|
--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: sentence-transformers |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:900 |
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- loss:MatryoshkaLoss |
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- loss:MultipleNegativesRankingLoss |
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base_model: BAAI/bge-base-en-v1.5 |
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datasets: [] |
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metrics: |
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- cosine_accuracy@1 |
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- cosine_accuracy@3 |
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- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- cosine_precision@1 |
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- cosine_precision@3 |
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- cosine_precision@5 |
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- cosine_precision@10 |
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- cosine_recall@1 |
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- cosine_recall@3 |
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- cosine_recall@5 |
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- cosine_recall@10 |
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- cosine_ndcg@10 |
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- cosine_mrr@10 |
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- cosine_map@100 |
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widget: |
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- source_sentence: '["Vendor Risk Assessment\n\nView\n\nBreach Management\n\nView\n\nPrivacy |
|
Policy Management\n\nView\n\nPrivacy Center\n\nView\n\nLearn more\n\nSecurity\n\nIdentify |
|
data risk and enable protection & control\n\nData Security Posture Management\n\nView\n\nData |
|
Access Intelligence & Governance\n\nView\n\nData Risk Management\n\nView\n\nData |
|
Breach Analysis\n\nView\n\nLearn more\n\nGovernance\n\nOptimize Data Governance |
|
with granular insights into your data\n\nData Catalog\n\nView\n\nData Lineage\n\nView\n\nData |
|
Quality\n\nView\n\nData Controls Orchestrator\n\nView\n\nSolutions\n\nTechnologies\n\nCovering |
|
you everywhere with 1000+ integrations across data systems.\n\nSnowflake\n\nView\n\nAWS\n\nView\n\nMicrosoft |
|
365\n\nView\n\nSalesforce\n\nView\n\nWorkday\n\nView\n\nGCP\n\nView\n\nAzure\n\nView\n\nOracle\n\nView\n\nLearn |
|
more\n\nRegulations\n\nAutomate compliance with global privacy regulations.\n\nUS |
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California CCPA\n\nView\n\nUS California CPRA\n\nView\n\nEuropean Union GDPR\n\nView\n\nThailand’s |
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PDPA\n\nView\n\nChina PIPL\n\nView\n\nCanada PIPEDA\n\nView\n\nBrazil''s LGPD\n\nView\n\n\\+ |
|
More\n\nView\n\nLearn more\n\nRoles\n\nIdentify data risk and enable protection |
|
& control.\n\nPrivacy\n\nView\n\nSecurity\n\nView\n\nGovernance\n\nView\n\nMarketing\n\nView\n\nResources\n\nBlog\n\nRead |
|
through our articles written by industry experts\n\nCollateral\n\nProduct brochures, |
|
white papers, infographics, analyst reports and more.\n\nKnowledge Center\n\nLearn |
|
about the data privacy, security and governance landscape.\n\nSecuriti Education\n\nCourses |
|
and Certifications for data privacy, security and governance professionals.\n\nCompany\n\nAbout |
|
Us\n\nLearn all about Securiti, our mission and history\n\nPartner Program\n\nJoin |
|
our Partner Program\n\nContact Us\n\nContact us to learn more or schedule a demo\n\nNews |
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Coverage\n\nRead about Securiti in the news\n\nPress Releases\n\nFind our latest |
|
press releases\n\nCareers\n\nJoin the"]' |
|
sentences: |
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- What is the purpose of tracking changes and transformations of data throughout |
|
its lifecycle? |
|
- What is the role of ePD in the European privacy regime and its relation to GDPR? |
|
- How can data governance be optimized using granular insights? |
|
- source_sentence: '[''Learn more\n\nAsset and Data Discovery\n\nDiscover dark and |
|
native data assets\n\nLearn more\n\nData Access Intelligence & Governance\n\nIdentify |
|
which users have access to sensitive data and prevent unauthorized access\n\nLearn |
