Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,963 @@
+---
+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](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-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]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+* 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   | 0.1        |
+| 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
+* 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   | 0.09       |
+| 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
+* 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   | 0.1        |
+| 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
+* 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   | 0.07       |
+| 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
+* 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   | 0.06       |
+| 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** |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 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: 444.92 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 21.97 tokens</li><li>max: 82 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       | anchor                                                                                                   |
+  |:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
+  | <code>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 | 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</code>                               | <code>What enables users to find and access datasets in the Data Catalog?</code>                         |
+  | <code>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 | 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 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</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     | <code>What is Brazil's LGPD?</code>                                                                      |
+  | <code>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</code> | <code>What does Securiti aim to achieve in terms of data security, privacy, and compliance risks?</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
+- `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
+<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`: 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
+
+</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 |
+|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
+| 0.3448  | 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
+```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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21447ab7410b9b388c63df984b4e230dfa0fd93ef46d5419bbb218e7d805bc5d
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
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+      "rstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}