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MugheesAwan11
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bge-base-securiti-dataset-1-v11

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:900
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
bge-base-securiti-dataset-1-v11 / README.md
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metadata
language:
  - en
license: apache-2.0
library_name: sentence-transformers
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:900
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
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
widget:
  - source_sentence: >-
      Automation View Cookie Consent View Universal Consent View Vendor Risk
      Assessment View Breach Management View Privacy Policy Management View
      Privacy Center View Learn more Security Identify data risk and enable
      protection & control Data Security Posture Management View Data Access
      Intelligence & Governance View Data Risk Management View Data Breach
      Analysis View Learn more Governance Optimize Data Governance with granular
      insights into your data Data Catalog View Data Lineage View Data Quality
      View Data Controls Orchestrator View Solutions Technologies Covering you
      everywhere with 1000+ integrations across data systems. Snowflake View AWS
      View Microsoft 365 View Salesforce View Workday View GCP View Azure View
      Oracle View Learn more Regulations Automate compliance with global privacy
      regulations. US California CCPA View US California CPRA View European
      Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View
      Brazil's LGPD View \+ More View Learn more Roles Identify data risk and
      enable protection & control. Privacy View Security View Governance View
      Marketing View Resources Blog Read through our articles written by
      industry experts Collateral Product brochures, white papers, infographics,
      analyst reports and more. Knowledge Center Learn about the data privacy,
      security and governance landscape. Securiti Education Courses and
      Certifications for data privacy, security and governance professionals.
      Company About Us 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
    sentences:
      - >-
        What does DSPM stand for in Privacy Center and its related products and
        services?
      - Which agency protects Californians' digital privacy under CPRA?
      - >-
        How does Data Security Posture Management help with data risk
        identification and control?
  - source_sentence: >-
      the affected data subjects and regulatory authority about the breach and
      whether any of their information has been compromised as a result. ###
      Data Protection Impact Assessment There is no requirement for conducting
      data protection impact assessment under the PDPA. ### Record of Processing
      Activities A data controller must keep and maintain a record of any
      privacy notice, data subject request, or any other information relating to
      personal data processed by him in the form and manner that may be
      determined by the regulatory authority. ### Cross Border Data Transfer
      Requirements The PDPA provides that personal data can be transferred out
      of Malaysia only when the recipient country is specified as adequate in
      the Official Gazette. The personal data of data subjects can not be
      disclosed without the consent of the data subject. The PDPA provides the
      following exceptions to the cross border data transfer requirements: Where
      the consent of data subject is obtained for transfer; or Where the
      transfer is necessary for the performance of contract between the parties;
      The transfer is for the purpose of any legal proceedings or for the
      purpose of obtaining legal advice or for establishing, exercising or
      defending legal rights; The data user has taken all reasonable precautions
      and exercised all due diligence to ensure that the personal data will not
      in that place be processed in any manner which, if that place is Malaysia,
      would be a contravention of this PDPA; The transfer is necessary in order
      to protect the vital interests of the data subject; or The transfer is
      necessary as being in the public interest in circumstances as determined
      by the Minister. ## Data Subject Rights The data subjects or the person
      whose data is being collected has certain rights under the PDPA. The most
      prominent rights can be categorized under the following: ## Right to
      withdraw consent The PDPA, like some of the other landmark data protection
      laws such as CPRA and GDPR gives data subjects the right to revoke their
      consent at any time by way of written notice from having their data
      collected processed. ## Right to access and rectification As per this
      right, anyone whose data has been collected has the right to request to
      review their personal data and have it updated. The onus is on the data
      handlers to respond to such a request as soon as possible while also
      making it easier for data subjects on how they can request access to their
      personal data. ## Right to data portability Data subjects have the right
      to request that their data be stored in a manner where it
    sentences:
      - >-
        How can data subjects exercise their right to data portability under the
        PDPA?
      - >-
        What are the potential fines and penalties for non-compliance with
        POPIA?
      - >-
        What actions must organizations take under New Zealand's Privacy Act
        2020, including breach notifications and Data Protection Officer
        appointment?
  - source_sentence: >-
      Securiti, our mission is to enable enterprises to safely harness the
      incredible power of data and the cloud by controlling the complex
      security, privacy and compliance risks. Copyright (C) 2023 Securiti
      Sitemap XML Sitemap #### Newsletter #### Company About Us Careers Contact
      Us Partner Program News Coverage Press Releases #### Resources Blog
      Collateral Knowledge Center Securiti Education Privacy Center Free Do Not
