|
--- |
|
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:7872 |
|
- loss:MatryoshkaLoss |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
|
- source_sentence: 'personal information within 45 days. If personal information was |
|
sold, organizations must also identify and inform the consumer of the sources |
|
of information, its collection purpose, and the categories of third parties to |
|
whom the data was sold to. As per the CCPA, the following information must be |
|
provided in an access request: The categories of personal information the business |
|
has collected about the consumer in the preceding 12 months. For each category |
|
identified, the categories of third parties to whom it disclosed that particular |
|
category of personal information. The categories of sources from which the personal |
|
information was collected. The business or commercial purpose for which it collected |
|
or sold the personal information. The categories of third parties with whom the |
|
business shares consumers’ Personal Information. The right to access is one of |
|
the toughest articles for businesses to comply with because organizations need |
|
to track the location of every consumer’s personal information in all on-premises |
|
and multicloud data systems.' |
|
sentences: |
|
- What are the UCPA requirements for organizations regarding personal data handling, |
|
including pseudonymous and sensitive data, and data transfer to third parties |
|
in certain circumstances? |
|
- What are the benefits of implementing CCPA for businesses in terms of reducing |
|
costs, liabilities, and human effort while ensuring effortless compliance? |
|
- What information must organizations provide regarding the categories of third |
|
parties in relation to personal information under the CCPA? |
|
- source_sentence: 'on businesses that meet these criteria, regardless of their physical |
|
presence in Colorado. Colorado is a one-party consent state for recording conversations. |
|
This means that as long as one participant in the conversation consents to the |
|
recording, it is generally legal. However, it''s important to understand and adhere |
|
to the specific legal requirements and limitations. ## Join Our Newsletter Get |
|
all the latest information, law updates and more delivered to your inbox ### Share |
|
Copy 41 ### 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 15, |
|
2023 ## Right of Access to Personal Data: What To Know The wealth of data available' |
|
sentences: |
|
- What solutions does Oracle offer for data security and governance? |
|
- What are the legal requirements for recording conversations in Colorado, considering |
|
consent laws and data protection regulations? |
|
- What are the key components of the NVIDIA computing platform? |
|
- source_sentence: 'such personal data have been collected or where such collected |
|
personal data are beyond the extent required, discriminatory, unfair or illegal. |
|
### Right to Erasure Data subjects can request omission or erasure of the personal |
|
data upon cessation of the purpose for which the processing has been conducted, |
|
or where all justifications for maintaining such personal data by the organization |
|
cease to exist. ## Facts related to Qatar DPL 1 The DPL incorporates concepts |
|
familiar from other international privacy frameworks to protect a consumer''s |
|
personal data. 2 Under the DPL, a data controller is responsible for identifying |
|
all parties who process personal data on its behalf. 3 In Qatar, the Compliance |
|
and Data Protection department (the “CDP”)at MoTC is responsible for the enforcement |
|
of the DPL. . 4 The MoTC can also impose fines of up to QAR 5 million (US$1.4 |
|
million)' |
|
sentences: |
|
- What is Securiti's mission regarding data protection laws and regulations? |
|
- What is the role of the Nominating and Corporate Governance Committee at NVIDIA? |
|
- What is the right to erasure and how does it apply to personal data in Qatar under |
|
the DPL? |
|
- source_sentence: '. It allows you to identify gaps in compliance and address the |
|
risks. Seamlessly expand assessment capabilities across your vendor ecosystem |
|
to maintain compliance against LPPD requirements. ## Map data flows Track data |
|
flows in your organizations by having a centralized catalogue of internal data |
|
process flows as well as flows for data transfer to service providers and other |
|
third parties. ## Manage vendor risk Articles: 8, 9, 12 Track, manage and monitor |
|
privacy and security readiness for all your service providers from a single interface. |
|
Collaborate instantly with vendors, automate data requests, and manage all vendor |
|
contracts and compliance documents. ## Breach Response Notification Article: 12(5), |
|
Data Protection Board Decision 2019/10 Automates compliance actions and breach |
|
notifications to concerned stakeholders in relation to security incidents by leveraging |
|
a knowledge database on security incident diagnosis and response. ## Key data |
|
subject rights encoded within LPPD Access: Data subjects have the right to access, |
|
, and privacy impact assessment system, you can gauge your organization''s posture |
|
against Qatar DPL requirements, identify the gaps, and address the risks. Seamlessly |
|
being able to expand assessment capabilities across your vendor ecosystem to maintain |