|
more\n\nData Privacy Automation\n\nPrivacyCenter.Cloud | Data Mapping | DSR Automation |
|
| Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice\n\nLearn |
|
more\n\nSensitive Data Intelligence\n\nDiscover & Classify Structured and Unstructured |
|
Data | People Data Graph\n\nLearn more\n\nData Flow Intelligence & Governance\n\nPrevent |
|
sensitive data sprawl through real-time streaming platforms\n\nLearn more\n\nData |
|
Consent Automation\n\nFirst Party Consent | Third Party & Cookie Consent\n\nLearn |
|
more\n\nData Security Posture Management\n\nSecure sensitive data in hybrid multicloud |
|
and SaaS environments\n\nLearn more\n\nData Breach Impact Analysis & Response\n\nAnalyze |
|
impact of a data breach and coordinate response per global regulatory obligations\n\nLearn |
|
more\n\nData Catalog\n\nAutomatically catalog datasets and enable users to find, |
|
understand, trust and access data\n\nLearn more\n\nData Lineage\n\nTrack changes |
|
and transformations of data throughout its lifecycle\n\nData Controls Orchestrator\n\nView\n\nData |
|
Command Center\n\nView\n\nSensitive Data Intelligence\n\nView\n\nAsset Discovery\n\nData |
|
Discovery & Classification\n\nSensitive Data Catalog\n\nPeople Data Graph\n\nLearn |
|
more\n\nPrivacy\n\nAutomate compliance with global privacy regulations\n\nData |
|
Mapping Automation\n\nView\n\nData Subject Request Automation\n\nView\n\nPeople |
|
Data Graph\n\nView\n\nAssessment Automation\n\nView\n\nCookie Consent\n\nView\n\nUniversal |
|
Consent\n\nView\n\nVendor Risk Assessment\n\nView\n\nBreach Management\n\nView\n\nPrivacy |
|
Policy Management\n\nView\n\nPrivacy Center\n\nView\n\nLearn more\n\nSecurity\n\nIdentify |
|
data risk and enable protection & control\n\nData Security Posture Management\n\nView\n\nData |
|
Access Intelligence & Governance\n\nView\n\nData Risk Management\n\nView\n\nData |
|
Breach Analysis\n\nView\n\nLearn more\n\nGovernance\n\nOptimize Data Governance |
|
with granular insights into your data\n\nData Catalog\n\nView\n\nData Lineage\n\nView\n\nData |
|
Quality\n\nView\n\nData Controls Orchestrator\n\n'', ''\n\nView\n\nLearn more\n\nAsset |
|
and Data Discovery\n\nDiscover dark and native data assets\n\nLearn more\n\nData |
|
Access Intelligence & Governance\n\nIdentify which users have access to sensitive |
|
data and prevent unauthorized access\n\nLearn more\n\nData Privacy Automation\n\nPrivacyCenter.Cloud |
|
| Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | |
|
Breach Management | Privacy Notice\n\nLearn more\n\nSensitive Data Intelligence\n\nDiscover |
|
& Classify Structured and Unstructured Data | People Data Graph\n\nLearn more\n\nData |
|
Flow Intelligence & Governance\n\nPrevent sensitive data sprawl through real-time |
|
streaming platforms\n\nLearn more\n\nData Consent Automation\n\nFirst Party Consent |
|
| Third Party & Cookie Consent\n\nLearn more\n\nData Security Posture Management\n\nSecure |
|
sensitive data in hybrid multicloud and SaaS environments\n\nLearn more\n\nData |
|
Breach Impact Analysis & Response\n\nAnalyze impact of a data breach and coordinate |
|
response per global regulatory obligations\n\nLearn more\n\nData Catalog\n\nAutomatically |
|
catalog datasets and enable users to find, understand, trust and access data\n\nLearn |
|
more\n\nData Lineage\n\nTrack changes and transformations of data throughout its |
|
lifecycle\n\nData Controls Orchestrator\n\nView\n\nData Command Center\n\nView\n\nSensitive |
|
Data Intelligence\n\nView\n\nAsset Discovery\n\nData Discovery & Classification\n\nSensitive |
|
Data Catalog\n\nPeople Data Graph\n\nLearn more\n\nPrivacy\n\nAutomate compliance |
|
with global privacy regulations\n\nData Mapping Automation\n\nView\n\nData Subject |
|
Request Automation\n\nView\n\nPeople Data Graph\n\nView\n\nAssessment Automation\n\nView\n\nCookie |
|
Consent\n\nView\n\nUniversal Consent\n\nView\n\nVendor Risk Assessment\n\nView\n\nBreach |
|
Management\n\nView\n\nPrivacy Policy Management\n\nView\n\nPrivacy Center\n\nView\n\nLearn |
|
more\n\nSecurity\n\nIdentify data risk and enable protection & control\n\nData |
|
Security Posture Management\n\nView\n\nData Access Intelligence & Governance\n\nView\n\nData |
|
Risk Management\n\nView\n\nData Breach Analysis\n\nView\n\nLearn more\n\nGovernance\n\nOptimize |
|
Data Governance with granular insights into your data\n\nData Catalog\n\nView\n\nData |