      Sell Tool What is DSPM #### Terms Terms & Policies Security & Compliance
      Manage cookie preferences My Privacy Center #### Get in touch email
      protected 300 Santana Row Suite 450. San Jose, CA 95128 Contact Us
      Schedule a Demo Products By Role Data Command Center Sensitive Data
      Intelligence Privacy Security Governance Data Controls Orchestrator By Use
      Cases Back Asset Discovery Asset Discovery Data Discovery & Classification
      Data Discovery & Classification Sensitive Data Catalog Sensitive Data
      Catalog People Data Graph People Data Graph Data Mapping Automation View
      Data Subject Request Automation View People Data Graph View Assessment
      Automation View Cookie Consent View Universal Consent View Vendor Risk
      Assessment View Breach Management View Privacy Policy Management View
      Privacy Center View Data Security Posture Management View Data Access
      Intelligence & Governance View Data Risk Management View Data Breach
      Analysis View Data Catalog View Data Lineage View Data Quality View Asset
      and Data Discovery View Data Access Intelligence & Governance View Data
      Privacy Automation View Sensitive Data Intelligence View Data Flow
      Intelligence & Governance View Data Consent Automation View Data Security
      Posture Management View Data Breach Impact Analysis & Response View Data
      Catalog View Data Lineage View Solutions
    sentences:
      - >-
        What is the purpose of the "Terms & Policies" section in the context of
        iti Education?
      - >-
        How does SDI contribute to Securiti's mission of controlling security,
        privacy, and compliance risks in data and cloud usage?
      - >-
        What is the definition of personal data under Singapore's PDPA and how
        does it compare to other countries' data protection laws?
  - source_sentence: >-
      View Data Quality View Data Controls Orchestrator View Solutions
      Technologies Covering you everywhere with 1000+ integrations across data
      systems. Snowflake View AWS View Microsoft 365 View Salesforce View
      Workday View GCP View Azure View Oracle View Learn more Regulations
      Automate compliance with global privacy regulations. US California CCPA
      View US California CPRA View European Union GDPR View Thailand’s PDPA View
      China PIPL View Canada PIPEDA View Brazil's LGPD View \+ More View Learn
      more Roles Identify data risk and enable protection & control. Privacy
      View Security View Governance View Marketing View Resources Blog Read
      through our articles written by industry experts Collateral Product
      brochures, white papers, infographics, analyst reports and more. Knowledge
      Center Learn about the data privacy, security and governance landscape.
      Securiti Education Courses and Certifications for data privacy, security
      and governance professionals. Company About Us 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 Privacy Automation #
      International data transfers under New Zealand’s new Privacy Act By
      Securiti Research Team Published December 3, 2020 / Updated October 3,
      2023 Table of contents Step 1: Assess whether the foreign entity provides
      comparable privacy safeguards Step 2: Enter into a contract with the data
      recipient ensuring comparable privacy safeguards Step 3: Take express
      authorisation of the concerned data subject Step 4: Confirm whether the
      foreign entity or person is part of
    sentences:
      - >-
        How can organizations automate compliance with Uganda's Data Protection
        and Privacy Act 2019 for data subject requests?
      - >-
        What information is the data controller required to provide to the data
        subject under PDPL?
      - What are the solutions and technologies offered by Securiti?
  - source_sentence: >-
      View GCP View Azure View Oracle View US California CCPA View US California
      CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View
      Canada PIPEDA View Brazil's LGPD View \+ More View Privacy View Security
      View Governance View Marketing View Resources Blog View Collateral View
      Knowledge Center View Securiti Education View Company About Us View
      Partner Program View Contact Us View News Coverage View Press Releases
      View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide
      Leader in Data Privacy View Events Spotlight Talks Education Contact Us
      Schedule a Demo Products By Use Cases By Roles Data Command Center View
      Learn more Asset and Data Discovery Discover dark and native data assets
      Learn more Data Access Intelligence & Governance Identify which users have
      access to sensitive data and prevent unauthorized access Learn more Data
      Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation |
      Assessment Automation | Vendor Assessment | Breach Management | Privacy
      Notice Learn more Sensitive Data Intelligence Discover & Classify
      Structured and Unstructured Data | People Data Graph Learn more Data Flow
      Intelligence & Governance Prevent sensitive data sprawl through real-time
      streaming platforms Learn more Data Consent Automation First Party Consent
      | Third Party & Cookie Consent Learn more Data Security Posture Management
      Secure sensitive data in hybrid multicloud and SaaS environments Learn
      more Data Breach Impact Analysis & Response Analyze impact of a data
      breach and coordinate response per global regulatory obligations Learn
      more Data Catalog Automatically catalog datasets and enable users to find,
      understand, trust and access data Learn more Data Lineage ,  GCP View
      Azure View Oracle View US California CCPA View US California CPRA View
      European Union GDPR View Thailand’s PDPA View China PIPL View Canada
      PIPEDA View Brazil's LGPD View \+ More View Privacy View Security View
      Governance View Marketing View Resources Blog View Collateral View
      Knowledge Center View Securiti Education View Company About Us View
      Partner Program View Contact Us View News Coverage View Press Releases
