|
--- |
|
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_ndcg@80 |
|
- cosine_mrr@10 |
|
- cosine_map@100 |
|
pipeline_tag: sentence-similarity |
|
tags: |
|
- sentence-transformers |
|
- sentence-similarity |
|
- feature-extraction |
|
- generated_from_trainer |
|
- dataset_size:1496 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: We are currently involved in, and may in the future be involved |
|
in, legal proceedings, claims, and government investigations in the ordinary course |
|
of business. These include proceedings, claims, and investigations relating to, |
|
among other things, regulatory matters, commercial matters, intellectual property, |
|
competition, tax, employment, pricing, discrimination, consumer rights, personal |
|
injury, and property rights. |
|
sentences: |
|
- What factors does the regulatory authority consider when ensuring data protection |
|
in cross border transfers in Zimbabwe? |
|
- How does Securiti enable enterprises to safely use data and the cloud while managing |
|
security, privacy, and compliance risks? |
|
- What types of legal issues is the company currently involved in? |
|
- source_sentence: The Company’s minority market share in the global smartphone, personal |
|
computer and tablet markets can make developers less inclined to develop or upgrade |
|
software for the Company’s products and more inclined to devote their resources |
|
to developing and upgrading software for competitors’ products with larger market |
|
share. When developers focus their efforts on these competing platforms, the availability |
|
and quality of applications for the Company’s devices can suffer. |
|
sentences: |
|
- What is the role of obtaining consent in Thailand's PDPA? |
|
- Why might developers be less inclined to develop or upgrade software for the Company's |
|
products? |
|
- What caused the increase in energy generation and storage segment revenue in 2023? |
|
- source_sentence: '** : EMEA (Europe, the Middle East and Africa) The Irish DPA implements |
|
the GDPR into the national law by incorporating most of the provisions of the |
|
GDPR with limited additions and deletions. It contains several provisions restricting |
|
data subjects’ rights that they generally have under the GDPR, for example, where |
|
restrictions are necessary for the enforcement of civil law claims. Resources* |
|
: Irish DPA Overview Irish Cookie Guidance ### Japan #### Japan’s Act on the Protection |
|
of Personal Information (APPI) **Effective Date (Amended APPI)** : April 01, 2022 |
|
**Region** : APAC (Asia-Pacific) Japan’s APPI regulates personal related information |
|
and applies to any Personal Information Controller (the “PIC''''), that is a person |
|
or entity providing personal related information for use in business in Japan. |
|
The APPI also applies to the foreign' |
|
sentences: |
|
- What are the requirements for CIIOs and personal information processors in the |
|
state cybersecurity department regarding cross-border data transfers and certifications? |
|
- How does the Irish DPA implement the GDPR into national law? |
|
- What is the current status of the Personal Data Protection Act in El Salvador |
|
compared to Monaco and Venezuela? |
|
- source_sentence: View Salesforce View Workday View GCP View Azure View Oracle View |
|
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 Privacy |
|
View Security View Governance View Marketing View Resources Blog View Collateral |
|
View Knowledge Center View Securiti Education View Company About Us View Partner |
|
Program View Contact Us View News Coverage |
|
sentences: |
|
- What is the role of ANPD in ensuring LGPD compliance and protecting data subject |
|
rights, including those related to health professionals? |
|
- According to the Spanish data protection law, who is required to hire a DPO if |
|
they possess certain information in the event of a data breach? |
|
- What is GCP and how does it relate to privacy, security, governance, marketing, |
|
and resources? |
|
- source_sentence: 'vital interests of the data subject; Complying with an obligation |
|
prescribed in PDPL, not being a contractual obligation, or complying with an order |
|
from a competent court, the Public Prosecution, the investigation Judge, or the |
|
Military Prosecution; or Preparing or pursuing a legal claim or defense. vs Articles: |
|
44 50, Recitals: 101, 112 GDPR states that personal data shall be transferred |
|
to a third country or international organization with an adequate protection level |
|
as determined by the EU Commission. Suppose there is no decision on an adequate |
|
protection level. In that case, a transfer is only permitted when the data controller |
|
or data processor provides appropriate safeguards that ensure data subject rights. |
|
Appropriate safeguards include: BCRs with specific requirements (e.g., a legal |
|
basis for processing, a retention period, and complaint procedures) Standard data |
|
protection clauses adopted by the EU Commission, level of protection. If there |
|
is no adequate level of protection, then data controllers in Turkey and abroad |
|
shall commit, in writing, to provide an adequate level of protection abroad, as |
|
well as agree on the fact that the transfer is permitted by the Board of KVKK. |
|
