|
--- |
|
base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
|
language: |
|
- en |
|
library_name: sentence-transformers |
|
license: apache-2.0 |
|
metrics: |
|
- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
|
- cosine_accuracy@10 |
|
- cosine_precision@1 |
|
- cosine_precision@3 |
|
- cosine_precision@5 |
|
- cosine_precision@10 |
|
- cosine_recall@1 |
|
- cosine_recall@3 |
|
- cosine_recall@5 |
|
- cosine_recall@10 |
|
- cosine_ndcg@10 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:494 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'Program Join our Partner Program Contact Us Contact us to learn |
|
more or schedule a demo News Coverage Read about Securiti in the news Press Releases |
|
Find our latest press releases Careers Join the talented Securiti team Knowledge |
|
Center » Data Privacy Automation # New Zealand''s Privacy Act of 2020 By Securiti |
|
Research Team Published March 7, 2022 / Updated August 11, 2023 New Zealand was |
|
one of the first countries that enacted a law specifically dedicated to its residents'' |
|
right to privacy with its Privacy Act of 1993. Whilst the entire definition of |
|
what "privacy" means has undergone a radical shift since then New Zealand’s principles |
|
based legislation has remained relatively fit for purpose. Even with the advent |
|
of social media and the internet adding an entirely new paradigm to that topic. |
|
In recognition of the evolution of privacy, New Zealand updated its' |
|
sentences: |
|
- Where can I find Securiti's latest press releases? |
|
- What are the requirements for data transfer under Spain's data protection law, |
|
including certifications and information for data subjects? |
|
- What is the term for the right to delete personal data upon request, also known |
|
as 'the right to be forgotten', and what are the other data protection rights |
|
under GDPR? |
|
- source_sentence: 'that the third party: has appropriate policies and processes in |
|
place; has trained its staff to ensure information is appropriately safeguarded |
|
at all times; has adequate security measures in place. Simultaneously, the Cross-border |
|
Guidelines also specify that organizations must provide notice to customers that: |
|
their personal information may be sent to another jurisdiction for processing; |
|
while the information is in the other jurisdiction, it may be accessed by the |
|
courts, law enforcement, and national security authorities. ## 10\. Data Subject |
|
Rights PIPEDA bestows the following rights to data subjects: Right to access Right |
|
to accuracy and completeness Right to withdraw consent and submit complaints ## |
|
11\. Penalties for PIPEDA Non-Compliance PIPEDA imposes administrative penalties |
|
for non-compliance, where the amount may vary depending upon the severity and |
|
the kind of violation. According to PIPEDA, : organizations must keep personal |
|
information accurate. 7. **Safeguards** : organizations must protect personal |
|
information against loss or theft. 8. **Openness** : privacy policy and practices |
|
must be understandable and easily available. 9. **Individual access** : data subjects |
|
have a right to access the personal information an organization holds about them. |
|
10. **Resource** : organizations must develop accessible complaint procedures. |
|
## 3\. Obligations for the Data Controller and Data Processor PIPEDA does not |
|
differentiate between data controllers and data processors and provides a similar |
|
set of responsibilities for both controllers and processors. PIPEDA demands all |
|
organizations appoint individuals who will be accountable for ensuring streamlined |
|
compliance of an organization’s data activities in accordance with the provisions |
|
of PIPEDA. ## 4\. Consent Requirements In many circumstances, PIPEDA requires |
|
organizations to obtain the data subject’s consent to use, disclose, and retain |
|
any personal information.' |
|
sentences: |
|
- What are the key provisions of South Korea's data privacy law? |
|
- What are the circumstances in which the data subject must be notified about the |
|
collection of personal data? |
|
- How does PIPEDA ensure staff's compliance with guidelines and obligations regarding |
|
information protection? |
|
- source_sentence: 'The criteria used The purpose of processing This information must |
|
be provided within 15 days from the date of the data subject’s request. vs GDPR |
|
states that, when responding to an access request, a data controller must indicate |
|
the following: The categories of personal data concerned The recipients or categories |
|
of recipients to whom personal data have been disclosed to The retention period |
|
The right to lodge a complaint with the supervisory authority The existence of |
|
data transfers The existence of automated decision making The information must |
|
be provided without undue delay and in any event within one month of the receipt |
|
of the request. LGPD grants the right to data portability through an express request |
|
and subject to commercial and industrial secrecy, pursuant to the regulation of |
|
the controlling agency. This right, however, does not include data that has already |
|
been anonymised by the controller. vs GDPR defines the right to' |
|
sentences: |
|
- What is considered an offense related to obstructing the OPC in an investigation? |
|
- What does LGPD grant the right to in terms of data portability? |
|