|
compliance against Qatar DPL requirements. ## Map data flows Articles: 23, 24, |
|
25 Track data flows in your organizations, trace this data, catalog, transfer, |
|
and document business process flows internally and to service providers or third |
|
parties. ## Manage vendor risk Articles: 15, 12 Keep track of privacy and security |
|
readiness for all your service providers from a single interface. Collaborate |
|
instantly with vendors, automate data requests and deletions, and manage all vendor |
|
contracts and compliance documents. ## Breach Response Notification Articles: |
|
11(5), 14 Automates compliance actions and breach notifications to concerned stakeholders |
|
in relation to security incidents by leveraging a knowledge database on security |
|
incident diagnosis and response.' |
|
sentences: |
|
- What is the purpose of a centralized catalogue in managing data flows, vendor |
|
risk, and compliance with LPPD and Qatar DPL requirements? |
|
- What are the security requirements for data handlers according to Spain's Data |
|
Protection Law? |
|
- What are some key rights granted to data subjects under Bahrain PDPL? |
|
- source_sentence: 'office of the Federal Commissioner for Data Protection and Freedom |
|
of Information, with its headquarters in the city of Bonn. It is led by a Federal |
|
Commissioner, elected via a vote by the German Bundestag. Eligibility criteria |
|
include being at least 35 years old, appropriate qualifications in the field of |
|
data protection law gained through relevant professional experience. The Commissioner''s |
|
term is for five years, which can be extended once. The Commissioner has the responsibility |
|
to act as the primary office responsible for enforcing the Federal Data Protection |
|
Act within Germany. Some of the office''s key responsibilities include: Advising |
|
the Bundestag, the Bundesrat, and the Federal Government on administrative and |
|
legislative measures related to data protection within the country; To oversee |
|
and implement both the GDPR and Federal Data Protection Act within Germany; To |
|
promote awareness within the public related to the risks, rules, safeguards, and |
|
rights concerning the processing of personal data; To handle all, within Germany. |
|
It supplements and aligns with the requirements of the EU GDPR. Yes, Germany is |
|
covered by GDPR (General Data Protection Regulation). GDPR is a regulation that |
|
applies uniformly across all EU member states, including Germany. The Federal |
|
Data Protection Act established the office of the Federal Commissioner for Data |
|
Protection and Freedom of Information, with its headquarters in the city of Bonn. |
|
It is led by a Federal Commissioner, elected via a vote by the German Bundestag. |
|
Germany''s interpretation is the Bundesdatenschutzgesetz (BDSG), the German Federal |
|
Data Protection Act. It mirrors the GDPR in all key areas while giving local German |
|
regulatory authorities the power to enforce it more efficiently nationally. ## |
|
Join Our Newsletter Get all the latest information, law updates and more delivered |
|
to your inbox ### Share Copy 14 ### More Stories that May Interest You View More' |
|
sentences: |
|
- What is the collection and use of personal information by businesses? |
|
- How does Data Mapping Automation optimize data governance and enable data security |
|
and protection? |
|
- What are the main responsibilities of the Federal Commissioner for Data Protection |
|
and Freedom of Information in enforcing data protection laws in Germany, including |
|
the GDPR and the Federal Data Protection Act? |
|
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: 0.6907216494845361 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8865979381443299 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9381443298969072 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9690721649484536 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.29553264604810997 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18762886597938144 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09690721649484535 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8865979381443299 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9381443298969072 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9690721649484536 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8386189701330025 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7955735558828344 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7967787552384278 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 512 |
|
type: dim_512 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8762886597938144 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.9278350515463918 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9690721649484536 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2920962199312715 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.18556701030927836 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09690721649484535 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8762886597938144 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.9278350515463918 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9690721649484536 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8329963353635171 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7889011618393064 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7896128390908116 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 256 |