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Lineage\n\nView\n\nData Quality\n\nView\n\nData Controls'']' |
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sentences: |
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- What is the purpose of Asset and Data Discovery in data governance and security? |
|
- Which EU member states have strict cyber laws? |
|
- What is the obligation for organizations to provide Data Protection Impact Assessments |
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(DPIAs) under the LGPD? |
|
- source_sentence: '['' which the data is processed.\n\n**Right to Access:** Data |
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subjects have the right to obtain confirmation whether or not the controller holds |
|
personal data about them, access their personal data, and obtain descriptions |
|
of data recipients.\n\n**Right to Rectification** : Under the right to rectification, |
|
data subjects can request the correction of their data.\n\n**Right to Erasure:** |
|
Data subjects have the right to request the erasure and destruction of the data |
|
that is no longer needed by the organization.\n\n**Right to Object:** The data |
|
subject has the right to prevent the data controller from processing personal |
|
data if such processing causes or is likely to cause unwarranted damage or distress |
|
to the data subject.\n\n**Right not to be Subjected to Automated Decision-Making** |
|
: The data subject has the right to not be subject to automated decision-making |
|
that significantly affects the individual.\n\n## Facts related to Ghana’s Data |
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Protection Act 2012\n\n1\n\nWhile processing personal data, organizations must |
|
comply with eight privacy principles: lawfulness of processing, data quality, |
|
security measures, accountability, purpose specification, purpose limitation, |
|
openness, and data subject participation.\n\n2\n\nIn the event of a security breach, |
|
the data controller shall take measures to prevent the breach and notify the Commission |
|
and the data subject about the breach as soon as reasonably practicable after |
|
the discovery of the breach.\n\n3\n\nThe DPA specifies lawful grounds for data |
|
processing, including data subject’s consent, the performance of a contract, the |
|
interest of data subject and public interest, lawful obligations, and the legitimate |
|
interest of the data controller.\n\n4\n\nThe DPA requires data controllers to |
|
register with the Data Protection Commission (DPC).\n\n5\n\nThe DPA provides varying |
|
fines and terms of imprisonment according to the severity and sensitivity of the |
|
violation, such as any person who sells personal data may get fined up to 2500 |
|
penalty units or up to five years imprisonment or both.\n\n### Forrester Names |
|
Securiti a Leader in the Privacy Management Wave Q4, 2021\n\nRead the Report\n\n### |
|
Securiti named a Leader in the IDC MarketScape for Data Privacy Compliance Software\n\nRead |
|
the Report\n\nAt 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.\n\nCopyright (C) 2023 Securiti\n\nSitem'']' |
|
sentences: |
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- What information is required for data subjects regarding data transfers under |
|
the GDPR, including personal data categories, data recipients, retention period, |
|
and automated decision making? |
|
- What privacy principles must organizations follow when processing personal data |
|
under Ghana's Data Protection Act 2012? |
|
- What is the purpose of Thailand's PDPA? |
|
- source_sentence: '[" consumer has the right to have his/her personal data stored |
|
or processed by the data controller be deleted.\n\n## Portability\n\nThe consumer |
|
has a right to obtain a copy of his/her personal data in a portable, technically |
|
feasible and readily usable format that allows the consumer to transmit the data |
|
to another controller without hindrance.\n\n## Opt\n\nout\n\nThe consumer has |
|
the right to opt out of the processing of the personal data for purposes of targeted |
|
advertising, the sale of personal data, or profiling in furtherance of decisions |
|
that produce legal or similarly significant effects concerning the consumer.\n\n**Time |
|
period to fulfill DSR request:\n\n** All data subject rights’ requests (DSR requests) |
|
must be fulfilled by the data controller within a 45 day period.\n\n**Extension |
|