- How does automation aid in complying with data privacy regulations like the PDPO? |
|
- source_sentence: 'uriti Research Team Published December 3, 2020 / Updated October |
|
3, 2023 On 1 December 2020, New Zealand’s new Privacy Act 2020 came into effect. |
|
Our experts at Securiti have compiled the following list of compliance actions |
|
to remind organizations of their obligations under New Zealand’s new Privacy Act. |
|
## 1\. Notify privacy breaches within 72 hours Organizations must notify privacy |
|
breach that has caused serious harm to the affected individual or is likely to |
|
do so, to the Privacy Commissioner and the affected individuals as soon as practicable |
|
or within 72 hours after becoming aware of the breach. Where it is not reasonably |
|
practicable to notify the affected individual or each member of a group of affected |
|
individuals, organizations must notify the public in a manner that no individual |
|
is identified. Companies that fail to notify privacy breaches without any reasonable |
|
excuse would be liable on conviction to a fine not exceeding $10,000. ## 2\. Notify |
|
privacy breaches caused by any' |
|
sentences: |
|
- When are controllers and data processors required to appoint a DPO according to |
|
the PDP Law and state regulations in Indonesia? |
|
- What is the time frame for notifying privacy breaches under New Zealand's new |
|
Privacy Act? |
|
- What rights do Colorado residents have over their personal data under the Colorado |
|
Privacy Act? |
|
- source_sentence: Careers View Events Spotlight Talks IDC Names Securiti a Worldwide |
|
Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule |
|
a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset |
|
and Data Discovery Discover dark and native data assets Learn more Data Access |
|
Intelligence & Governance Identify which users have access to sensitive data and |
|
prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud |
|
| Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | |
|
Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover |
|
& Classify Structured and Unstructured Data | People Data Graph Learn more Data |
|
Flow Intelligence & Governance Prevent sensitive data sprawl through real-, Press |
|
Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide |
|
Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule |
|
a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset |
|
and Data Discovery Discover dark and native data assets Learn more Data Access |
|
Intelligence & Governance Identify which users have access to sensitive data and |
|
prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud |
|
| Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | |
|
Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover |
|
& Classify Structured and Unstructured Data | People Data Graph Learn more Data |
|
Flow Intelligence & Governance Prevent |
|
sentences: |
|
- What is the purpose of the Data Command Center? |
|
- What are IBM's future prospects and preparedness for new business opportunities? |
|
- What is the US California CCPA? |
|
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: 0.34845360824742266 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5855670103092784 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6701030927835051 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.756701030927835 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.34845360824742266 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1951890034364261 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13402061855670103 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.0756701030927835 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.34845360824742266 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5855670103092784 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6701030927835051 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.756701030927835 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5507373799577976 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4849337260677468 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4942402452655515 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3463917525773196 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5938144329896907 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.668041237113402 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.756701030927835 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3463917525773196 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1979381443298969 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13360824742268038 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07567010309278348 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3463917525773196 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5938144329896907 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.668041237113402 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.756701030927835 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5517739147624575 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.48604565537555244 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4956303541940711 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.3422680412371134 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5670103092783505 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6618556701030928 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7484536082474227 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.3422680412371134 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1890034364261168 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.13237113402061854 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07484536082474226 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.3422680412371134 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5670103092783505 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6618556701030928 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7484536082474227 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5412682955861301 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.475321551300933 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.48455040697749474 |