|
type: dim_256 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.8556701030927835 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8969072164948454 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.9381443298969072 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2852233676975945 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.17938144329896905 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.09381443298969072 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.6907216494845361 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.8556701030927835 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8969072164948454 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.9381443298969072 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.8161733445083468 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7769595810832928 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7795708391204863 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 128 |
|
type: dim_128 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5979381443298969 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.7731958762886598 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.8247422680412371 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8865979381443299 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5979381443298969 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.25773195876288657 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.16494845360824742 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08865979381443297 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5979381443298969 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.7731958762886598 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.8247422680412371 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8865979381443299 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.7462462760759706 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.7009818360333826 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.7046924157583041 |
|
name: Cosine Map@100 |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: dim 64 |
|
type: dim_64 |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.5154639175257731 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6804123711340206 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.711340206185567 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.7731958762886598 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.5154639175257731 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.2268041237113402 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.1422680412371134 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.07731958762886597 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.5154639175257731 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6804123711340206 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.711340206185567 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.7731958762886598 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6463393588703956 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.6055105547373589 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.6128426579691056 |
|
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-v16") |
|
# Run inference |
|
sentences = [ |
|
"office of the \u200b\u200bFederal Commissioner for Data Protection and Freedom of Information, with its headquarters in the city of Bonn. It is led by a Federal Commissioner, elected via a vote by the German Bundestag. Eligibility criteria include being at least 35 years old, appropriate qualifications in the field of data protection law gained through relevant professional experience. The Commissioner's term is for five years, which can be extended once. The Commissioner has the responsibility to act as the primary office responsible for enforcing the Federal Data Protection Act within Germany. Some of the office's key responsibilities include: Advising the Bundestag, the Bundesrat, and the Federal Government on administrative and legislative measures related to data protection within the country; To oversee and implement both the GDPR and Federal Data Protection Act within Germany; To promote awareness within the public related to the risks, rules, safeguards, and rights concerning the processing of personal data; To handle all, within Germany. It supplements and aligns with the requirements of the EU GDPR. Yes, Germany is covered by GDPR (General Data Protection Regulation). GDPR is a regulation that applies uniformly across all EU member states, including Germany. The Federal Data Protection Act established the office of the \u200b\u200bFederal Commissioner for Data Protection and Freedom of Information, with its headquarters in the city of Bonn. It is led by a Federal Commissioner, elected via a vote by the German Bundestag. Germany's interpretation is the Bundesdatenschutzgesetz (BDSG), the German Federal Data Protection Act. It mirrors the GDPR in all key areas while giving local German regulatory authorities the power to enforce it more efficiently nationally. ## Join Our Newsletter Get all the latest information, law updates and more delivered to your inbox ### Share Copy 14 ### More Stories that May Interest You View More", |
