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---
language:
- en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:872
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
datasets: []
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
widget:
- source_sentence: 'amendements to PIPA came into force on 05 Auguest 2020. 2 Some
parts of PIPA also apply to online service providers. 3 The latest amendment to
PIPA has introduced the concept of ‘pseudonymised data’ for the feasibility of
data economy. 4 Under the PIPA, all data handlers must appoint a chief privacy
officer. 5 Cookies, IP information, etc. are also regulated by the PIPA as personal
information. 6 Breach of a corrective order issued by the PIPC can lead to an
administrative fine of not more than KRW 30 million. ### 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'
sentences:
- What recognition did Securiti receive in the field of data privacy?
- How does the Office of the Privacy Commissioner educate agencies and organisations
in breach of the law?
- What is the concept of 'pseudonymised data' introduced by the latest amendment
to PIPA?
- source_sentence: '18th, 2020, and it has been in effect since then. ## Influence
of GDPR It is well known that the LGPD was drafted and based on the GDPR, so much
so that some people call it Brazil’s GDPR. The LGPD contains 65 articles that
provide individuals with data subject rights, impose obligations upon organizations
for lawful processing of personal data, require notification of data breaches
to the supervisory authority and affected data subjects, create a national supervisory
authority to interpret and enforce the law, regulate international transfer of
data, define lawful consent collection guidelines and impose heavy penalties on
violators similar to the GDPR. ## Essence of the LGPD Law LGPD provides: 9 data
subject rights requests exercisable by individual data subjects; 10 legal bases
for lawful processing; Obligatory and transparent disclosure requirements for
organizations to contain within their privacy policy; Consent collection and management
requirements for organizations;'
sentences:
- What are the penalties for misusing personal data and obstructing investigations
under the PDPA and its amendments?
- Which data privacy regulation, similar to the GDPR, had a significant impact in
the US after the promulgation of the GDPR in the EU?
- What are the requirements for consent collection and management under the LGPD
law?
- source_sentence: 'to the Privacy Act of 2020. ## Obligations for Organisations Under
the Privacy Act 2020 Under the Privacy Act’s jurisdiction, all organizations have
specific responsibilities or obligations towards their users. The most important
of these obligations include the following: ### 1\. Lawful Purpose Requirements
While data processing has become immensely important for nearly all businesses,
the Privacy Act ensures that such data processing can only occur if the organization
collecting the data has a lawful purpose for the collection and that collection
of the information is necessary for that purpose. It is also expected that the
information will be collected directly from the individual concerned. When collecting
personal information, organizations are required to ensure the individual is aware
of: The fact that the information is being collected; The purpose for which it
is being collected; The intended recipients of the information; The details of
the organization that will be collecting and holding the information; Any laws
that authorize or'
sentences:
- What are the obligations of organizations towards users under the Privacy Act
of 2020, including lawful purpose and consent requirements?
- What is the role of the Spanish Data Protection Agency in enforcing data protection
legislation in Spain and how does it ensure its effectiveness in enforcing the
law across the country?
- What is the purpose of Kuwait's Data Privacy Protection Regulation (DPPR)?
- source_sentence: '## Right of Access to Personal Data: What To Know The wealth of
data available to organizations globally has brought tremendous improvements in
their ability to target and cater to their customers'' needs. Organizations...
View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection
law until the Communication and Information Technology Regulatory Authority (CITRA)
introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product
Tour See how easy it is to manage privacy compliance with robotic automation.
Watch a demo At Securiti, our mission is to enable enterprises to safely harness
the incredible power of data and the cloud by controlling the complex security,
privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap
#### Newsletter #### Company About Us , Personal Data: What To Know The wealth
of data available to organizations globally has brought tremendous improvements
in their ability to target and cater to their customers'' needs. Organizations...
