organizations liable for failing to obtain valid consent before the processing
    of cookies.


    This Guidance was issued based on the report released by the DPC on the findings
    of a “cookie sweep survey”. The survey was conducted on around 38 organizations
    operating within the territory of Ireland and around 35 of those companies were
    found to be significantly lacking in cookie compliance requirements. The DPC noticed
    the following non-compliance practices of organizations, among others:


    Dropping of non


    essential cookies on landing pages without obtaining user’s consent,


    The lifespans of most cookies that are dropped are not proportionate to the purposes
    of the cookies,


    Inadequate cookie banners,


    Frequent use of pre


    checked boxes for the processing of non


    essential cookies,


    A lack of stand


    alone cookie policies,


    Failure to fulfill the requirements of a valid consent as per the General Data
    Protection Regulation (GDPR) and the Irish e


    Privacy Regulations.


    Based on its identification of the above non-compliance areas, the Irish DPC released
    the comprehensive Guidance for organizations. The Guidance explains the purposes
    of cookies as well as it adheres to the requirements of the GDPR, e-Privacy Directive,
    and the Guidelines on Consent of the European Data Protection Board, released
    on 4 May 2020 that declared cookie walls invalid.


    _Read EDPB’s Updated Guidelines on Consent_


    The Guidance also complements the landmark decision by the Court of Justice of
    the'
  sentences:
  - What are the requirements for valid consent under the GDPR and Irish e-Privacy
    Regulations according to the Irish DPC's Guidance on cookies?
  - What are the CPPA's duties in enforcing CCPA and CPRA?
  - What legislative measures has Spain taken to protect citizens' personal information
    and data, and how does it compare to Saudi Arabia's data protection law?
- source_sentence: ' are:


    Regulation No. 20 of 2016 concerning Protection of Personal Data in Electronic
    Systems (MoCI Reg);


    Amended Law No. 11 of 2008 on Electronic Information and Transaction (EIT Law);


    Government Regulation No. 71 of 2019 on the Implementation of the Electronic System
    and Transaction (GR 71).


    There are also sectoral regulations that regulate the personal data in a specific
    sector e.g, banking sector, health sector, etc.


    ## Indonesia’s Incoming Personal Data Protection (PDP) Law


    Following delays due to COVID-19, Indonesia is now geared to pass its first Personal
    Data Protection Act (PDP Law). On January 24, 2020, the bill’s final draft was
    submitted to the Indonesian House of Representatives. The PDP law will address
    the much-needed reforms to the country’s data privacy protection rules. The law
    is built on the European Union’s General Data Protection Regulation (GDPR).


    In essence, Indonesia will soon follow the same data subject rights and personal
    data processing regulations set by the European Union in their GDPR.


    The PDP Law will have 72 articles across 15 chapters. These articles and chapters
    will extensively cover data ownership rights, prohibitions on data use, along
    with the collection, storage, processing, and transfer of personal data of Indonesian
    users.


    With Indonesia being an active part of the global economy and attracting millions
    of tourists annually, businesses should quickly align their business operations
    to comply with the upcoming PDP law.


    ## Who Needs to Comply with the PDP Law


    The PDP law will impact local businesses in Indonesia and will also have an impact
    on companies across the globe that deal with Indonesian consumers. . The PDP law
    will apply to any registered company dealing with Indonesian residents, irrespective
    of where they are registered.


    Whether an entity is public or private, local or international, the PDP Law will
    automatically apply to them if they deal with the personal data of Indonesian
    residents. The new PDP Law is expected to apply to all sectors, bringing forward
    comprehensive provisions on personal data protection, both electronically and
    non-electronically.


    ### Material Scope of the PDP Law


    The PDP law will regulate sensitive personal data as well as other personal data
    that may endanger or harm the privacy of the data subject.