in time period:\n\n** data controllers may seek for an extension of 45 days in |
|
fulfilling the request depending on the complexity and number of the consumer''s |
|
requests.\n\n**Denial of DSR request:\n\n** If a DSR request is to be denied, |
|
the data controller must inform the consumer of the reasons within a 45 days period.\n\n**Appeal |
|
against refusal:\n\n** Consumers have a right to appeal the decision for refusal |
|
of grant of the DSR request. The appeal must be decided within 45 days but the |
|
time period can be further extended by 60 additional days.\n\n**Limitation of |
|
DSR requests per year:\n\n** Requests for data portability may be made only twice |
|
in a year.\n\n**Charges:\n\n** DSR requests must be fulfilled free of charge once |
|
in a year. Any subsequent request within a 12 month period can be charged.\n\n**Authentication:\n\n** |
|
A data controller is not to respond to a consumer request unless it can authenticate |
|
the request using reasonably commercial means. A data controller can request additional |
|
information from the consumer for the purposes of authenticating the request.\n\n## |
|
Who must comply?\n\nCPA applies to all data controllers who conduct business in |
|
Colorado or produce or deliver commercial products or services that are intentionally |
|
targeted to residents of Colorado\n\nif they match any one or both of these conditions:\n\nIf |
|
they control or process the personal data of 100,000 consumers or more during |
|
a calendar year; or\n\nIf they derive revenue or receive a discount on the price |
|
of goods or services from the sale of personal data and process or control the |
|
personal data of 25,000"]' |
|
sentences: |
|
- What is the US California CCPA and how does it relate to data privacy regulations? |
|
- What does the People Data Graph serve in terms of privacy, security, and governance? |
|
- What rights does a consumer have regarding the portability of their personal data? |
|
- source_sentence: '["PR and Federal Data Protection Act within Germany;\n\nTo promote |
|
awareness within the public related to the risks, rules, safeguards, and rights |
|
concerning the processing of personal data;\n\nTo handle all complaints raised |
|
by data subjects related to data processing in addition to carrying out investigations |
|
to find out if any data handler has breached any provisions of the Act;\n\n## |
|
Penalties for Non\n\ncompliance\n\nThe GDPR already laid down some stringent penalties |
|
for companies that would be found in breach of the law''s provisions. More importantly, |
|
as opposed to other data protection laws such as the CCPA and CPRA, non-compliance |
|
with the law also meant penalties.\n\nGermany''s Federal Data Protection Act has |
|
a slightly more lenient take in this regard. Suppose a data handler is found to |
|
have fraudulently collected data, processed, shared, or sold data without proper |
|
consent from the data subjects, not responded or responded with delay to a data |
|
subject request, or failed to inform the data subject of a breach properly. In |
|
that case, it can be fined up to €50,000.\n\nThis is in addition to the GDPR''s |
|
€20 million or 4% of the total worldwide annual turnover of the preceding financial |
|
year, whichever is higher, that any organisation found in breach of the law is |
|
subject to.\n\nHowever, for this fine to be applied, either the data subject, |
|
the Federal Commissioner, or the regulatory authority must file an official complaint.\n\n## |
|
How an Organization Can Operationalize the Law\n\nData handlers processing data |
|
inside Germany can remain compliant with the country''s data protection law if |
|
they fulfill the following conditions:\n\nHave a comprehensive privacy policy |
|
that educates all users of their rights and how to contact the relevant personnel |
|
within the organisation in case of a query\n\nHire a competent Data Protection |
|
Officer that understands the GDPR and Federal Data Protection Act thoroughly and |
|
can lead compliance efforts within your organisation\n\nEnsure all the company''s |
|
employees and staff are acutely aware of their responsibilities under the law\n\nConduct |
|
regular data protection impact assessments as well as data mapping exercises to |
|
ensure maximum efficiency in your compliance efforts\n\nNotify the relevant authorities |
|
of a data breach as soon as possible\n\n## How can Securiti Help\n\nData privacy |
|
