sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:900 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
datasets: []
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:900
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 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?
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.*
-->