vs Articles 44 50 Recitals 101, 112 GDPR states that personal data shall be transferred |
|
to a third country or international organization with an adequate protection level |
|
as determined by the EU Commission. Suppose there is no decision on an adequate |
|
protection level. In that case, a transfer is only permitted when the data controller |
|
or data processor provides appropriate safeguards that ensure data subject'' rights. |
|
Appropriate safeguards include: BCRs with specific requirements (e.g., a legal |
|
basis for processing, a retention period, and complaint procedures); standard |
|
data protection clauses adopted by the EU Commission or by a supervisory authority; |
|
an approved code' |
|
sentences: |
|
- What is the right to be informed in relation to personal data? |
|
- In what situations can a controller process personal data to protect vital interests? |
|
- What obligations in PDPL must data controllers or processors meet to protect personal |
|
data transferred to a third country or international organization? |
|
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: 0.4020618556701031 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5773195876288659 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6804123711340206 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7938144329896907 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.4020618556701031 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1924398625429553 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1360824742268041 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07938144329896907 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.4020618556701031 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5773195876288659 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6804123711340206 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7938144329896907 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5832092053824987 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@80 |
|
value: 0.6222698401457883 |
|
name: Cosine Ndcg@80 |
|
- type: cosine_mrr@10 |
|
value: 0.5174930453280969 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5253009685878662 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5670103092783505 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6597938144329897 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7938144329896907 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.18900343642611683 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1319587628865979 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07938144329896907 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5670103092783505 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6597938144329897 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7938144329896907 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5860165941440372 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@80 |
|
value: 0.6252535691605303 |
|
name: Cosine Ndcg@80 |
|
- type: cosine_mrr@10 |
|
value: 0.5218622156766489 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5297061448856729 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.5979381443298969 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.6494845360824743 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7628865979381443 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.1993127147766323 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.12989690721649483 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07628865979381441 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.41237113402061853 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.5979381443298969 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.6494845360824743 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7628865979381443 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.5782766042135054 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_ndcg@80 |
|
value: 0.6240012013315989 |
|
name: Cosine Ndcg@80 |
|
- type: cosine_mrr@10 |
|
value: 0.5207167403043692 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5307304570652817 |
|
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-3-v23") |
|
# Run inference |
|
sentences = [ |
|
"vital interests of the data subject; Complying with an obligation prescribed in PDPL, not being a contractual obligation, or complying with an order from a competent court, the Public Prosecution, the investigation Judge, or the Military Prosecution; or Preparing or pursuing a legal claim or defense. vs Articles: 44 50, Recitals: 101, 112 GDPR states that personal data shall be transferred to a third country or international organization with an adequate protection level as determined by the EU Commission. Suppose there is no decision on an adequate protection level. In that case, a transfer is only permitted when the data controller or data processor provides appropriate safeguards that ensure data subject rights. Appropriate safeguards include: BCRs with specific requirements (e.g., a legal basis for processing, a retention period, and complaint procedures) Standard data protection clauses adopted by the EU Commission, level of protection. If there is no adequate level of protection, then data controllers in Turkey and abroad shall commit, in writing, to provide an adequate level of protection abroad, as well as agree on the fact that the transfer is permitted by the Board of KVKK. vs Articles 44 50 Recitals 101, 112 GDPR states that personal data shall be transferred to a third country or international organization with an adequate protection level as determined by the EU Commission. Suppose there is no decision on an adequate protection level. In that case, a transfer is only permitted when the data controller or data processor provides appropriate safeguards that ensure data subject' rights. Appropriate safeguards include: BCRs with specific requirements (e.g., a legal basis for processing, a retention period, and complaint procedures); standard data protection clauses adopted by the EU Commission or by a supervisory authority; an approved code", |