|
'What are the main responsibilities of the Federal Commissioner for Data Protection and Freedom of Information in enforcing data protection laws in Germany, including the GDPR and the Federal Data Protection Act?', |
|
'What is the collection and use of personal information by businesses?', |
|
] |
|
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> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Dataset: `dim_768` |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6907 | |
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| cosine_accuracy@3 | 0.8866 | |
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| cosine_accuracy@5 | 0.9381 | |
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| cosine_accuracy@10 | 0.9691 | |
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| cosine_precision@1 | 0.6907 | |
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| cosine_precision@3 | 0.2955 | |
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| cosine_precision@5 | 0.1876 | |
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| cosine_precision@10 | 0.0969 | |
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| cosine_recall@1 | 0.6907 | |
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| cosine_recall@3 | 0.8866 | |
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| cosine_recall@5 | 0.9381 | |
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| cosine_recall@10 | 0.9691 | |
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| cosine_ndcg@10 | 0.8386 | |
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| cosine_mrr@10 | 0.7956 | |
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| **cosine_map@100** | **0.7968** | |
|
|
|
#### 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 | 0.6907 | |
|
| cosine_accuracy@3 | 0.8763 | |
|
| cosine_accuracy@5 | 0.9278 | |
|
| cosine_accuracy@10 | 0.9691 | |
|
| cosine_precision@1 | 0.6907 | |
|
| cosine_precision@3 | 0.2921 | |
|
| cosine_precision@5 | 0.1856 | |
|
| cosine_precision@10 | 0.0969 | |
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| cosine_recall@1 | 0.6907 | |
|
| cosine_recall@3 | 0.8763 | |
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| cosine_recall@5 | 0.9278 | |
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| cosine_recall@10 | 0.9691 | |
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| cosine_ndcg@10 | 0.833 | |
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| cosine_mrr@10 | 0.7889 | |
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| **cosine_map@100** | **0.7896** | |
|
|
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#### Information Retrieval |
|
* Dataset: `dim_256` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.6907 | |
|
| cosine_accuracy@3 | 0.8557 | |
|
| cosine_accuracy@5 | 0.8969 | |
|
| cosine_accuracy@10 | 0.9381 | |
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| cosine_precision@1 | 0.6907 | |
|
| cosine_precision@3 | 0.2852 | |
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| cosine_precision@5 | 0.1794 | |
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| cosine_precision@10 | 0.0938 | |
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| cosine_recall@1 | 0.6907 | |
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| cosine_recall@3 | 0.8557 | |
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| cosine_recall@5 | 0.8969 | |
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| cosine_recall@10 | 0.9381 | |
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| cosine_ndcg@10 | 0.8162 | |
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| cosine_mrr@10 | 0.777 | |
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| **cosine_map@100** | **0.7796** | |
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|
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#### Information Retrieval |
|
* Dataset: `dim_128` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.5979 | |
|
| cosine_accuracy@3 | 0.7732 | |
|
| cosine_accuracy@5 | 0.8247 | |
|
| cosine_accuracy@10 | 0.8866 | |
|
| cosine_precision@1 | 0.5979 | |
|
| cosine_precision@3 | 0.2577 | |
|
| cosine_precision@5 | 0.1649 | |
|
| cosine_precision@10 | 0.0887 | |
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| cosine_recall@1 | 0.5979 | |
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| cosine_recall@3 | 0.7732 | |
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| cosine_recall@5 | 0.8247 | |
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| cosine_recall@10 | 0.8866 | |
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| cosine_ndcg@10 | 0.7462 | |
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| cosine_mrr@10 | 0.701 | |
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| **cosine_map@100** | **0.7047** | |
|
|
|
#### Information Retrieval |
|
* Dataset: `dim_64` |
|
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
|
|
|
| Metric | Value | |
|
|:--------------------|:-----------| |
|
| cosine_accuracy@1 | 0.5155 | |
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| cosine_accuracy@3 | 0.6804 | |
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| cosine_accuracy@5 | 0.7113 | |
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| cosine_accuracy@10 | 0.7732 | |