    ### Territorial Scope of the Law


    The PDP Bill applies to companies both within and outside of the territory of
    Indonesia where'
  sentences:
  - What does Securiti offer businesses in terms of automating privacy and security
    processes, and why is it important for businesses to embrace robotic automation
    for compliance?
  - What companies will be impacted by Indonesia's Personal Data Protection Law?
  - What is the purpose of the Data Command Center in relation to the company's products
    and solutions?
- source_sentence: 'Contact us to learn more or schedule a demo


    News Coverage


    Read about Securiti in the news


    Press Releases


    Find our latest press releases


    Careers


    Join the talented Securiti team


    Knowledge Center » Data Privacy Automation


    # What is China’s Data Security Law?


    By Securiti Research Team


    Published August 9, 2021 / Updated October 2, 2023


    In China, the following are three main laws that cover the data privacy and data
    security regime:


    The Cybersecurity Law of the People’s Republic of China (the “CSL”), implemented
    on June 1, 2017.


    The Personal Information Protection Law of the People’s Republic of China (the
    “PIPL”), effective from November 1, 2021.


    The Data Security Law (the “DSL”), will be implemented from September 1, 2021.


    The focus of this article is on the DSL that was promulgated to standardize data
    processing activities, ensure data security, promote data development and utilization,
    and protect the legitimate rights and interests of individuals and organizations.


    Table of contents


    Scope of Application and Extraterritorial Effect of DSL


    Penalties for Non


    Compliance


    How Securiti Can Help


    ## Scope of Application and Extraterritorial Effect of DSL


    The DSL applies to and regulates data processing activities by organizations and
    individuals, and security supervision of such activities within the territory
    of China. The DSL also regulates data processing activities conducted outside
    of China that harm China’s national security or the public interest, or the legal
    interests of citizens and organizations in China. It would be right to state that
    DSL has extensive and extra-territorial application. It imposes a number of obligations
    on organizations and individuals even those that are not based in China regarding
    data categorization and classification, data risk controls and risk assessments,
    cross-border data transfers, and data export controls.


    The DSL applies to data recorded in electronic and other forms including digital
    and cyber information, and information recorded in other forms such as paper records.
    Data processing activities regulated by DSL include, without limitation, the collection,
    storage, use, processing, transmission, provision, or disclosure of data.


    Organizations and individuals need to understand and fulfill the following requirements
    of the DSL in order to avoid unnecessary compliance risks and penalties:


    ##'
  sentences:
  - What can organizations do to manage vendor risk in relation to data protection
    and compliance, considering Kenya's Data Protection Act 2019?
  - What are the time limits for organizations to respond to a request under the NZ
    Privacy Act 2020, and what are the requirements for transferring personal information
    outside NZ?
  - How does the Data Security Law in China contribute to data standardization, security
    protection, and development?
- source_sentence: "\n\nOblige with Data Localization Requirements:\n\nOblige with\
    \ Product Safety and Certifications Requirements:\n\nFulfill Content Monitoring\
    \ Requirements:\n\nChina’s Cybersecurity Law (the “CSL”), which went into effect\
    \ on June 1st, 2017, applies to the construction, operation, maintenance, and\
    \ use of information networks, and the supervision and administration of cybersecurity\
    \ in China. The CSL provides guidelines on cybersecurity requirements for safeguarding\
    \ Chinese cyberspace. The law protects the legal interests and rights of organizations\
    \ as well as individuals in China. It also promotes the secure development of\
    \ technology and the digitization of the economy in China. Following entities\
    \ come under the application scope of the CSL:\n\n**Network Operators:\n\n** It\
    \ refers to the owners and administrators of networks and network service providers,\
    \ and could be interpreted to include any companies providing services, or running\
    \ their business through a computer network in China.\n\n**Critical Information\
    \ Infrastructure Operators (CIIOs):\n\n** It refers to operators of critical information\
    \ infrastructure in important industries and sectors (such as information service,\
    \ public service, and e\n\ngovernment) and other information infrastructure that,\
    \ if leaked, may severely threaten the national security, national economy, people’s\
    \ livelihood, and public interests.\n\n**Network Products and Services Providers:\n\
    \n** Organizations that provide information through networks or provide services\
    \ to obtain information, including users, network services providers which provide\
    \ network tools, devices, media, etc.\n\nCompliance with the CSL is not straightforward\
    \ since CSL has several ambiguities and complicated obligations for network operators\
    \ and CIIOs. Additional laws and guidelines will also be considered concerning\
    \ the CSL compliance, including guidelines concerning the security assessment\
    \ of cross- border transfers of personal information and important data, Data\
    \ Security Law (DSL), and recently promulgated Personal Information Protection\
    \ Law (PIPL).\n\nWe have prepared the following compliance checklist for the covered\
    \ entities to ensure compliance with the CSL. Please note that this is not an\
    \ exhaustive compliance list. For a detailed overview of the CSL, please refer\
    \ to our article on What is China’s Cybersecurity Law?\n\n## 1\\. Fulfill Network\
    \ Operations Security Requirements:\n\n## A. Requirements for network operators:\n\
    \nNetwork operators must adopt the following security measures to prevent network\
    \ interference, damage, or unauthorized access, and prevent network data from\
    \ leakage, theft, or alteration:\n\nEstablish internal, \n## 5\\. Oblige with\
    \ Product Safety and Certifications Requirements:\n\n## A. Requirements for Network\
    \ Products and Services Providers:\n\nCybersecurity product manufacturers, security\
    \ service suppliers, and other organizations that provide services through networks\
    \ should oblige with the following requirements:\n\nNetwork products and services\
    \ providers must not set up malicious programs.\n\nUpon discovering a security\
    \ flaw, vulnerability, or another risk in their product or service, they must\
    \ take remedial action immediately, inform users and report the issue to the relevant\
    \ departments.\n\nNetwork product and service providers are required to conduct\
    \ security maintenance for their products and services.\n\n## B. Requirements\
    \ for CIIOs:\n\nCIIOs must, when procuring network products and services that\
    \ may impact national security, submit the products and services to CAC and the\
    \ State Council departments for a review for national security purposes. Critical\
    \ network equipment and special cybersecurity products can only be sold or provided\
    \ after being certified by a qualified establishment, and are in compliance with\
    \ national standards.\n\n## 6\\. Fulfill Content Monitoring Requirements:\n\n\
    According to Article 47 of the CSL, network operators are required to monitor\
    \ the information released by their users for information that is “prohibited\
    \ from being published or transmitted by laws or administrative regulations. If\
    \ such information is discovered, network operators must cease the transmission\
    \ of information, remove the information, keep records, and report any unlawful\
    \ content to relevant authorities. Securiti helps organizations automate their\
    \ privacy management operations using artificial intelligence and robotic automation.\
    \ Request a demo and start your CSL compliance process today.\n\n## Join Our Newsletter\n\
    \nGet all the latest information, law updates and more delivered to your inbox\n\
    \n### Share\n\nCopy\n\n55\n\n### More Stories that May Interest You\n\nView More\n\
    \nSeptember 11, 2023\n\n## Securiti named a Leader in the IDC MarketScape for\
    \ Data Privacy Compliance Software\n\nSecuriti has just been recognized as a Leader\
    \ in the “IDC MarketScape: Worldwide Data Privacy Compliance Software 2023 Vendor\
    \ Assessment” report. This makes us...\n\nView More\n\nMay 10, 2023\n\n## Privacy\n\
    \nby\n\nDesign and Privacy\n\nby\n\nDefault\n\nPrivacy-by-design and privacy-by-default\
    \ are two cornerstone concepts of data protection regulatory frameworks. Thus,\
    \ compliance thereof is an essential legal prerequisite for any entity which...\n\
    \nView More\n\nApril 5,"
  sentences:
  - What are the 13 IPPs of New Zealand's Privacy Act 2020 that apply to all organizations,
    including those outside of New Zealand, offering goods/services to individuals
    in New Zealand or collecting information about individuals in New Zealand?
  - What security measures must network operators adopt to fulfill content monitoring
    requirements under China's Cybersecurity Law, and what obligations do network
    products and services providers and CIIOs have in relation to product safety and
    certifications?
  - How does the PDPA in Malaysia protect personal data in commercial transactions
    and who does it apply to?
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
  results:
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 768
      type: dim_768
    metrics:
    - type: cosine_accuracy@1
      value: 0.08
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.27
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.45
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.67
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.08
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08999999999999998
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06699999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.08
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.27
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.45
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.67
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.33828063637415534
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.23589682539682535
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.24406326043435023
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.06
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.25
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.39
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.66
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.06
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08333333333333331
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07800000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06599999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.06
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.25
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.39
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.66
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.31695711820935435
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.2123928571428571
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.22150012925090945
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.05
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.24
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.38
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07600000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05999999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.24
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.38
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.2931065726305541
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19853174603174606
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.21132630111968292
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.05
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.28
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.36
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.55
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.05
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.09333333333333334
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.07200000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05499999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.05
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.28
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.36
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.55
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.278284909333787
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.19384126984126987
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.20776022518923803
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.04
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.21
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.53
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.04
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.07
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06000000000000001
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05299999999999999
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.04
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.21
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.53
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.25162020276083924
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.16670634920634916
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.177182977653562
      name: Cosine Map@100
---

