|
--- |
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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: |
|
- 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 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
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- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
widget: |
|
- source_sentence: Vendor Risk Assessment View Breach Management View Privacy Policy |
|
Management View Privacy Center View Learn more Security Identify data risk and |
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enable protection & control Data Security Posture Management View Data Access |
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Intelligence & Governance View Data Risk Management View Data Breach Analysis |
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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 in the news Press Releases Find |
|
our latest press releases Careers Join the |
|
sentences: |
|
- 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 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 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 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 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 |
|
sentences: |
|
- 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 |
|
(DPIAs) under the LGPD? |
|
- source_sentence: 'which the data is processed. **Right to Access:** Data 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. |
|
**Right to Rectification** : Under the right to rectification, data subjects can |
|
request the correction of their data. **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. **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. |
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**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. ## Facts related to Ghana’s Data Protection Act 2012 1 |
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While 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. 2 |
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In 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. 3 The 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. 4 The |
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DPA requires data controllers to register with the Data Protection Commission |
|
(DPC). 5 The 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. ### 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 |
|
Sitem' |
|
sentences: |
|
- 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. ## Portability The 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. ## Opt out The consumer has the right to opt out |
|
of the processing of the personal data for purposes of targeted advertising, the |
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sale of personal data, or profiling in furtherance of decisions that produce legal |
|
or similarly significant effects concerning the consumer. **Time period to fulfill |
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DSR request: ** All data subject rights’ requests (DSR requests) must be fulfilled |
|
by the data controller within a 45 day period. **Extension in time period: ** |
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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. **Denial of |
|
DSR request: ** If a DSR request is to be denied, the data controller must inform |
|
the consumer of the reasons within a 45 days period. **Appeal against refusal: |
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** 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. **Limitation of DSR requests per year: |
|
** Requests for data portability may be made only twice in a year. **Charges: |
|
** DSR requests must be fulfilled free of charge once in a year. Any subsequent |
|
request within a 12 month period can be charged. **Authentication: ** 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. ## Who must |
|
comply? CPA 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 if they match any one or both of these conditions: If |
|
they control or process the personal data of 100,000 consumers or more during |
|
a calendar year; or If 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; To promote |
|
awareness within the public related to the risks, rules, safeguards, and rights |
|
concerning the processing of personal data; To 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; ## Penalties |
|
for Non compliance The 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. Germany''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. This 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. However, |
|
for this fine to be applied, either the data subject, the Federal Commissioner, |
|
or the regulatory authority must file an official complaint. ## How an Organization |
|
Can Operationalize the Law Data handlers processing data inside Germany can remain |
|
compliant with the country''s data protection law if they fulfill the following |
|
conditions: Have 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 Hire a competent Data Protection Officer that understands the GDPR |
|
and Federal Data Protection Act thoroughly and can lead compliance efforts within |
|
your organisation Ensure all the company''s employees and staff are acutely aware |
|
of their responsibilities under the law Conduct regular data protection impact |
|
assessments as well as data mapping exercises to ensure maximum efficiency in |
|
your compliance efforts Notify the relevant authorities of a data breach as soon |
|
as possible ## How can Securiti Help Data 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: |
|
- What are the benefits of automating compliance with global privacy regulations |
|
for data protection and control? |
|