      View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide
      Leader in Data Privacy View Events Spotlight Talks Education Contact Us
      Schedule a Demo Products By Use Cases By Roles Data Command Center View
      Learn more Asset and Data Discovery Discover dark and native data assets
      Learn more Data Access Intelligence & Governance Identify which users have
      access to sensitive data and prevent unauthorized access Learn more Data
      Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation |
      Assessment Automation | Vendor Assessment | Breach Management | Privacy
      Notice Learn more Sensitive Data Intelligence Discover & Classify
      Structured and Unstructured Data | People Data Graph Learn more Data Flow
      Intelligence & Governance Prevent sensitive data sprawl through real-time
      streaming platforms Learn more Data Consent Automation First Party Consent
      | Third Party & Cookie Consent Learn more Data Security Posture Management
      Secure sensitive data in hybrid multicloud and SaaS environments Learn
      more Data Breach Impact Analysis & Response Analyze impact of a data
      breach and coordinate response per global regulatory obligations Learn
      more Data Catalog Automatically catalog datasets and enable users to find,
      understand, trust and access data Learn more Data Lineage Track changes
    sentences:
      - >-
        What is the name of the data protection law in Switzerland and how does
        it align with GDPR?
      - >-
        What products and solutions does Oracle offer for data privacy and
        security, and how do they comply with regulations in different regions
        and countries?
      - >-
        What are the key provisions and changes in the Personal Data Protection
        Bill 2021 in India, and how can Securiti assist with compliance?
pipeline_tag: sentence-similarity
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: 0.1
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.36
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.52
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.75
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12000000000000002
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10399999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07499999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.36
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.52
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.75
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.38525834974191675
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2732420634920635
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2814101237233525
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.09
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.37
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.51
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.74
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.09
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12333333333333334
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10199999999999998
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07399999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.09
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.37
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.51
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.74
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3758407177747965
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2634761904761904
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.27248653158220537
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.1
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.35
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.47
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.72
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11666666666666668
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09399999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07199999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.1
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.35
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.47
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.72
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.36999387575978315
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2624880952380952
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2732550259916666
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.07
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.33
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.48
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.71
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.07
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11000000000000001
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09599999999999997
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.07099999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.07
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.33
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.48
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.71
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3526473529461716
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.24250396825396822
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.25319653384818785
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.06
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.32
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.46
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.06
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.10666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.09199999999999997
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.06799999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.06
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.32
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.46
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.33933653623127435
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.23408730158730165
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.24510801120449394
            name: Cosine Map@100