# SentenceTransformer based on BAAI/bge-base-en-v1.5

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0

### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, '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})
  (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("MugheesAwan11/bge-base-securiti-dataset-1-v7")
# Run inference
sentences = [
    '\n\nOblige with Data Localization Requirements:\n\nOblige with Product Safety and Certifications Requirements:\n\nFulfill Content Monitoring Requirements:\n\nChina’s Cybersecurity Law (the “CSL”), which went into effect on June 1st, 2017, applies to the construction, operation, maintenance, and use of information networks, and the supervision and administration of cybersecurity in China. The CSL provides guidelines on cybersecurity requirements for safeguarding Chinese cyberspace. The law protects the legal interests and rights of organizations as well as individuals in China. It also promotes the secure development of technology and the digitization of the economy in China. Following entities come under the application scope of the CSL:\n\n**Network Operators:\n\n** It refers to the owners and administrators of networks and network service providers, and could be interpreted to include any companies providing services, or running their business through a computer network in China.\n\n**Critical Information Infrastructure Operators (CIIOs):\n\n** It refers to operators of critical information infrastructure in important industries and sectors (such as information service, public service, and e\n\ngovernment) and other information infrastructure that, if leaked, may severely threaten the national security, national economy, people’s livelihood, and public interests.\n\n**Network Products and Services Providers:\n\n** Organizations that provide information through networks or provide services to obtain information, including users, network services providers which provide network tools, devices, media, etc.\n\nCompliance with the CSL is not straightforward since CSL has several ambiguities and complicated obligations for network operators and CIIOs. Additional laws and guidelines will also be considered concerning the CSL compliance, including guidelines concerning the security assessment of cross- border transfers of personal information and important data, Data Security Law (DSL), and recently promulgated Personal Information Protection Law (PIPL).\n\nWe have prepared the following compliance checklist for the covered entities to ensure compliance with the CSL. Please note that this is not an exhaustive compliance list. For a detailed overview of the CSL, please refer to our article on What is China’s Cybersecurity Law?\n\n## 1\\. Fulfill Network Operations Security Requirements:\n\n## A. Requirements for network operators:\n\nNetwork operators must adopt the following security measures to prevent network interference, damage, or unauthorized access, and prevent network data from leakage, theft, or alteration:\n\nEstablish internal, \n## 5\\. Oblige with Product Safety and Certifications Requirements:\n\n## A. Requirements for Network Products and Services Providers:\n\nCybersecurity product manufacturers, security service suppliers, and other organizations that provide services through networks should oblige with the following requirements:\n\nNetwork products and services providers must not set up malicious programs.\n\nUpon discovering a security flaw, vulnerability, or another risk in their product or service, they must take remedial action immediately, inform users and report the issue to the relevant departments.\n\nNetwork product and service providers are required to conduct security maintenance for their products and services.\n\n## B. Requirements for CIIOs:\n\nCIIOs must, when procuring network products and services that may impact national security, submit the products and services to CAC and the State Council departments for a review for national security purposes. Critical network equipment and special cybersecurity products can only be sold or provided after being certified by a qualified establishment, and are in compliance with national standards.\n\n## 6\\. Fulfill Content Monitoring Requirements:\n\nAccording to Article 47 of the CSL, network operators are required to monitor the information released by their users for information that is “prohibited from being published or transmitted by laws or administrative regulations. If such information is discovered, network operators must cease the transmission of information, remove the information, keep records, and report any unlawful content to relevant authorities. Securiti helps organizations automate their privacy management operations using artificial intelligence and robotic automation. Request a demo and start your CSL compliance process today.\n\n## Join Our Newsletter\n\nGet all the latest information, law updates and more delivered to your inbox\n\n### Share\n\nCopy\n\n55\n\n### More Stories that May Interest You\n\nView More\n\nSeptember 11, 2023\n\n## Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software\n\nSecuriti has just been recognized as a Leader in the “IDC MarketScape: Worldwide Data Privacy Compliance Software 2023 Vendor Assessment” report. This makes us...\n\nView More\n\nMay 10, 2023\n\n## Privacy\n\nby\n\nDesign and Privacy\n\nby\n\nDefault\n\nPrivacy-by-design and privacy-by-default are two cornerstone concepts of data protection regulatory frameworks. Thus, compliance thereof is an essential legal prerequisite for any entity which...\n\nView More\n\nApril 5,',
    "What security measures must network operators adopt to fulfill content monitoring requirements under China's Cybersecurity Law, and what obligations do network products and services providers and CIIOs have in relation to product safety and certifications?",
    'How does the PDPA in Malaysia protect personal data in commercial transactions and who does it apply to?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.08       |
| cosine_accuracy@3   | 0.27       |
| cosine_accuracy@5   | 0.45       |
| cosine_accuracy@10  | 0.67       |
| cosine_precision@1  | 0.08       |
| cosine_precision@3  | 0.09       |
| cosine_precision@5  | 0.09       |
| cosine_precision@10 | 0.067      |
| cosine_recall@1     | 0.08       |
| cosine_recall@3     | 0.27       |
| cosine_recall@5     | 0.45       |
| cosine_recall@10    | 0.67       |
| cosine_ndcg@10      | 0.3383     |
| cosine_mrr@10       | 0.2359     |
| **cosine_map@100**  | **0.2441** |