View More September 13, 2023 ## Kuwait''s DPPR Kuwait didn’t have any data protection
law until the Communication and Information Technology Regulatory Authority (CITRA)
introduced the Data Privacy Protection Regulation (DPPR). The... ## Take a Product
Tour See how easy it is to manage privacy compliance with robotic automation.
Watch a demo At Securiti, our mission is to enable enterprises to safely harness
the incredible power of data and the cloud by controlling the complex security,
privacy and compliance risks. Copyright (C) 2023 Securiti Sitemap XML Sitemap
#### Newsletter #### Company About Us Careers Contact Us'
sentences:
- What is the definition of personal data according to the PDPO?
- What are the requirements for organizations to notify the regulatory authority
in case of a data breach according to the PDPL and accompanying Regulations?
- Why did CITRA introduce Kuwait's DPPR?
- source_sentence: 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 broch
sentences:
- What resources are available for learning more about GCP?
- What are the penalties for unauthorized personal data transfer, including maximum
fines for data fiduciaries in various scenarios?
- What are the key provisions of South Korea's data privacy law?
pipeline_tag: sentence-similarity
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.36082474226804123
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5463917525773195
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5773195876288659
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6907216494845361
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36082474226804123
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18213058419243983
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11546391752577319
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0690721649484536
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.36082474226804123
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5463917525773195
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5773195876288659
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6907216494845361
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5180083093560761
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.46394207167403045
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.47681473846718614
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.36082474226804123
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5360824742268041
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5773195876288659
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7010309278350515
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.36082474226804123
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17869415807560135
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11546391752577319
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07010309278350516
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.36082474226804123
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5360824742268041
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5773195876288659
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.7010309278350515
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5187124999739344
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4620520373097693
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4737872459927759
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.32989690721649484
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4948453608247423
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5773195876288659
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6804123711340206
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32989690721649484
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1649484536082474
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11546391752577319
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06804123711340206
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32989690721649484
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4948453608247423
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5773195876288659
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6804123711340206
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4929368061598079
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.43412698412698414
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.44657071536051934
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.3402061855670103
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5051546391752577
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5670103092783505
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6907216494845361
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3402061855670103
