|
--- |
|
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:1872 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: The Secretary of Health and Human.pathname_key_services may issue |
|
an Emergency Use Authorization (EUA) to authorize unapproved medical products, |
|
or unapproved uses of approved medical products, to be manufactured, marketed, |
|
and sold in the context of an actual or potential emergency designated by the |
|
government. |
|
sentences: |
|
- What was the aggregate intrinsic value of exercised stock options as of December |
|
30, 2023? |
|
- What are some of the regulations related to data breach impact analysis and response? |
|
- What does the Emergency Use Authorization (EUA) by the U.S. Secretary of Health |
|
and Human Services allow? |
|
- source_sentence: 'the Virginia Consumer Data Protection Act protect consumers? The |
|
Virginia Consumer Data Protection Act protects consumers by prohibiting deceptive |
|
and unfair trade practices, giving consumers the right to sue for damages, and |
|
providing a mechanism for enforcement against businesses engaging in such practices. |
|
## Join Our Newsletter Get all the latest information, law updates and more delivered |
|
to your inbox ### Share Copy 54 ### More Stories that May Interest You View More |
|
September 21, 2023 ## Navigating Generative AI Privacy Challenges & Safeguarding |
|
Tips Introduction The emergence of Generative AI has ushered in a new era of innovation |
|
in the ever-evolving technological landscape that pushes the boundaries of... |
|
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' |
|
sentences: |
|
- What is Securiti's mission and history regarding Italy's GDPR implementation and |
|
compliance? |
|
- Which states have enacted data privacy laws like the VCDPA? |
|
- How does the Virginia Consumer Data Protection Act protect consumers and how is |
|
this protection enforced? |
|
- source_sentence: 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 , 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 |
|
sentences: |
|
- Why is it important to manage security of sensitive data in hybrid multicloud |
|
and SaaS environments, prevent data sprawl, and analyze the impact of data breaches? |
|
- What right does the consumer have regarding their personal data in terms of deletion? |
|
- What is the legal basis for the LGPD in Brazil? |
|
- source_sentence: 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 |
|
sentences: |
|
- What is ANPD's primary goal regarding LGPD and its rights and regulations? |
|
- What options are there for joining the Securiti team and expanding knowledge in |
|
data privacy, security, and governance? |
|
- How does the Data Controls Orchestrator help automate compliance with global privacy |
|
regulations? |
|
- source_sentence: 'remediate the incident, promptly notify relevant individuals, |
|
and report such data security incidents to the regulatory department(s). Thus, |
|
you should have a robust security breach response mechanism in place. ## 7\. Cross |
|
border data transfer and data localization requirements: Under DSL, Critical Information |
|
Infrastructure Operators are required to store the important data in the territory |
|
of China and cross-border transfer is regulated by the CSL. CIIOs need to conduct |
|
a security assessment in accordance with the measures jointly defined by CAC and |
|
the relevant departments under the State Council for the cross-border transfer |
|
of important data for business necessity. For non Critical Information Infrastructure |
|
operators, the important data cross-border transfer will be regulated by the measures |
|
announced by the Cyberspace Administration of China (CAC) and other authorities. |
|
However, those “measures” have still not yet been released. DSL also intends to |
|
establish a data national security review and export control system to restrict |
|
the cross-border transmission of data' |
|
sentences: |
|
- What are the requirements for storing important data in the territory of China |
|
under DSL? |
|
- How does behavioral targeting relate to the processing of personal data under |
|
Bahrain PDPL? |
|
- What is the margin of error generally estimated for worldwide Monthly Active People |
|
(MAP)? |
|
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: 0.28865979381443296 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5360824742268041 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6804123711340206 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7835051546391752 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.28865979381443296 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.17869415807560135 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1360824742268041 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07835051546391751 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.28865979381443296 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5360824742268041 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6804123711340206 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7835051546391752 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5259450080571785 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4444403534609721 