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| cosine_precision@1 | 0.5155 | |
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| cosine_precision@3 | 0.2268 | |
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| cosine_precision@5 | 0.1423 | |
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| cosine_precision@10 | 0.0773 | |
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| cosine_recall@1 | 0.5155 | |
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| cosine_recall@3 | 0.6804 | |
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| cosine_recall@5 | 0.7113 | |
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| cosine_recall@10 | 0.7732 | |
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| cosine_ndcg@10 | 0.6463 | |
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| cosine_mrr@10 | 0.6055 | |
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| **cosine_map@100** | **0.6128** | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 7,872 training samples |
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* Columns: <code>positive</code> and <code>anchor</code> |
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* Approximate statistics based on the first 1000 samples: |
|
| | positive | anchor | |
|
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 18 tokens</li><li>mean: 206.12 tokens</li><li>max: 414 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 21.62 tokens</li><li>max: 102 tokens</li></ul> | |
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* Samples: |
|
| positive | anchor | |
|
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------| |
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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> | |
|
| <code>on both in terms of material and territorial scope. ### 1.1 Material Scope The Spanish data protection law affords blanket protection for all data that may have been collected on a data subject. There are only a handful of exceptions that include: Information subject to a pending legal case Information collected concerning the investigation of terrorism or organised crime Information classified as "Confidential" for matters related to Spain's national security ### 1.2 Territorial Scope The Spanish data protection law applies to all data handlers that are: Carrying out data collection activities in Spain Not established in Spain but carrying out data collection activities on Spanish territory Not established within the European Union but carrying out data collection activities on Spanish residents unless for data transit purposes only ## 2\. Obligations for Organizations Under Spanish Data Protection Law The Spanish data protection law and GDPR lay out specific obligations for all data handlers. These obligations ensure, . ### 2.3 Privacy Policy Requirements Spain's data protection law requires all data handlers to inform the data subject of the following in their privacy policy: The purpose of collecting the data and the recipients of the information The obligatory or voluntary nature of the reply to the questions put to them The consequences of obtaining the data or of refusing to provide them The possibility of exercising rights of access, rectification, erasure, portability, and objection The identity and address of the controller or their local Spanish representative ### 2.4 Security Requirements Article 9 of Spain's Data Protection Law is direct and explicit in stating the responsibility of the data handler is to take adequate measures to ensure the protection of any data collected. It mandates all data handlers to adopt technical and organisational measures necessary to ensure the security of the personal data and prevent their alteration, loss, and unauthorised processing or access. Additionally, collection of any</code> | <code>What are the requirements for organizations under the Spanish data protection law regarding privacy policies and security measures?</code> | |
|
| <code>before the point of collection of their personal information. ## Right to Erasure The right to erasure gives consumers the right to request deleting all their data stored by the organization. Organizations are supposed to comply within 45 days and must deliver a report to the consumer confirming the deletion of their information. ## Right to Opt-in for Minors Personal information containing minors' personal information cannot be sold by a business unless the minor (age of 13 to 16 years) or the Parent/Guardian (if the minor is aged below 13 years) opt-ins to allow this sale. Businesses can be held liable for the sale of minors' personal information if they either knew or wilfully disregarded the consumer's status as a minor and the minor or Parent/Guardian had not willingly opted in. ## Right to Continued Protection Even when consumers choose to allow a business to collect and sell their personal information, businesses' must sign written</code> | <code>What are the conditions under which businesses can sell minors' personal information?</code> | |
|
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: |
|
```json |
|
{ |
|
"loss": "MultipleNegativesRankingLoss", |
|
"matryoshka_dims": [ |
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768, |
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512, |
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256, |