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1683848797250859
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.1134020618556701
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0690721649484536
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.3402061855670103
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5051546391752577
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5670103092783505
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6907216494845361
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5032662355781912
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4449517263950254
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4553038204145196
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.32989690721649484
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4948453608247423
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5567010309278351
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6597938144329897
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32989690721649484
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1649484536082474
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.11134020618556702
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.06597938144329896
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.32989690721649484
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.4948453608247423
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.5567010309278351
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.6597938144329897
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.481245330711533
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.42577319587628865
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.43965778950983864
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-v12")
# Run inference
sentences = [
"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 broch",
'What resources are available for learning more about GCP?',
"What are the key provisions of South Korea's data privacy law?",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.3608 |
| cosine_accuracy@3 | 0.5464 |
| cosine_accuracy@5 | 0.5773 |
| cosine_accuracy@10 | 0.6907 |
| cosine_precision@1 | 0.3608 |
| cosine_precision@3 | 0.1821 |
| cosine_precision@5 | 0.1155 |
| cosine_precision@10 | 0.0691 |
| cosine_recall@1 | 0.3608 |
| cosine_recall@3 | 0.5464 |
| cosine_recall@5 | 0.5773 |
| cosine_recall@10 | 0.6907 |
| cosine_ndcg@10 | 0.518 |
| cosine_mrr@10 | 0.4639 |
| **cosine_map@100** | **0.4768** |
#### 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 | 0.3608 |
| cosine_accuracy@3 | 0.5361 |
| cosine_accuracy@5 | 0.5773 |
| cosine_accuracy@10 | 0.701 |
| cosine_precision@1 | 0.3608 |
| cosine_precision@3 | 0.1787 |
| cosine_precision@5 | 0.1155 |
| cosine_precision@10 | 0.0701 |
| cosine_recall@1 | 0.3608 |
| cosine_recall@3 | 0.5361 |
| cosine_recall@5 | 0.5773 |
| cosine_recall@10 | 0.701 |
| cosine_ndcg@10 | 0.5187 |
| cosine_mrr@10 | 0.4621 |
| **cosine_map@100** | **0.4738** |
#### 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 | 0.3299 |
| cosine_accuracy@3 | 0.4948 |
| cosine_accuracy@5 | 0.5773 |
| cosine_accuracy@10 | 0.6804 |
| cosine_precision@1 | 0.3299 |
| cosine_precision@3 | 0.1649 |
| cosine_precision@5 | 0.1155 |
| cosine_precision@10 | 0.068 |
| cosine_recall@1 | 0.3299 |
| cosine_recall@3 | 0.4948 |
| cosine_recall@5 | 0.5773 |
| cosine_recall@10 | 0.6804 |
| cosine_ndcg@10 | 0.4929 |
| cosine_mrr@10 | 0.4341 |
| **cosine_map@100** | **0.4466** |
#### 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 | 0.3402 |
| cosine_accuracy@3 | 0.5052 |
| cosine_accuracy@5 | 0.567 |
| cosine_accuracy@10 | 0.6907 |
| cosine_precision@1 | 0.3402 |
| cosine_precision@3 | 0.1684 |
| cosine_precision@5 | 0.1134 |
| cosine_precision@10 | 0.0691 |
| cosine_recall@1 | 0.3402 |
| cosine_recall@3 | 0.5052 |
| cosine_recall@5 | 0.567 |
| cosine_recall@10 | 0.6907 |
| cosine_ndcg@10 | 0.5033 |
| cosine_mrr@10 | 0.445 |
| **cosine_map@100** | **0.4553** |
#### 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 | 0.3299 |
| cosine_accuracy@3 | 0.4948 |
| cosine_accuracy@5 | 0.5567 |
| cosine_accuracy@10 | 0.6598 |
| cosine_precision@1 | 0.3299 |
| cosine_precision@3 | 0.1649 |
| cosine_precision@5 | 0.1113 |
| cosine_precision@10 | 0.066 |
| cosine_recall@1 | 0.3299 |
| cosine_recall@3 | 0.4948 |
| cosine_recall@5 | 0.5567 |
| cosine_recall@10 | 0.6598 |
| cosine_ndcg@10 | 0.4812 |
| cosine_mrr@10 | 0.4258 |
| **cosine_map@100** | **0.4397** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 872 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: 89 tokens</li><li>mean: 229.38 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.92 tokens</li><li>max: 102 tokens</li></ul> |
* Samples:
| positive | anchor |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|