#### Information Retrieval
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.06       |
| cosine_accuracy@3   | 0.25       |
| cosine_accuracy@5   | 0.39       |
| cosine_accuracy@10  | 0.66       |
| cosine_precision@1  | 0.06       |
| cosine_precision@3  | 0.0833     |
| cosine_precision@5  | 0.078      |
| cosine_precision@10 | 0.066      |
| cosine_recall@1     | 0.06       |
| cosine_recall@3     | 0.25       |
| cosine_recall@5     | 0.39       |
| cosine_recall@10    | 0.66       |
| cosine_ndcg@10      | 0.317      |
| cosine_mrr@10       | 0.2124     |
| **cosine_map@100**  | **0.2215** |

#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.05       |
| cosine_accuracy@3   | 0.24       |
| cosine_accuracy@5   | 0.38       |
| cosine_accuracy@10  | 0.6        |
| cosine_precision@1  | 0.05       |
| cosine_precision@3  | 0.08       |
| cosine_precision@5  | 0.076      |
| cosine_precision@10 | 0.06       |
| cosine_recall@1     | 0.05       |
| cosine_recall@3     | 0.24       |
| cosine_recall@5     | 0.38       |
| cosine_recall@10    | 0.6        |
| cosine_ndcg@10      | 0.2931     |
| cosine_mrr@10       | 0.1985     |
| **cosine_map@100**  | **0.2113** |

#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.05       |
| cosine_accuracy@3   | 0.28       |
| cosine_accuracy@5   | 0.36       |
| cosine_accuracy@10  | 0.55       |
| cosine_precision@1  | 0.05       |
| cosine_precision@3  | 0.0933     |
| cosine_precision@5  | 0.072      |
| cosine_precision@10 | 0.055      |
| cosine_recall@1     | 0.05       |
| cosine_recall@3     | 0.28       |
| cosine_recall@5     | 0.36       |
| cosine_recall@10    | 0.55       |
| cosine_ndcg@10      | 0.2783     |
| cosine_mrr@10       | 0.1938     |
| **cosine_map@100**  | **0.2078** |