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

This is a sentence-transformers model finetuned from 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
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Language: en
  • License: apache-2.0

Model Sources

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:

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("MugheesAwan11/bge-base-securiti-dataset-1-v11")
# Run inference
sentences = [
    "View GCP View Azure View Oracle View US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \\+ More View Privacy View Security View Governance View Marketing View Resources Blog View Collateral View Knowledge Center View Securiti Education View Company About Us View Partner Program View Contact Us View News Coverage View Press Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover & Classify Structured and Unstructured Data | People Data Graph Learn more Data Flow Intelligence & Governance Prevent sensitive data sprawl through real-time streaming platforms Learn more Data Consent Automation First Party Consent | Third Party & Cookie Consent Learn more Data Security Posture Management Secure sensitive data in hybrid multicloud and SaaS environments Learn more Data Breach Impact Analysis & Response Analyze impact of a data breach and coordinate response per global regulatory obligations Learn more Data Catalog Automatically catalog datasets and enable users to find, understand, trust and access data Learn more Data Lineage ,  GCP View Azure View Oracle View US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada PIPEDA View Brazil's LGPD View \\+ More View Privacy View Security View Governance View Marketing View Resources Blog View Collateral View Knowledge Center View Securiti Education View Company About Us View Partner Program View Contact Us View News Coverage View Press Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover & Classify Structured and Unstructured Data | People Data Graph Learn more Data Flow Intelligence & Governance Prevent sensitive data sprawl through real-time streaming platforms Learn more Data Consent Automation First Party Consent | Third Party & Cookie Consent Learn more Data Security Posture Management Secure sensitive data in hybrid multicloud and SaaS environments Learn more Data Breach Impact Analysis & Response Analyze impact of a data breach and coordinate response per global regulatory obligations Learn more Data Catalog Automatically catalog datasets and enable users to find, understand, trust and access data Learn more Data Lineage Track changes",
    'What products and solutions does Oracle offer for data privacy and security, and how do they comply with regulations in different regions and countries?',
    'What are the key provisions and changes in the Personal Data Protection Bill 2021 in India, and how can Securiti assist with compliance?',
]
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]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.1
cosine_accuracy@3 0.36
cosine_accuracy@5 0.52
cosine_accuracy@10 0.75
cosine_precision@1 0.1
cosine_precision@3 0.12
cosine_precision@5 0.104
cosine_precision@10 0.075
cosine_recall@1 0.1
cosine_recall@3 0.36
cosine_recall@5 0.52
cosine_recall@10 0.75
cosine_ndcg@10 0.3853
cosine_mrr@10 0.2732
cosine_map@100 0.2814

Information Retrieval

Metric Value
cosine_accuracy@1 0.09
cosine_accuracy@3 0.37
cosine_accuracy@5 0.51
cosine_accuracy@10 0.74
cosine_precision@1 0.09
cosine_precision@3 0.1233
cosine_precision@5 0.102
cosine_precision@10 0.074
cosine_recall@1 0.09
cosine_recall@3 0.37
cosine_recall@5 0.51
cosine_recall@10 0.74
cosine_ndcg@10 0.3758
cosine_mrr@10 0.2635
cosine_map@100 0.2725

Information Retrieval

Metric Value
cosine_accuracy@1 0.1
cosine_accuracy@3 0.35
cosine_accuracy@5 0.47
cosine_accuracy@10 0.72
cosine_precision@1 0.1
cosine_precision@3 0.1167
cosine_precision@5 0.094
cosine_precision@10 0.072
cosine_recall@1 0.1
cosine_recall@3 0.35
cosine_recall@5 0.47
cosine_recall@10 0.72
cosine_ndcg@10 0.37
cosine_mrr@10 0.2625
cosine_map@100 0.2733

Information Retrieval

Metric Value
cosine_accuracy@1 0.07
cosine_accuracy@3 0.33
cosine_accuracy@5 0.48
cosine_accuracy@10 0.71
cosine_precision@1 0.07
cosine_precision@3 0.11
cosine_precision@5 0.096
cosine_precision@10 0.071
cosine_recall@1 0.07
cosine_recall@3 0.33
cosine_recall@5 0.48
cosine_recall@10 0.71
cosine_ndcg@10 0.3526
cosine_mrr@10 0.2425
cosine_map@100 0.2532