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4516380787113637 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.29896907216494845 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5567010309278351 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7010309278350515 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7731958762886598 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.29896907216494845 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1855670103092783 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.14020618556701028 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07731958762886595 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.29896907216494845 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5567010309278351 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7010309278350515 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7731958762886598 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5284665496563921 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.4504540991654395 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4581455693989837 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.27835051546391754 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5360824742268041 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6701030927835051 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7628865979381443 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.27835051546391754 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.17869415807560135 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.134020618556701 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07628865979381441 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.27835051546391754 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5360824742268041 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6701030927835051 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7628865979381443 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5088292931094907 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.42847733595156284 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.4347457352503283 |
|
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-v17") |
|
# Run inference |
|
sentences = [ |
|
'remediate the incident, promptly notify relevant individuals, and report such data security incidents to the regulatory department(s). Thus, you should have a robust security breach response mechanism in place. ## 7\\. Cross border data transfer and data localization requirements: Under DSL, Critical Information Infrastructure Operators are required to store the important data in the territory of China and cross-border transfer is regulated by the CSL. CIIOs need to conduct a security assessment in accordance with the measures jointly defined by CAC and the relevant departments under the State Council for the cross-border transfer of important data for business necessity. For non Critical Information Infrastructure operators, the important data cross-border transfer will be regulated by the measures announced by the Cyberspace Administration of China (CAC) and other authorities. However, those “measures” have still not yet been released. DSL also intends to establish a data national security review and export control system to restrict the cross-border transmission of data', |
|
'What are the requirements for storing important data in the territory of China under DSL?', |
|
'What is the margin of error generally estimated for worldwide Monthly Active People (MAP)?', |
|
] |
|
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 | 0.2887 | |
|
| cosine_accuracy@3 | 0.5361 | |
|
| cosine_accuracy@5 | 0.6804 | |
|
| cosine_accuracy@10 | 0.7835 | |
|
| cosine_precision@1 | 0.2887 | |
|
| cosine_precision@3 | 0.1787 | |
|
| cosine_precision@5 | 0.1361 | |
|
| cosine_precision@10 | 0.0784 | |
|
| cosine_recall@1 | 0.2887 | |
|
| cosine_recall@3 | 0.5361 | |
|
| cosine_recall@5 | 0.6804 | |
|
| cosine_recall@10 | 0.7835 | |
|
| cosine_ndcg@10 | 0.5259 | |
|
| cosine_mrr@10 | 0.4444 | |
|
| **cosine_map@100** | **0.4516** | |
|
|
|
#### 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 | 0.299 | |
|
| cosine_accuracy@3 | 0.5567 | |
|
| cosine_accuracy@5 | 0.701 | |
|
| cosine_accuracy@10 | 0.7732 | |
|
| cosine_precision@1 | 0.299 | |
|
| cosine_precision@3 | 0.1856 | |
|
| cosine_precision@5 | 0.1402 | |
|
| cosine_precision@10 | 0.0773 | |
|
| cosine_recall@1 | 0.299 | |
|
| cosine_recall@3 | 0.5567 | |
|
| cosine_recall@5 | 0.701 | |
|
| cosine_recall@10 | 0.7732 | |
|
| cosine_ndcg@10 | 0.5285 | |
|
| cosine_mrr@10 | 0.4505 | |
|
| **cosine_map@100** | **0.4581** | |
|
|
|
#### 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 | 0.2784 | |
|
| cosine_accuracy@3 | 0.5361 | |
|
| cosine_accuracy@5 | 0.6701 | |
|
| cosine_accuracy@10 | 0.7629 | |
|
| cosine_precision@1 | 0.2784 | |
|
| cosine_precision@3 | 0.1787 | |
|
| cosine_precision@5 | 0.134 | |
|
| cosine_precision@10 | 0.0763 | |
|
| cosine_recall@1 | 0.2784 | |
|
| cosine_recall@3 | 0.5361 | |
|
| cosine_recall@5 | 0.6701 | |
|
| cosine_recall@10 | 0.7629 | |
|
| cosine_ndcg@10 | 0.5088 | |
|
| cosine_mrr@10 | 0.4285 | |
|
| **cosine_map@100** | **0.4347** | |
|
|
|
<!-- |
|