#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.04       |
| cosine_accuracy@3   | 0.21       |
| cosine_accuracy@5   | 0.3        |
| cosine_accuracy@10  | 0.53       |
| cosine_precision@1  | 0.04       |
| cosine_precision@3  | 0.07       |
| cosine_precision@5  | 0.06       |
| cosine_precision@10 | 0.053      |
| cosine_recall@1     | 0.04       |
| cosine_recall@3     | 0.21       |
| cosine_recall@5     | 0.3        |
| cosine_recall@10    | 0.53       |
| cosine_ndcg@10      | 0.2516     |
| cosine_mrr@10       | 0.1667     |
| **cosine_map@100**  | **0.1772** |

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## Training Details

### Training Dataset

#### Unnamed Dataset


* Size: 900 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
  |         | positive                                                                              | anchor                                                                            |
  |:--------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
  | type    | string                                                                                | string                                                                            |
  | details | <ul><li>min: 159 tokens</li><li>mean: 446.78 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 22.04 tokens</li><li>max: 82 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            | anchor                                                                                                                                     |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------|
  | <code> issues related to the organization's privacy officers, exemption from consent requirements, biometric information registration, and breach reports. The next two stages will come into effect in September 2023 and September 2024, respectively.<br><br>### Hong Kong<br><br>#### Hong Kong Personal Data (Privacy) Ordinance (PDPO)<br><br>**Effective Date** : Since 1995 **Region** : APAC (Asia-Pacific)<br><br>The PDPO is the primary legislation in Hong Kong which was enacted to protect the privacy of individuals’ personal data, and regulate the collection, holding, processing, disclosure, or use of personal data by the organizations. The PDPO applies to private and public sector organizations that process, use, hold, or collect personal data. It covers any organization that deals with the collection and processing of personal data irrespective of the location of processing, provided that the personal data is controlled by the data user based in Hong Kong.<br><br>Resources*<br><br>:<br><br>Hong Kong PDPO Overview<br><br>### Ireland<br><br>#### Irish Data Protection Act (DPA)<br><br>**Effective Date** : May 24, 2018 **Region** : EMEA (Europe, the Middle East and Africa)<br><br>The Irish DPA implements the GDPR into the national law by incorporating most of the provisions of the GDPR with limited additions and deletions. It contains several provisions restricting data subjects’ rights that they generally have under the GDPR, for example, where restrictions are necessary for the enforcement of civil law claims.<br><br>Resources*<br><br>:<br><br>Irish DPA Overview<br><br>Irish Cookie Guidance<br><br>### Japan<br><br>#### Japan’s Act on the Protection of Personal Information (APPI)<br><br>**Effective Date (Amended APPI)** : April 01, 2022 **Region** : APAC (Asia-Pacific)<br><br>Japan’s APPI regulates personal related information and applies to any Personal Information Controller (the “PIC''), that is a person or entity providing personal related information for use in business in Japan. The APPI also applies to the foreign PICs which handle personal information of data subjects (“principals”) in Japan for the purpose of supplying goods or services to those persons.The act ensures the individual’s rights to privacy and also the legal use of personal data for economic development.<br><br>Resources*<br><br>:<br><br>Japan APPI Overview<br><br>### New Zealand<br><br>#### New Zealand</code>                                                                                                                                                                                                | <code>What are the regulations regarding breach reports in New Zealand?</code>                                                             |
  | <code> data. Finally, as previously mentioned, consumers can opt-out of the collection of their sensitive personal data.<br><br>**Means to submit DSR request:<br><br>** A consumer may exercise a right by submitting an authenticated request to a controller, by means prescribed by the controller, specifying the right the consumer intends to exercise. In the instance of processing personal data concerning a child, the parent or legal guardian of the child can exercise a right on the child's behalf. In the case of processing personal data concerning a consumer subject to guardianship, conservatorship, or other protective arrangements under Title 75, Chapter 5, Protection of Persons Under Disability and Their Property, the guardian or the conservator of the consumer shall exercise a right on the consumer's behalf.<br><br>**Time period to fulfill DSR request<br><br>** : A controller shall comply with a consumer's request to exercise a right within 45 days after the day on which a controller had received that particular request. The controller then shall take action on the consumer's request; and inform the consumer of any action taken on the consumer's request.