and compliance have become incredibly vital in earning users'' trust globally. |
|
Most users now expect most businesses to take all the relevant measures to ensure |
|
the data they collect is properly stored, protected, and maintained. Data protection |
|
laws have made such efforts legally mandatory"]' |
|
sentences: |
|
- How does Data Access Intelligence & Governance prevent unauthorized access to |
|
sensitive data? |
|
- What is required for an official complaint to be filed under Germany's Federal |
|
Data Protection Act? |
|
- Why is tracking data lineage important for data management and security? |
|
pipeline_tag: sentence-similarity |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
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- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.07 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.26 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.44 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.63 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.07 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.08666666666666668 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.088 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06299999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.07 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.26 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.44 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.63 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3150525932481703 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2180119047619047 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.23183767291183585 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.06 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.24 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.44 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07999999999999999 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.088 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.059999999999999984 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.24 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.44 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2944478644544164 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19998809523809516 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21493741340512212 |
|
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: 0.21 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.4 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.6 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.07 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.06999999999999999 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.059999999999999984 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.07 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.21 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.4 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.6 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.29018137407094874 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19626984126984123 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21169474427113727 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.07 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.17 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.32 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.53 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.07 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.056666666666666664 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.064 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.05299999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.07 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.17 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.32 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.53 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.2594266732084936 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.17759523809523803 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.194555422694347 |
|
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-v8") |
|
# Run inference |
|
sentences = [ |
|