Information Retrieval

Metric Value
cosine_accuracy@1 0.06
cosine_accuracy@3 0.32
cosine_accuracy@5 0.46
cosine_accuracy@10 0.68
cosine_precision@1 0.06
cosine_precision@3 0.1067
cosine_precision@5 0.092
cosine_precision@10 0.068
cosine_recall@1 0.06
cosine_recall@3 0.32
cosine_recall@5 0.46
cosine_recall@10 0.68
cosine_ndcg@10 0.3393
cosine_mrr@10 0.2341
cosine_map@100 0.2451

Training Details

Training Dataset

Unnamed Dataset

  • Size: 900 training samples
  • Columns: positive and anchor
  • Approximate statistics based on the first 1000 samples:
    positive anchor
    type string string
    details
    • min: 159 tokens
    • mean: 444.92 tokens
    • max: 512 tokens
    • min: 7 tokens
    • mean: 21.97 tokens
    • max: 82 tokens
  • Samples:
    positive anchor
    Consent Third Party & Cookie Consent Learn more Data Security Posture Management Secure sensitive data in hybrid multicloud and SaaS environments Learn more Data Breach Impact Analysis & Response Analyze impact of a data breach and coordinate response per global regulatory obligations Learn more Data Catalog Automatically catalog datasets and enable users to find, understand, trust and access data Learn more Data Lineage Track changes and transformations of data throughout its lifecycle Data Controls Orchestrator View Data Command Center View Sensitive Data Intelligence View Asset Discovery Data Discovery & Classification Sensitive Data Catalog People Data Graph Learn more Privacy Automate compliance with global privacy regulations Data Mapping Automation View Data Subject Request Automation View People Data Graph View Assessment Automation View Cookie Consent View Universal Consent View Vendor Risk Assessment View Breach Management View Privacy Policy Management View Privacy Center View Learn more Security Identify data risk and enable protection & control Data Security Posture Management View Data Access Intelligence & Governance View Data Risk Management View Data Breach Analysis View Learn more Governance Optimize Data Governance with granular insights into your data Data Catalog View Data Lineage View Data Quality View Data Controls Orchestrator View Solutions Technologies Covering you everywhere with 1000+ integrations across data systems. Snowflake View AWS View Microsoft 365 View Salesforce View Workday View GCP View Azure View Oracle View Learn more Regulations Automate compliance with global privacy regulations. US California CCPA View US California CPRA View European Union GDPR View Thailand’s PDPA View China PIPL View Canada, Consent
    PA View China PIPL View Canada PIPEDA View Brazil's LGPD View + More View Privacy View Security View Governance View Marketing View Resources Blog View Collateral View Knowledge Center View Securiti Education View Company About Us View Partner Program View Contact Us View News Coverage View Press Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud Data Mapping
    MoTC is responsible for the enforcement of the DPL. . 4 The MoTC can also impose fines of up to QAR 5 million (US$1.4 million) for violations of certain provisions of the DPL. 5 There is currently no obligation for organizations in Qatar to appoint a data protection officer under the DPL. ### Forrester Names Securiti a Leader in the Privacy Management Wave Q4, 2021 Read the Report ### Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software Read the Report At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap #### Newsletter #### Company About Us Careers Contact Us Partner Program News Coverage Press Releases #### Resources Blog Collateral Knowledge Center Securiti Education Privacy Center Free Do Not Sell Tool What is DSPM #### Terms Terms & Policies Security & Compliance Manage cookie preferences My Privacy Center #### Get in touch email protected 300 Santana Row Suite 450. San Jose, CA 95128 Contact Us Schedule a Demo Products By Role Data Command Center Sensitive Data Intelligence Privacy Security Governance Data Controls Orchestrator By Use Cases Back Asset Discovery Asset Discovery Data Discovery & Classification Data Discovery & Classification Sensitive Data Catalog Sensitive Data Catalog People Data Graph People Data Graph Data Mapping Automation View Data Subject Request Automation View People Data Graph View Assessment Automation View Cookie Consent View Universal Consent View Vendor Risk Assessment View Breach Management View Privacy Policy Management View Privacy Center View Data Security Posture Management View Data Access Intelligence & Governance View Data Risk Management , . 5 Infringement of the provisions of the DPA may be penalized by not more than KES 5 million or 1% of the previous fiscal year’s annual turnover. ### Forrester Names Securiti a Leader in the Privacy Management Wave Q4, 2021 Read the Report ### Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software Read the Report At Securiti, our mission is to enable enterprises to safely harness the incredible power of data and the cloud by controlling the complex security, privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap #### Newsletter #### Company About Us Careers Contact Us Partner Program News Coverage Press Releases #### Resources Blog Collateral Knowledge Center Securiti Education Privacy Center Free Do Not Sell Tool What is DSPM #### Terms Terms & Policies Security & Compliance Manage cookie preferences My Privacy Center #### Get in touch email protected 300 Santana Row Suite 450. San Jose, CA 95128 Contact Us Schedule a Demo Products By Role Data Command Center Sensitive Data Intelligence Privacy Security Governance Data Controls Orchestrator By Use Cases Back Asset Discovery Asset Discovery Data Discovery & Classification Data Discovery & Classification Sensitive Data Catalog Sensitive Data Catalog People Data Graph People Data Graph Data Mapping Automation View Data Subject Request Automation View People Data Graph View Assessment Automation View Cookie Consent View Universal Consent View Vendor Risk Assessment View Breach Management View Privacy Policy Management View Privacy Center View Data Security Posture Management View Data Access Intelligence & Governance View Data Risk Management View Data Breach Analysis View Data Catalog View Data Lineage View Data Quality View What does Securiti aim to achieve in terms of data security, privacy, and compliance risks?
  • Loss: MatryoshkaLoss with these parameters:
    {
    "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
  • learning_rate: 2e-05
  • num_train_epochs: 10
  • 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