## 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: 1,872 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: 4 tokens</li><li>mean: 207.32 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 21.79 tokens</li><li>max: 102 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
|
| <code>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, 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</code> | <code>What is the purpose of Third Party & Cookie Consent in data automation and security?</code> | |
|
| <code>the Tietosuojalaki. ### Greece #### Greece **Effective Date** : August 28, 2019 **Region** : EMEA (Europe, Middle East, Africa) Greek Law 4624/2019 was enacted to implement the GDPR and Directive (EU) 2016/680. The Hellenic Data Protection Agency (Αρχή προστασίας δεδομένων προσωπικού χαρακτήρα) is primarily responsible for overseeing the enforcement and implementation of Law 4624/2019 as well as the ePrivacy Directive within Greece. ### Iceland #### Iceland **Effective Date** : July 15, 2018 **Region** : EMEA (Europe, Middle East, Africa) Act 90/2018 on Data Protection and Processing</code> | <code>What is the role of the Hellenic Data Protection Agency in overseeing the enforcement and implementation of Greek Law 4624/2019 and the ePrivacy Directive in Greece?</code> | |
|
| <code>EU. GDPR also applies to organizations located outside the EU (those that do not have an establishment in the EU) if they offer goods or services to, or monitor the behavior of, data subjects located in the EU, irrespective of their nationality or the company’s location. ## Data Subject Rights PDPL provides individuals rights relating to their personal data, which they can exercise. Under PDPL, the data controller should ensure the identity verification of the data subject before processing his/her data subject request. Also, the data controller must not charge for data subjects for making the data subject requests. The data subject may file a complaint to the Authority against the data controller, where the data subject does not accept the data controller’s decision regarding the request, or if the prescribed period has expired without the data subject’s receipt of any notice regarding his request. GDPR also ensures data subject rights where the data subjects can request the controller or, whatever their nationality or place of residence, concerning the processing of their personal data.” Regarding extraterritorial scope, GDPR applies to organizations that are not established in the EU, but instead monitor individuals’ behavior, as long as their behavior occurs in the EU. GDPR also applies to organizations located outside the EU (those that do not have an establishment in the EU) if they offer goods or services to, or monitor the behavior of, data subjects located in the EU, irrespective of their nationality or the company’s location. ## Rights Both regulations give individuals rights relating to their personal data, which they can exercise. Under LPPD, the data controller must process data subject’ requests and take all necessary administrative and technical measures within 30 days. LPPD does not provide a period extension. There is no fee for the data subject’ request to data controllers. However, the data controller may impose a fee, as set by the</code> | <code>What are the data subjects' rights under GDPR regarding behavior monitoring, and how do they compare to the rights under PDPL?</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 |
|
], |
|
"matryoshka_weights": [ |
|
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`: 2 |
|
- `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`: 2 |
|
- `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_256_cosine_map@100 | dim_512_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| 0.1695 | 10 | 3.9813 | - | - | - | |
|
| 0.3390 | 20 | 2.6276 | - | - | - | |
|
| 0.5085 | 30 | 1.7029 | - | - | - | |
|
| 0.6780 | 40 | 0.641 | - | - | - | |
|
| 0.8475 | 50 | 0.391 | - | - | - | |
|
| **1.0** | **59** | **-** | **0.4761** | **0.4928** | **0.4919** | |
|
| 0.1695 | 10 | 1.362 | - | - | - | |
|
| 0.3390 | 20 | 0.7574 | - | - | - | |
|
| 0.5085 | 30 | 0.5287 | - | - | - | |
|
| 0.6780 | 40 | 0.096 | - | - | - | |
|
| 0.8475 | 50 | 0.0699 | - | - | - | |
|
| **1.0** | **59** | **-** | **0.4483** | **0.4913** | **0.4925** | |
|
| 1.0169 | 60 | 0.25 | - | - | - | |
|
| 1.1864 | 70 | 1.043 | - | - | - | |
|
| 1.3559 | 80 | 0.8176 | - | - | - | |
|
| 1.5254 | 90 | 0.6276 | - | - | - | |
|
| 1.6949 | 100 | 0.0992 | - | - | - | |
|
| 1.8644 | 110 | 0.0993 | - | - | - | |
|
| 2.0 | 118 | - | 0.4469 | 0.4785 | 0.4862 | |
|
| 0.1695 | 10 | 1.0617 | - | - | - | |
|
| 0.3390 | 20 | 0.7721 | - | - | - | |
|
| 0.5085 | 30 | 0.6991 | - | - | - | |
|
| 0.6780 | 40 | 0.095 | - | - | - | |
|
| 0.8475 | 50 | 0.0695 | - | - | - | |
|
| **1.0** | **59** | **-** | **0.4519** | **0.4786** | **0.4748** | |
|
| 1.0169 | 60 | 0.1892 | - | - | - | |
|
| 1.1864 | 70 | 0.7125 | - | - | - | |
|
| 1.3559 | 80 | 0.5113 | - | - | - | |
|
| 1.5254 | 90 | 0.437 | - | - | - | |
|
| 1.6949 | 100 | 0.0432 | - | - | - | |
|
| 1.8644 | 110 | 0.0471 | - | - | - | |
|
| 2.0 | 118 | - | 0.4347 | 0.4581 | 0.4516 | |
|
|
|
* 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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