|
'What obligations in PDPL must data controllers or processors meet to protect personal data transferred to a third country or international organization?', |
|
'In what situations can a controller process personal data to protect vital interests?', |
|
] |
|
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 | 0.4021 | |
|
| cosine_accuracy@3 | 0.5773 | |
|
| cosine_accuracy@5 | 0.6804 | |
|
| cosine_accuracy@10 | 0.7938 | |
|
| cosine_precision@1 | 0.4021 | |
|
| cosine_precision@3 | 0.1924 | |
|
| cosine_precision@5 | 0.1361 | |
|
| cosine_precision@10 | 0.0794 | |
|
| cosine_recall@1 | 0.4021 | |
|
| cosine_recall@3 | 0.5773 | |
|
| cosine_recall@5 | 0.6804 | |
|
| cosine_recall@10 | 0.7938 | |
|
| cosine_ndcg@10 | 0.5832 | |
|
| cosine_ndcg@80 | 0.6223 | |
|
| cosine_mrr@10 | 0.5175 | |
|
| **cosine_map@100** | **0.5253** | |
|
|
|
#### 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 | 0.4124 | |
|
| cosine_accuracy@3 | 0.567 | |
|
| cosine_accuracy@5 | 0.6598 | |
|
| cosine_accuracy@10 | 0.7938 | |
|
| cosine_precision@1 | 0.4124 | |
|
| cosine_precision@3 | 0.189 | |
|
| cosine_precision@5 | 0.132 | |
|
| cosine_precision@10 | 0.0794 | |
|
| cosine_recall@1 | 0.4124 | |
|
| cosine_recall@3 | 0.567 | |
|
| cosine_recall@5 | 0.6598 | |
|
| cosine_recall@10 | 0.7938 | |
|
| cosine_ndcg@10 | 0.586 | |
|
| cosine_ndcg@80 | 0.6253 | |
|
| cosine_mrr@10 | 0.5219 | |
|
| **cosine_map@100** | **0.5297** | |
|
|
|
#### 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 | 0.4124 | |
|
| cosine_accuracy@3 | 0.5979 | |
|
| cosine_accuracy@5 | 0.6495 | |
|
| cosine_accuracy@10 | 0.7629 | |
|
| cosine_precision@1 | 0.4124 | |
|
| cosine_precision@3 | 0.1993 | |
|
| cosine_precision@5 | 0.1299 | |
|
| cosine_precision@10 | 0.0763 | |
|
| cosine_recall@1 | 0.4124 | |
|
| cosine_recall@3 | 0.5979 | |
|
| cosine_recall@5 | 0.6495 | |
|
| cosine_recall@10 | 0.7629 | |
|
| cosine_ndcg@10 | 0.5783 | |
|
| cosine_ndcg@80 | 0.624 | |
|
| cosine_mrr@10 | 0.5207 | |
|
| **cosine_map@100** | **0.5307** | |
|
|
|
<!-- |
|
## 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: 1,496 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: 67 tokens</li><li>mean: 216.99 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 21.6 tokens</li><li>max: 102 tokens</li></ul> | |
|
* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| |
|
| <code>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-time streaming platforms Learn more Data Consent Automation First Party Consent | Third Party & Cookie</code> | <code>What is the purpose of the Data Command Center?</code> | |
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| <code>data subject must be notified of any such extension within one month of receiving the request, along with the reasons for the delay and the possibility of complaining to the supervisory authority. The right to restrict processing applies when the data subject contests data accuracy, the processing is unlawful, and the data subject opposes erasure and requests restriction. The controller must inform data subjects before any such restriction is lifted. Under GDPR, the data subject also has the right to obtain from the controller the rectification of inaccurate personal data and to have incomplete personal data completed. Article: 22 Under PDPL, if a decision is based solely on automated processing of personal data intended to assess the data subject regarding his/her performance at work, financial standing, credit-worthiness, reliability, or conduct, then the data subject has the right to request processing in a manner that is not solely automated. This right shall not apply where the decision is taken in the course of entering into</code> | <code>What is the requirement for notifying the data subject of any extension under GDPR and PDPL?</code> | |
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| <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> | |
|
* 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`: 1 |
|
- `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`: 1 |
|
- `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 | |
|
|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:| |
|
| 0.2128 | 10 | 3.8486 | - | - | - | |
|
| 0.4255 | 20 | 2.3622 | - | - | - | |
|
| 0.6383 | 30 | 2.3216 | - | - | - | |
|
| 0.8511 | 40 | 1.3247 | - | - | - | |
|
| **1.0** | **47** | **-** | **0.5307** | **0.5297** | **0.5253** | |
|
|
|
* 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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