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128, |
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64 |
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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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1, |
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1 |
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], |
|
"n_dims_per_step": -1 |
|
} |
|
``` |
|
|
|
### Training Hyperparameters |
|
#### Non-Default Hyperparameters |
|
|
|
- `eval_strategy`: epoch |
|
- `per_device_train_batch_size`: 32 |
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- `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 |
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- `load_best_model_at_end`: True |
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- `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 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
|
- `adam_epsilon`: 1e-08 |
|
- `max_grad_norm`: 1.0 |
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- `num_train_epochs`: 2 |
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- `max_steps`: -1 |
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- `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 |
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- `use_ipex`: False |
|
- `bf16`: True |
|
- `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 |
|
- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `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} |
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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 |
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- `group_by_length`: False |
|
- `length_column_name`: length |
|
- `ddp_find_unused_parameters`: None |
|
- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
|
- `dataloader_pin_memory`: True |
|
- `dataloader_persistent_workers`: False |
|
- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `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 |
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- `batch_eval_metrics`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: proportional |
|
|
|
</details> |
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|
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### Training Logs |
|
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_512_cosine_map@100 | dim_64_cosine_map@100 | dim_768_cosine_map@100 | |
|
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:| |
|
| 0.0407 | 10 | 7.3954 | - | - | - | - | - | |
|
| 0.0813 | 20 | 6.0944 | - | - | - | - | - | |
|
| 0.1220 | 30 | 4.9443 | - | - | - | - | - | |
|
| 0.1626 | 40 | 3.8606 | - | - | - | - | - | |
|
| 0.2033 | 50 | 3.0961 | - | - | - | - | - | |
|
| 0.2439 | 60 | 1.8788 | - | - | - | - | - | |
|
| 0.2846 | 70 | 2.3815 | - | - | - | - | - | |
|
| 0.3252 | 80 | 4.0698 | - | - | - | - | - | |
|
| 0.3659 | 90 | 2.2183 | - | - | - | - | - | |
|
| 0.4065 | 100 | 1.9142 | - | - | - | - | - | |
|
| 0.4472 | 110 | 1.5149 | - | - | - | - | - | |
|
| 0.4878 | 120 | 1.7036 | - | - | - | - | - | |
|
| 0.5285 | 130 | 2.9528 | - | - | - | - | - | |
|
| 0.5691 | 140 | 1.0596 | - | - | - | - | - | |
|
| 0.6098 | 150 | 1.7619 | - | - | - | - | - | |
|
| 0.6504 | 160 | 1.6529 | - | - | - | - | - | |
|
| 0.6911 | 170 | 3.097 | - | - | - | - | - | |
|
| 0.7317 | 180 | 1.3802 | - | - | - | - | - | |
|
| 0.7724 | 190 | 1.9744 | - | - | - | - | - | |
|
| 0.8130 | 200 | 5.1313 | - | - | - | - | - | |
|
| 0.8537 | 210 | 1.405 | - | - | - | - | - | |
|
| 0.8943 | 220 | 1.4389 | - | - | - | - | - | |
|
| 0.9350 | 230 | 3.6439 | - | - | - | - | - | |
|
| 0.9756 | 240 | 3.7227 | - | - | - | - | - | |
|
| 1.0122 | 249 | - | 0.6623 | 0.7328 | 0.7549 | 0.5729 | 0.7572 | |
|
| 1.0041 | 250 | 1.3183 | - | - | - | - | - | |
|
| 1.0447 | 260 | 5.2631 | - | - | - | - | - | |
|
| 1.0854 | 270 | 4.0516 | - | - | - | - | - | |
|
| 1.1260 | 280 | 2.5487 | - | - | - | - | - | |
|
| 1.1667 | 290 | 1.7379 | - | - | - | - | - | |
|
| 1.2073 | 300 | 1.1724 | - | - | - | - | - | |
|
| 1.2480 | 310 | 0.7885 | - | - | - | - | - | |
|
| 1.2886 | 320 | 1.2341 | - | - | - | - | - | |
|
| 1.3293 | 330 | 3.3722 | - | - | - | - | - | |
|
| 1.3699 | 340 | 1.2227 | - | - | - | - | - | |
|
| 1.4106 | 350 | 0.8475 | - | - | - | - | - | |
|
| 1.4512 | 360 | 0.7605 | - | - | - | - | - | |
|
| 1.4919 | 370 | 0.8954 | - | - | - | - | - | |
|
| 1.5325 | 380 | 1.9712 | - | - | - | - | - | |
|
| 1.5732 | 390 | 0.5607 | - | - | - | - | - | |
|
| 1.6138 | 400 | 0.9671 | - | - | - | - | - | |
|
| 1.6545 | 410 | 1.0024 | - | - | - | - | - | |
|
| 1.6951 | 420 | 2.1374 | - | - | - | - | - | |
|
| 1.7358 | 430 | 0.8213 | - | - | - | - | - | |
|
| 1.7764 | 440 | 2.1253 | - | - | - | - | - | |
|
| 1.8171 | 450 | 2.7885 | - | - | - | - | - | |
|
| 1.8577 | 460 | 0.9053 | - | - | - | - | - | |
|
| 1.8984 | 470 | 0.9261 | - | - | - | - | - | |
|
| 1.9390 | 480 | 3.1218 | - | - | - | - | - | |
|
| 1.9797 | 490 | 3.0135 | - | - | - | - | - | |
|
| **1.9878** | **492** | **-** | **0.7047** | **0.7796** | **0.7896** | **0.6128** | **0.7968** | |
|
|
|
* 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 |
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@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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