- 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: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.08 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.29 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.48 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.65 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.08 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09666666666666668 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09599999999999997 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06499999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.08 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.29 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.48 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.65 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.3356834483699582 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23805952380952378 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.25373588653956675 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.33 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.52 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.68 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.11 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.10399999999999998 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06799999999999998 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.33 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.52 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.68 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.35403179411423247 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.2524960317460317 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.26470102220887337 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.09 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.27 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.45 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.65 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.09 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.09 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.09 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.06499999999999999 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.09 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.27 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.45 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.65 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.33203261209382817 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.23417063492063486 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.24858408269645846 |
|
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: 0.23 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.44 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.57 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.06 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.07666666666666666 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.08799999999999997 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.056999999999999995 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.06 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.23 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.44 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.57 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.28544770610641695 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.19726587301587298 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.21493811628701745 |
|
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-v10") |
|
# Run inference |
|
sentences = [ |
|
"PR and Federal Data Protection Act within Germany; To promote awareness within the public related to the risks, rules, safeguards, and rights concerning the processing of personal data; To 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; ## Penalties for Non compliance The 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. Germany'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. This 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. However, for this fine to be applied, either the data subject, the Federal Commissioner, or the regulatory authority must file an official complaint. ## How an Organization Can Operationalize the Law Data handlers processing data inside Germany can remain compliant with the country's data protection law if they fulfill the following conditions: Have 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 Hire a competent Data Protection Officer that understands the GDPR and Federal Data Protection Act thoroughly and can lead compliance efforts within your organisation Ensure all the company's employees and staff are acutely aware of their responsibilities under the law Conduct regular data protection impact assessments as well as data mapping exercises to ensure maximum efficiency in your compliance efforts Notify the relevant authorities of a data breach as soon as possible ## How can Securiti Help Data 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] |
|
``` |
|
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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|
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## Evaluation |
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|
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### Metrics |
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|
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#### 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 | 0.08 | |
|
| cosine_accuracy@3 | 0.29 | |
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| cosine_accuracy@5 | 0.48 | |
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| cosine_accuracy@10 | 0.65 | |
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| cosine_precision@1 | 0.08 | |
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| cosine_precision@3 | 0.0967 | |
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| cosine_precision@5 | 0.096 | |
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| cosine_precision@10 | 0.065 | |
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| cosine_recall@1 | 0.08 | |
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| cosine_recall@3 | 0.29 | |
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| cosine_recall@5 | 0.48 | |
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| cosine_recall@10 | 0.65 | |
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| cosine_ndcg@10 | 0.3357 | |
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| cosine_mrr@10 | 0.2381 | |