| <code>controller should inform the data subject in every situation where his or her personal data is processed. The LPPD provides a general requirement to provide information on the collection methods but does not explicitly refer to automated decision-making or profiling. vs Articles: 5 14, Recitals: 58 63 This right requires the controller to provide the following information to the data subject when requested. This should be given in a concise, transparent, intelligible, and easily accessible form, using plain language: The identity and contact details of the controller, controller’s representative, and DPO, where applicable The purpose and the legal basis of the processing The categories of personal data concerned The recipients of the personal data The appropriate or suitable safeguards and the means to obtain a copy of them or where they have been made available The controller must provide information necessary to ensure fair and transparent processing whether or not the personal</code> | <code>What information must the controller provide regarding their identity and contact details?</code> |
| <code>and deletions, and manage all vendor contracts and compliance documents. ## Key Rights Under Ghana’s Data Protection Act 2012 **Right to be Informed** : Data subjects have the right to be informed of the processing of their personal data and the purposes for 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</code> | <code>What are the key rights provided to data subjects under Ghana's Data Protection Act 2012?</code> |
| <code>aim to protect personal data, they have differences in scope, requirements, and applicability. PDPA applies to Thailand, while GDPR applies to the European Union. The effect of PDPA in Thailand is to regulate how personal data is processed, collected, used, and protected by individuals and organizations in the country. Thailand's PDPA includes provisions related to personal data breach notifications, requiring data controllers to notify the Personal Data Protection Committee (PDPC) of a personal data breach as soon as possible, preferably within 72 hours of becoming aware of it. The principles of PDPA in Thailand include obtaining consent, especially for minors, ensuring data security, issuing timely data breach notifications, designating a data protection officer, conducting data protection impact assessments, maintaining a record of processing activities, and ensuring adequate standards when transferring data across borders. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share</code> | <code>What is the role of obtaining consent in Thailand's PDPA?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 10
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 10
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 |
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|
| 0.3571 | 10 | 6.8967 | - | - | - | - | - |
| 0.7143 | 20 | 6.1128 | - | - | - | - | - |
| 1.0 | 28 | - | 0.4344 | 0.4387 | 0.4857 | 0.3831 | 0.4515 |
| 1.0714 | 30 | 4.4294 | - | - | - | - | - |
| 1.4286 | 40 | 3.2369 | - | - | - | - | - |
| 1.7857 | 50 | 3.2624 | - | - | - | - | - |
| 2.0 | 56 | - | 0.4345 | 0.4456 | 0.4752 | 0.3885 | 0.4672 |
| 2.1429 | 60 | 2.1973 | - | - | - | - | - |
| 2.5 | 70 | 1.815 | - | - | - | - | - |
| 2.8571 | 80 | 1.8725 | - | - | - | - | - |
| **3.0** | **84** | **-** | **0.4636** | **0.4469** | **0.4781** | **0.4012** | **0.4765** |
| 3.2143 | 90 | 1.2027 | - | - | - | - | - |
| 3.5714 | 100 | 1.3053 | - | - | - | - | - |
| 3.9286 | 110 | 1.1 | - | - | - | - | - |
| 4.0 | 112 | - | 0.4417 | 0.4282 | 0.4721 | 0.4154 | 0.4671 |
| 4.2857 | 120 | 0.8088 | - | - | - | - | - |
| 4.6429 | 130 | 0.8744 | - | - | - | - | - |
| 5.0 | 140 | 0.8075 | 0.4435 | 0.4443 | 0.4725 | 0.4116 | 0.4720 |
| 5.3571 | 150 | 0.5131 | - | - | - | - | - |
| 5.7143 | 160 | 0.6387 | - | - | - | - | - |
| 6.0 | 168 | - | 0.4495 | 0.4375 | 0.4768 | 0.4363 | 0.4794 |
| 6.0714 | 170 | 0.5041 | - | - | - | - | - |
| 6.4286 | 180 | 0.4053 | - | - | - | - | - |
| 6.7857 | 190 | 0.5665 | - | - | - | - | - |
| 7.0 | 196 | - | 0.4549 | 0.4504 | 0.4721 | 0.4382 | 0.4792 |
| 7.1429 | 200 | 0.3854 | - | - | - | - | - |
| 7.5 | 210 | 0.3085 | - | - | - | - | - |
| 7.8571 | 220 | 0.461 | - | - | - | - | - |
| 8.0 | 224 | - | 0.4570 | 0.4465 | 0.4722 | 0.4399 | 0.4785 |
| 8.2143 | 230 | 0.2521 | - | - | - | - | - |
| 8.5714 | 240 | 0.3944 | - | - | - | - | - |
| 8.9286 | 250 | 0.3524 | - | - | - | - | - |
| 9.0 | 252 | - | 0.4533 | 0.4457 | 0.4736 | 0.4394 | 0.4764 |
| 9.2857 | 260 | 0.2825 | - | - | - | - | - |
| 9.6429 | 270 | 0.3919 | - | - | - | - | - |
| 10.0 | 280 | 0.4004 | 0.4553 | 0.4466 | 0.4738 | 0.4397 | 0.4768 |
* 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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