'["PR and Federal Data Protection Act within Germany;\\n\\nTo promote awareness within the public related to the risks, rules, safeguards, and rights concerning the processing of personal data;\\n\\nTo handle all complaints raised by data subjects related to data processing in addition to carrying out investigations to find out if any data handler has breached any provisions of the Act;\\n\\n## Penalties for Non\\n\\ncompliance\\n\\nThe GDPR already laid down some stringent penalties for companies that would be found in breach of the law\'s provisions. More importantly, as opposed to other data protection laws such as the CCPA and CPRA, non-compliance with the law also meant penalties.\\n\\nGermany\'s Federal Data Protection Act has a slightly more lenient take in this regard. Suppose a data handler is found to have fraudulently collected data, processed, shared, or sold data without proper consent from the data subjects, not responded or responded with delay to a data subject request, or failed to inform the data subject of a breach properly. In that case, it can be fined up to €50,000.\\n\\nThis is in addition to the GDPR\'s €20 million or 4% of the total worldwide annual turnover of the preceding financial year, whichever is higher, that any organisation found in breach of the law is subject to.\\n\\nHowever, for this fine to be applied, either the data subject, the Federal Commissioner, or the regulatory authority must file an official complaint.\\n\\n## How an Organization Can Operationalize the Law\\n\\nData handlers processing data inside Germany can remain compliant with the country\'s data protection law if they fulfill the following conditions:\\n\\nHave a comprehensive privacy policy that educates all users of their rights and how to contact the relevant personnel within the organisation in case of a query\\n\\nHire a competent Data Protection Officer that understands the GDPR and Federal Data Protection Act thoroughly and can lead compliance efforts within your organisation\\n\\nEnsure all the company\'s employees and staff are acutely aware of their responsibilities under the law\\n\\nConduct regular data protection impact assessments as well as data mapping exercises to ensure maximum efficiency in your compliance efforts\\n\\nNotify the relevant authorities of a data breach as soon as possible\\n\\n## How can Securiti Help\\n\\nData privacy and compliance have become incredibly vital in earning users\' trust globally. Most users now expect most businesses to take all the relevant measures to ensure the data they collect is properly stored, protected, and maintained. Data protection laws have made such efforts legally mandatory"]', |
|
"What is required for an official complaint to be filed under Germany's Federal Data Protection Act?", |
|
'Why is tracking data lineage important for data management and security?', |
|
] |
|
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_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 | 0.07 | |
|
| cosine_accuracy@3 | 0.26 | |
|
| cosine_accuracy@5 | 0.44 | |
|
| cosine_accuracy@10 | 0.63 | |
|
| cosine_precision@1 | 0.07 | |
|
| cosine_precision@3 | 0.0867 | |
|
| cosine_precision@5 | 0.088 | |
|
| cosine_precision@10 | 0.063 | |
|
| cosine_recall@1 | 0.07 | |
|
| cosine_recall@3 | 0.26 | |
|
| cosine_recall@5 | 0.44 | |
|
| cosine_recall@10 | 0.63 | |
|
| cosine_ndcg@10 | 0.3151 | |
|
| cosine_mrr@10 | 0.218 | |
|
| **cosine_map@100** | **0.2318** | |
|
|
|
#### 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 | 0.06 | |
|
| cosine_accuracy@3 | 0.24 | |
|
| cosine_accuracy@5 | 0.44 | |
|
| cosine_accuracy@10 | 0.6 | |
|
| cosine_precision@1 | 0.06 | |
|
| cosine_precision@3 | 0.08 | |
|
| cosine_precision@5 | 0.088 | |
|
| cosine_precision@10 | 0.06 | |
|
| cosine_recall@1 | 0.06 | |
|
| cosine_recall@3 | 0.24 | |
|
| cosine_recall@5 | 0.44 | |
|
| cosine_recall@10 | 0.6 | |
|
| cosine_ndcg@10 | 0.2944 | |
|
| cosine_mrr@10 | 0.2 | |
|
| **cosine_map@100** | **0.2149** | |
|
|
|
#### 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 | 0.21 | |
|
| cosine_accuracy@5 | 0.4 | |
|
| cosine_accuracy@10 | 0.6 | |
|
| cosine_precision@1 | 0.07 | |
|
| cosine_precision@3 | 0.07 | |
|
| cosine_precision@5 | 0.08 | |
|
| cosine_precision@10 | 0.06 | |
|
| cosine_recall@1 | 0.07 | |
|
| cosine_recall@3 | 0.21 | |
|
| cosine_recall@5 | 0.4 | |
|
| cosine_recall@10 | 0.6 | |
|
| cosine_ndcg@10 | 0.2902 | |
|
| cosine_mrr@10 | 0.1963 | |
|
| **cosine_map@100** | **0.2117** | |
|
|
|
#### 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 | 0.07 | |
|
| cosine_accuracy@3 | 0.17 | |
|
| cosine_accuracy@5 | 0.32 | |
|
| cosine_accuracy@10 | 0.53 | |
|