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| **cosine_map@100** | **0.2537** | |
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|
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#### 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 | 0.09 | |
|
| cosine_accuracy@3 | 0.33 | |
|
| cosine_accuracy@5 | 0.52 | |
|
| cosine_accuracy@10 | 0.68 | |
|
| cosine_precision@1 | 0.09 | |
|
| cosine_precision@3 | 0.11 | |
|
| cosine_precision@5 | 0.104 | |
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| cosine_precision@10 | 0.068 | |
|
| cosine_recall@1 | 0.09 | |
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| cosine_recall@3 | 0.33 | |
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| cosine_recall@5 | 0.52 | |
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| cosine_recall@10 | 0.68 | |
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| cosine_ndcg@10 | 0.354 | |
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| cosine_mrr@10 | 0.2525 | |
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| **cosine_map@100** | **0.2647** | |
|
|
|
#### 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 | 0.09 | |
|
| cosine_accuracy@3 | 0.27 | |
|
| cosine_accuracy@5 | 0.45 | |
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| cosine_accuracy@10 | 0.65 | |
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| cosine_precision@1 | 0.09 | |
|
| cosine_precision@3 | 0.09 | |
|
| cosine_precision@5 | 0.09 | |
|
| cosine_precision@10 | 0.065 | |
|
| cosine_recall@1 | 0.09 | |
|
| cosine_recall@3 | 0.27 | |
|
| cosine_recall@5 | 0.45 | |
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| cosine_recall@10 | 0.65 | |
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| cosine_ndcg@10 | 0.332 | |
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| cosine_mrr@10 | 0.2342 | |
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| **cosine_map@100** | **0.2486** | |
|
|
|
#### 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 | 0.23 | |
|
| cosine_accuracy@5 | 0.44 | |
|
| cosine_accuracy@10 | 0.57 | |
|
| cosine_precision@1 | 0.06 | |
|
| cosine_precision@3 | 0.0767 | |
|
| cosine_precision@5 | 0.088 | |
|
| cosine_precision@10 | 0.057 | |
|
| cosine_recall@1 | 0.06 | |
|
| cosine_recall@3 | 0.23 | |
|
| cosine_recall@5 | 0.44 | |
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| cosine_recall@10 | 0.57 | |
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| cosine_ndcg@10 | 0.2854 | |
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| cosine_mrr@10 | 0.1973 | |
|
| **cosine_map@100** | **0.2149** | |
|
|
|
<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
|
|
|
### Training Dataset |
|
|
|
#### Unnamed Dataset |
|
|
|
|
|
* Size: 900 training samples |
|
* Columns: <code>positive</code> and <code>anchor</code> |
|
* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:--------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
|
| type | string | string | |
|
| details | <ul><li>min: 159 tokens</li><li>mean: 445.26 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 The Andorra personal data protection act came into force on May 17, 2022, by the Andorra Data Protection Authority (ADPA). Learn more about Andorra PDPA ### United Kingdom The 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 ### Botswana The 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 ### Zambia On 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 ### Jamaica On 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 ### Belarus The 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 ### Russian Federation The primary Russian law on data protection, Federal Law No. 152-FZ has been in effect since July 2006. Learn more ### Eswatini On March 4, 2022, the Eswatini Communications Commission published the Data Protection Act No. 5 of 2022, simultaneously announcing its immediate enforcement. Learn more ### Oman The Royal Decree 6/2022 promulgating the Personal Data Protection Law (PDPL) was passed on February 9, 2022. Learn more ### Sri Lanka Sri Lanka's parliament formally passed the Personal Data Protection Act (PDPA), No. 9 Of 2022, on March 19, 2022. Learn more ### Kuwait Kuwait's DPPR was formally introduced by the CITRA to ensure the Gulf country's data privacy infrastructure. Learn more ### Brunei Darussalam The draft Personal Data Protection Order is Brunei’s primary data protection law which came into effect in 2022. Learn more ### India India’</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. ### 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</code> | <code>What is the requirement for conducting a data protection impact assessment under the PDPA?</code> | |
|
| <code>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 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</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 | 7.4297 | - | - | - | - | |
|
| 0.6897 | 20 | 5.5127 | - | - | - | - | |
|
| 1.0 | 29 | - | 0.2399 | 0.2435 | 0.2579 | 0.1837 | |
|
| 1.0345 | 30 | 4.8788 | - | - | - | - | |
|
| 1.3793 | 40 | 4.0614 | - | - | - | - | |
|
| 1.7241 | 50 | 3.3471 | - | - | - | - | |
|
| 2.0 | 58 | - | 0.2373 | 0.2510 | 0.2545 | 0.1964 | |
|
| 2.0690 | 60 | 3.104 | - | - | - | - | |
|
| 2.4138 | 70 | 2.695 | - | - | - | - | |
|
| 2.7586 | 80 | 2.2038 | - | - | - | - | |
|
| 3.0 | 87 | - | 0.2416 | 0.2630 | 0.2587 | 0.2121 | |
|
| 3.1034 | 90 | 2.2576 | - | - | - | - | |
|
| 3.4483 | 100 | 2.1552 | - | - | - | - | |
|
| 3.7931 | 110 | 1.8199 | - | - | - | - | |
|
| 4.0 | 116 | - | 0.2429 | 0.2613 | 0.2546 | 0.2098 | |
|
| 4.1379 | 120 | 1.9192 | - | - | - | - | |
|
| 4.4828 | 130 | 1.7221 | - | - | - | - | |
|
| 4.8276 | 140 | 1.6878 | - | - | - | - | |
|
| **5.0** | **145** | **-** | **0.2486** | **0.2647** | **0.2537** | **0.2149** | |
|
|
|
* 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.* |
|
--> |
|
|
|
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## Model Card Authors |
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*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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## Model Card Contact |
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* |
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