Click to expand
  • 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: 1
  • 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: 10
  • 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

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
0.3448 10 9.0172 - - - - -
0.6897 20 7.8791 - - - - -
1.0 29 - 0.2696 0.2535 0.2642 0.2317 0.2805
1.0345 30 6.1959 - - - - -
1.3793 40 5.1573 - - - - -
1.7241 50 3.9165 - - - - -
2.0 58 - 0.2545 0.2678 0.2693 0.2320 0.2609
2.0690 60 3.6232 - - - - -
2.4138 70 3.0077 - - - - -
2.7586 80 2.951 - - - - -
3.0 87 - 0.2663 0.2909 0.2663 0.2438 0.2677
3.1034 90 2.3699 - - - - -
3.4483 100 2.404 - - - - -
3.7931 110 1.818 - - - - -
4.0 116 - 0.2752 0.279 0.2888 0.2447 0.2938
4.1379 120 1.4625 - - - - -
4.4828 130 1.9295 - - - - -
4.8276 140 1.5043 - - - - -
5.0 145 - 0.2633 0.2684 0.2771 0.2442 0.2841
5.1724 150 1.0966 - - - - -
5.5172 160 1.3741 - - - - -
5.8621 170 1.132 - - - - -
6.0 174 - 0.2635 0.2649 0.2861 0.2399 0.2760
6.2069 180 0.8199 - - - - -
6.5517 190 1.0209 - - - - -
6.8966 200 1.0516 - - - - -
7.0 203 - 0.2619 0.2738 0.2654 0.2474 0.2770
7.2414 210 0.7749 - - - - -
7.5862 220 1.0583 - - - - -
7.9310 230 0.832 - - - - -
8.0 232 - 0.2652 0.2739 0.2675 0.2441 0.2725
8.2759 240 0.7005 - - - - -
8.6207 250 0.8967 - - - - -
8.9655 260 0.8263 - - - - -
9.0 261 - 0.2609 0.2682 0.2656 0.2401 0.2817
9.3103 270 0.6493 - - - - -
9.6552 280 0.7889 - - - - -
10.0 290 0.7407 0.2532 0.2733 0.2725 0.2451 0.2814
  • 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 

@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 

@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 

@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}
}