<br><br>**Extension in the time period:<br><br>** An additional 45 days can be granted if it is reasonably necessary to comply with the request, keeping in mind the complexity of the request or the volume of the requests received by the controller. In such cases, the controller is to inform the consumer of the extension and provide reasons for the extension.<br><br>**Charges:<br><br>** Controllers are not allowed to charge a fee for responding to a request under the law apart from certain situations. If the request is a consumer's second or subsequent request within the same 12<br><br>month period, a controller may charge a reasonable fee. A controller may also charge a reasonable fee to cover the administrative costs of complying with a request or refuse to act on a request if:<br><br>the request is excessive, repetitive, technically infeasible as per the law; or<br><br>the controller considers that the primary goal for the submitted request was something other than exercising a right; or<br><br>the request disrupts or imposes an undue burden on the resources of the controller’s business.<br><br>**Appeal against refusal:<br><br>** The data controller may choose to not to take action on a consumer’s DSR request. It must provide the consumer the reasons for which it did not take the action within the 45 days time period of receiving the DSR request. The data controller may also choose to not honor the request</code> | <code>What is the time frame for a controller to fulfill a consumer's request to exercise a right, and what can extend this period?</code> |
  | <code> or use of personal data. This is the same as the term 'data controller.'<br><br>## Data Processor<br><br>Data Processor is a person or entity who processes personal data on behalf of another person or entity (a data user) instead of for his/her purpose(s).<br><br>## Consent<br><br>Consent is not a prerequisite for collecting personal data unless the personal data is used for a new purpose or for direct marketing purposes. Where consent is required, consent means to express and voluntary consent.<br><br>## Data Subjects' Rights under the PDPO:<br><br>The PDPO prescribes the following rights for the data subjects;<br><br>DPP 6 provides data subjects with the right to request access to and correction of their personal data. A data user should give reasons when refusing a data subject’s request to access or correction of his/her personal data.<br><br>Data subjects have the right to be informed by data user(s) regarding the holding of their personal data.<br><br>There is no explicit right to erasure available under the PDPO, however, data subjects can request the data user to delete his/her personal data that is no longer necessary for the processing. Also, data users are not allowed to retain personal data longer than necessary.<br><br>Under the PDPO, there is no right to object to processing (including profiling) available, but data subjects may opt<br><br>out from direct marketing activities.<br><br>## **Who needs to comply with the PDPO**?<br><br>The PDPO applies to private and public sector organizations that process, use, hold, or collect personal data. It covers any organization that deals with the collection and processing of personal data irrespective of the location of processing provided that the personal data is controlled by the data user based in Hong Kong.<br><br>The PDPO provides the following exemptions for the processing of personal data in Part VIII;<br><br>specified public or judicial interests<br><br>domestic or recreational purposes, or for<br><br>employment purposes.<br><br>The PDPO does not directly regulate data processors; therefore, they do not directly come under the application scope of the PDPO. However, data users are required to, by contractual or other means, ensure that their data processors meet the applicable requirements of the PDPO.<br><br>## **Organizations' obligations under the PDPO:**<br><br>PDPO does not explicitly state accountability principles and other privacy management related measures; however, the PCPD recommends</code>                                                                                                | <code>What rights do data subjects have under the PDPO regarding the right to object to processing, and what are the limitations?</code>   |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates

#### All Hyperparameters
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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`: True
- `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_fused
- `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
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional

</details>

### Training Logs
| Epoch     | Step   | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:---------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.6897    | 10     | 8.029         | -                      | -                      | -                      | -                     | -                      |
| 0.9655    | 14     | -             | 0.2004                 | 0.2241                 | 0.2170                 | 0.1726                | 0.2279                 |
| 1.3793    | 20     | 5.6389        | -                      | -                      | -                      | -                     | -                      |
| **1.931** | **28** | **-**         | **0.2078**             | **0.2113**             | **0.2215**             | **0.1772**            | **0.2441**             |

* The bold row denotes the saved checkpoint.

### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- 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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