|
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-v20") |
|
# Run inference |
|
sentences = [ |
|
'Careers View Events Spotlight Talks IDC Names Securiti a Worldwide Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover & Classify Structured and Unstructured Data | People Data Graph Learn more Data Flow Intelligence & Governance Prevent sensitive data sprawl through real-, Press Releases View Careers View Events Spotlight Talks IDC Names Securiti a Worldwide Leader in Data Privacy View Events Spotlight Talks Education Contact Us Schedule a Demo Products By Use Cases By Roles Data Command Center View Learn more Asset and Data Discovery Discover dark and native data assets Learn more Data Access Intelligence & Governance Identify which users have access to sensitive data and prevent unauthorized access Learn more Data Privacy Automation PrivacyCenter.Cloud | Data Mapping | DSR Automation | Assessment Automation | Vendor Assessment | Breach Management | Privacy Notice Learn more Sensitive Data Intelligence Discover & Classify Structured and Unstructured Data | People Data Graph Learn more Data Flow Intelligence & Governance Prevent', |
|
'What is the purpose of the Data Command Center?', |
|
"What are IBM's future prospects and preparedness for new business opportunities?", |
|
] |
|
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 | 0.3485 | |
|
| cosine_accuracy@3 | 0.5856 | |
|
| cosine_accuracy@5 | 0.6701 | |
|
| cosine_accuracy@10 | 0.7567 | |
|
| cosine_precision@1 | 0.3485 | |
|
| cosine_precision@3 | 0.1952 | |
|
| cosine_precision@5 | 0.134 | |
|
| cosine_precision@10 | 0.0757 | |
|
| cosine_recall@1 | 0.3485 | |
|
| cosine_recall@3 | 0.5856 | |
|
| cosine_recall@5 | 0.6701 | |
|
| cosine_recall@10 | 0.7567 | |
|
| cosine_ndcg@10 | 0.5507 | |
|
| cosine_mrr@10 | 0.4849 | |
|
| **cosine_map@100** | **0.4942** | |
|
|
|
#### 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 | 0.3464 | |
|
| cosine_accuracy@3 | 0.5938 | |
|
| cosine_accuracy@5 | 0.668 | |
|
| cosine_accuracy@10 | 0.7567 | |
|
| cosine_precision@1 | 0.3464 | |
|
| cosine_precision@3 | 0.1979 | |
|
| cosine_precision@5 | 0.1336 | |
|
| cosine_precision@10 | 0.0757 | |
|
| cosine_recall@1 | 0.3464 | |
|
| cosine_recall@3 | 0.5938 | |
|
| cosine_recall@5 | 0.668 | |
|
| cosine_recall@10 | 0.7567 | |
|
| cosine_ndcg@10 | 0.5518 | |
|
| cosine_mrr@10 | 0.486 | |
|
| **cosine_map@100** | **0.4956** | |
|
|
|
#### 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 | 0.3423 | |
|
| cosine_accuracy@3 | 0.567 | |
|
| cosine_accuracy@5 | 0.6619 | |
|
| cosine_accuracy@10 | 0.7485 | |
|
| cosine_precision@1 | 0.3423 | |
|
| cosine_precision@3 | 0.189 | |
|
| cosine_precision@5 | 0.1324 | |
|
| cosine_precision@10 | 0.0748 | |
|
| cosine_recall@1 | 0.3423 | |
|
| cosine_recall@3 | 0.567 | |
|
| cosine_recall@5 | 0.6619 | |
|
| cosine_recall@10 | 0.7485 | |
|
| cosine_ndcg@10 | 0.5413 | |
|
| cosine_mrr@10 | 0.4753 | |
|
| **cosine_map@100** | **0.4846** | |
|
|
|
<!-- |
|
## 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: 494 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: 18 tokens</li><li>mean: 223.56 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.87 tokens</li><li>max: 102 tokens</li></ul> | |
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* Samples: |
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| positive | anchor | |
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|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>### Denmark #### Denmark **Effective Date** : May 25, 2018 **Region** : EMEA (Europe, Middle East, Africa) Similar to other EU countries, Denmark has enacted a data protection act for the purpose of implementing the GDPR in the country. The Danish Data Protection Act (Act No. 502 of 23 May 2018) was enacted for the protection of natural persons with respect to personal data processing and to regulate the free movement of personal data. The Act replaced the previous Danish Act on Processing of Personal Data (Act no. 429 of 31/05/2000). Under the new Act, the Danish Data Protection Authority (Datatilsynet) oversees all aspects related to the supervision and enforcement of the Data Protection Act and the GDPR within the country as well as representing Denmark in the European Data Protection Board. ### Finland #### Finland **Effective Date** : January 1, 2019 **Region** : EMEA (Europe</code> | <code>What is the role of the Danish Data Protection Authority in Denmark's implementation of the GDPR?</code> | |
|