| cosine_precision@1 | 0.07 | |
|
| cosine_precision@3 | 0.0567 | |
|
| cosine_precision@5 | 0.064 | |
|
| cosine_precision@10 | 0.053 | |
|
| cosine_recall@1 | 0.07 | |
|
| cosine_recall@3 | 0.17 | |
|
| cosine_recall@5 | 0.32 | |
|
| cosine_recall@10 | 0.53 | |
|
| cosine_ndcg@10 | 0.2594 | |
|
| cosine_mrr@10 | 0.1776 | |
|
| **cosine_map@100** | **0.1946** | |
|
|
|
<!-- |
|
## 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: 512 tokens</li><li>mean: 512.0 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 22.05 tokens</li><li>max: 82 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------| |
|
| <code>["orra\n\nThe Andorra personal data protection act came into force on May 17, 2022, by the Andorra Data Protection Authority (ADPA). Learn more about Andorra PDPA\n\n### United Kingdom\n\nThe UK Data Protection Act (DPA) 2018 is the amended version of the Data Protection Act that was passed in 1998. The DPA 2018 implements the GDPR with several additions and restrictions. Learn more about UK DPA\n\n### Botswana\n\nThe Botswana Data Protection came into effect on October 15, 2021 after the issuance of the Data Protection Act (Commencement Date) Order 2021 by the Minister of Presidential Affairs, Governance and Public Administration. Learn more about Botswana DPA\n\n### Zambia\n\nOn March 31, 2021, the Zambian parliament formally passed the Data Protection Act No. 3 of 2021 and the Electronic Communications and Transactions Act No. 4 of 2021. Learn more about Zambia DPA\n\n### Jamaica\n\nOn November 30, 2020, the First Schedule of the Data Protection Act No. 7 of 2020 came into effect following the publication of Supplement No. 160 of Volume CXLIV in the Jamaica Gazette Supplement. Learn more about Jamaica DPA\n\n### Belarus\n\nThe Law on Personal Data Protection of May 7, 2021, No. 99-Z, entered into effect within Belarus on November 15, 2021. Learn more about Belarus DPA\n\n### Russian Federation\n\nThe primary Russian law on data protection, Federal Law No. 152-FZ has been in effect since July 2006. Learn more\n\n### Eswatini\n\nOn March 4, 2022, the Eswatini Communications Commission published the Data Protection Act No. 5 of 2022, simultaneously announcing its immediate enforcement. Learn more\n\n### Oman\n\nThe Royal Decree 6/2022 promulgating the Personal Data Protection Law (PDPL) was passed on February 9, 2022. Learn more\n\n### Sri Lanka\n\nSri Lanka's parliament formally passed the Personal Data Protection Act (PDPA), No. 9 Of 2022, on March 19, 2022. Learn more\n\n### Kuwait\n\nKuwait's DPPR was formally introduced by the CITRA to ensure the Gulf country's data privacy infrastructure. Learn more\n\n### Brunei Darussalam\n\nThe draft Personal Data Protection Order is Brunei’s primary data protection law which came into effect in 2022. Learn more\n\n### India\n\nIndia’"]</code> | <code>What is the name of India's data protection law before May 17, 2022?</code> | |
|
| <code>[' the affected data subjects and regulatory authority about the breach and whether any of their information has been compromised as a result.\n\n### Data Protection Impact Assessment\n\nThere is no requirement for conducting data protection impact assessment under the PDPA.\n\n### Record of Processing Activities\n\nA 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.\n\n### Cross Border Data Transfer Requirements\n\nThe 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:\n\nWhere the consent of data subject is obtained for transfer; or\n\nWhere the transfer is necessary for the performance of contract between the parties;\n\nThe 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;\n\nThe 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;\n\nThe transfer is necessary in order to protect the vital interests of the data subject; or\n\nThe transfer is necessary as being in the public interest in circumstances as determined by the Minister.\n\n## Data Subject Rights\n\nThe 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:\n\n## Right to withdraw consent\n\nThe 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.\n\n## Right to access and rectification\n\nAs 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.\n\n## Right to data portability\n\nData subjects have the right to request that their data be stored in a manner where it']</code> | <code>What is the requirement for conducting a data protection impact assessment under the PDPA?</code> | |
|