| <code>CPRA compliance involves adhering to the requirements outlined in the California Privacy Rights Act (CPRA) to protect consumer privacy and data rights. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share Copy 91 ### More Stories that May Interest You 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... View More September 11, 2023 ## Indonesia’s Protection of Personal Data Law: Explained In January 2020, Indonesia joined the burgeoning list of countries with their own data protection regulations. Provisions for data protection had existed within various... View More August 31, 2023 ##</code> | <code>Why is it important to comply with CPRA requirements and how does it protect data rights?</code> | |
|
| <code>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, 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</code> | <code>What is the role of Vendor Assessment in securing and protecting sensitive data in Data Access Intelligence & Governance?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
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```json |
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{ |
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"loss": "MultipleNegativesRankingLoss", |
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"matryoshka_dims": [ |
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768, |
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512, |
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256 |
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], |
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"matryoshka_weights": [ |
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1, |
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1, |
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1 |
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], |
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"n_dims_per_step": -1 |
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} |
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``` |
|
|
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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|
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- `eval_strategy`: epoch |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `learning_rate`: 2e-05 |
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- `num_train_epochs`: 4 |
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- `lr_scheduler_type`: cosine |
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- `warmup_ratio`: 0.1 |
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- `bf16`: True |
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- `tf32`: True |
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- `load_best_model_at_end`: True |
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- `optim`: adamw_torch_fused |
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- `batch_sampler`: no_duplicates |
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|
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
|
|
|
- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: epoch |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 32 |
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- `per_device_eval_batch_size`: 16 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 2e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 4 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: cosine |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.1 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: True |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: True |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: True |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch_fused |
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- `optim_args`: None |
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- `adafactor`: False |
|
- `group_by_length`: False |
|
- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `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 |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `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_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| 0.625 | 10 | 3.7981 | - | - | - | |
|
| 1.0 | 16 | - | 0.4653 | 0.4819 | 0.4810 | |
|
| 1.25 | 20 | 2.2066 | - | - | - | |
|
| 1.875 | 30 | 1.668 | - | - | - | |
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| 2.0 | 32 | - | 0.4837 | 0.4905 | 0.4933 | |
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| 2.5 | 40 | 0.9807 | - | - | - | |
|
| **3.0** | **48** | **-** | **0.4846** | **0.4954** | **0.4949** | |
|
| 3.125 | 50 | 1.0226 | - | - | - | |
|
| 3.75 | 60 | 1.0564 | - | - | - | |
|
| 4.0 | 64 | - | 0.4846 | 0.4956 | 0.4942 | |
|
|
|
* The bold row denotes the saved checkpoint. |
|
|
|
### Framework Versions |
|
- Python: 3.10.14 |
|
- Sentence Transformers: 3.0.1 |
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- Transformers: 4.41.2 |
|
- PyTorch: 2.1.2+cu121 |
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- Accelerate: 0.31.0 |
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- Datasets: 2.19.1 |
|
- Tokenizers: 0.19.1 |
|
|
|
## Citation |
|
|
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### 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} |
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} |
|
``` |
|
|
|
#### MultipleNegativesRankingLoss |
|
```bibtex |
|
@misc{henderson2017efficient, |
|
title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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eprint={1705.00652}, |
|
archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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