| <code>[" more\n\nPrivacy\n\nAutomate compliance with global privacy regulations\n\nData Mapping Automation\n\nView\n\nData Subject Request Automation\n\nView\n\nPeople Data Graph\n\nView\n\nAssessment Automation\n\nView\n\nCookie Consent\n\nView\n\nUniversal Consent\n\nView\n\nVendor Risk Assessment\n\nView\n\nBreach Management\n\nView\n\nPrivacy Policy Management\n\nView\n\nPrivacy Center\n\nView\n\nLearn more\n\nSecurity\n\nIdentify data risk and enable protection & control\n\nData Security Posture Management\n\nView\n\nData Access Intelligence & Governance\n\nView\n\nData Risk Management\n\nView\n\nData Breach Analysis\n\nView\n\nLearn more\n\nGovernance\n\nOptimize Data Governance with granular insights into your data\n\nData Catalog\n\nView\n\nData Lineage\n\nView\n\nData Quality\n\nView\n\nData Controls Orchestrator\n\nView\n\nSolutions\n\nTechnologies\n\nCovering you everywhere with 1000+ integrations across data systems.\n\nSnowflake\n\nView\n\nAWS\n\nView\n\nMicrosoft 365\n\nView\n\nSalesforce\n\nView\n\nWorkday\n\nView\n\nGCP\n\nView\n\nAzure\n\nView\n\nOracle\n\nView\n\nLearn more\n\nRegulations\n\nAutomate compliance with global privacy regulations.\n\nUS California CCPA\n\nView\n\nUS California CPRA\n\nView\n\nEuropean Union GDPR\n\nView\n\nThailand’s PDPA\n\nView\n\nChina PIPL\n\nView\n\nCanada PIPEDA\n\nView\n\nBrazil's LGPD\n\nView\n\n\\+ More\n\nView\n\nLearn more\n\nRoles\n\nIdentify data risk and enable protection & control.\n\nPrivacy\n\nView\n\nSecurity\n\nView\n\nGovernance\n\nView\n\nMarketing\n\nView\n\nResources\n\nBlog\n\nRead through our articles written by industry experts\n\nCollateral\n\nProduct brochures, white papers, infographics, analyst reports and more.\n\nKnowledge Center\n\nLearn about the data privacy, security and governance landscape.\n\nSecuriti Education\n\nCourses and Certifications for data privacy, security and governance professionals.\n\nCompany\n\nAbout Us\n\nLearn all about"]</code> | <code>What is Data Subject Request Automation?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
|
512, |
|
256, |
|
128, |
|
64 |
|
], |
|
"matryoshka_weights": [ |
|
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`: 5 |
|
- `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`: 5 |
|
- `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 | |
|
|:-------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:| |
|
| 0.3448 | 10 | 7.9428 | - | - | - | - | |
|
| 0.6897 | 20 | 6.0138 | - | - | - | - | |
|
| 1.0 | 29 | - | 0.2011 | 0.2099 | 0.2307 | 0.1829 | |
|
| 1.0345 | 30 | 5.4431 | - | - | - | - | |
|
| 1.3793 | 40 | 4.4675 | - | - | - | - | |
|
| 1.7241 | 50 | 3.7435 | - | - | - | - | |
|
| 2.0 | 58 | - | 0.2092 | 0.2161 | 0.2341 | 0.1983 | |
|
| 2.0690 | 60 | 3.6676 | - | - | - | - | |
|
| 2.4138 | 70 | 3.0414 | - | - | - | - | |
|
| 2.7586 | 80 | 2.5451 | - | - | - | - | |
|
| 3.0 | 87 | - | 0.2091 | 0.2137 | 0.2426 | 0.1868 | |
|
| 3.1034 | 90 | 2.7694 | - | - | - | - | |
|
| 3.4483 | 100 | 2.3624 | - | - | - | - | |
|
| 3.7931 | 110 | 2.1016 | - | - | - | - | |
|
| **4.0** | **116** | **-** | **0.2139** | **0.2137** | **0.2271** | **0.1964** | |
|
| 4.1379 | 120 | 2.3842 | - | - | - | - | |
|
| 4.4828 | 130 | 1.9261 | - | - | - | - | |
|
| 4.8276 | 140 | 1.9737 | - | - | - | - | |
|
| 5.0 | 145 | - | 0.2117 | 0.2149 | 0.2318 | 0.1946 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
|
- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
|
- Accelerate: 0.31.0 |
|
- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
|
### BibTeX |
|
|
|
#### Sentence Transformers |
|
```bibtex |
|
@inproceedings{reimers-2019-sentence-bert, |
|
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
|
author = "Reimers, Nils and Gurevych, Iryna", |
|
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
|
month = "11", |
|
year = "2019", |
|
publisher = "Association for Computational Linguistics", |
|
url = "https://arxiv.org/abs/1908.10084", |
|
} |
|
``` |
|
|
|
#### MatryoshkaLoss |
|
```bibtex |
|
@misc{kusupati2024matryoshka, |
|
title={Matryoshka Representation Learning}, |
|
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi}, |
|
year={2024}, |
|
eprint={2205.13147}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.LG} |
|
} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
|
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
|
year={2017}, |
|
eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
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