Project Web: https://magpie-align.github.io/ Arxiv Technical Report: https://arxiv.org/abs/2406.08464 Codes: https://github.com/magpie-align/magpie

  🧐 Dataset Details

The Magpie Team generates this dataset for direct preference optimization. This dataset was used to train Magpie-Align/MagpieLM-4B-Chat-v0.1. This dataset is a combination of two datasets:

Half of the dataset (100K) is from Magpie-Align/Magpie-Llama-3.1-Pro-DPO-100K-v0.1. Another half of the dataset (100K) uses… See the full description on the dataset page: https://huggingface.co/datasets/Magpie-Align/MagpieLM-DPO-Data-v0.1.

The General Data Protection Regulation (GDPR) provides, since 25 May 2018, for the mandatory designation of a Data Protection Officer (DPO) in public services and, under certain conditions, by companies and associations.

The delegate — also known as the Data Protection Officer (DPO) — is responsible for ensuring GDPR compliance with the processing of personal data of the body that designated him or her. Internal or external, the delegate may also be appointed on behalf of several bodies.

To ensure the effectiveness of his/her tasks, the delegate shall:

— must have specific professional qualities and knowledge; — must benefit from material and organisational resources, resources and positioning enabling it to carry out its tasks effectively and independently.

To learn more about the role of delegate: https://www.cnil.fr/fr/devenir-delegue-la-protection-des-donnees.

In accordance with the applicable texts, the CNIL shall publish in an open and easily reusable format the name and professional contact details of the bodies that have appointed a Data Protection Officer, as well as the means of contacting the Data Protection Officer.

** Warning 1:** The published data, including the public contact details of delegates, are extracted from the designations of delegates as received by the CNIL via its dedicated teleservice. Any delegate may request the modification of the contact details published directly to the CNIL’s Data Protection Officers Service.

** Warning 2:** Any re-use of published data which would have the nature of personal data (telephone number, e-mail address, etc.) presupposes, on the part of the re-user, verification of the full fulfilment of his/her obligations under the GDPR, in particular in terms of informing the delegates concerned and respecting their other rights as defined by the European Regulation. Otherwise, the re-user would in particular be exposed to the penalties provided for in the GDPR.

The Data Protection Officer (DPO) as a Service market is experiencing robust growth, driven by increasing data privacy regulations like GDPR and CCPA, and the rising complexity of managing data compliance across diverse geographical locations and business operations. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by a growing awareness of data breaches and associated financial and reputational risks, prompting organizations, particularly SMEs lacking internal expertise, to outsource DPO responsibilities. Key market drivers include the escalating volume of sensitive data, the expanding scope of data protection laws, and the increasing demand for specialized DPO services, such as data mapping, breach response planning, and employee training. Several trends are shaping the market landscape. The increasing adoption of cloud-based DPO solutions enhances accessibility and scalability. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) technologies within DPO services promises to improve efficiency and accuracy in data protection activities. However, challenges remain, including concerns around data security and confidentiality when outsourcing sensitive information and the variability in service offerings across providers. Despite these restraints, the ongoing expansion of data privacy legislation globally, coupled with the escalating demand for specialized expertise, ensures sustained market expansion throughout the forecast period. Major players like Deloitte, PwC, and KPMG are leveraging their existing consulting expertise to gain a significant foothold, while specialized DPO-as-a-service providers are focusing on niche solutions and innovative technological integrations to stand out.

The timeline shows the share of companies with DPO (Data Protection Officer) in Italy in 2018, broken down by industrial sector. As the graph highlights, the sectors where Data Protection Officers were more present were the banks and the insurance sectors.

Tandogan/dpo-training-data dataset hosted on Hugging Face and contributed by the HF Datasets community

The timeline shows the share of companies with DPO (Data Protection Officer) in Italy from 2017 to 2019. As the graph highlights, the company with a DPO increased really significantly. On the other hand, the number of companies totally disinterested in the figure of the data protection officer decreased from 15 to 10 percent.

StepControlled/DPO-Data-Mistral-Ours dataset hosted on Hugging Face and contributed by the HF Datasets community

chengpingan/LLM-QE-DPO-Training-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

List of programmes funded by the DPO in promoting the adoption of digital technology among the elderly - over the years, the DPO has been striving to implement various programmes to encourage elderly using digital technology to improve their quality of life and stay connected to the community and family.

Citation

@misc{zhang2024chemllm, title={ChemLLM: A Chemical Large Language Model}, author={Di Zhang and Wei Liu and Qian Tan and Jingdan Chen and Hang Yan and Yuliang Yan and Jiatong Li and Weiran Huang and Xiangyu Yue and Dongzhan Zhou and Shufei Zhang and Mao Su and Hansen Zhong and Yuqiang Li and Wanli Ouyang}, year={2024}, eprint={2402.06852}, archivePrefix={arXiv}, primaryClass={cs.AI} }

Educational roles need to acquire digital skills and competencies exposed in DigCompEdu regarding data literacy and responsible use of digital competence. With this tool, schools can analyze their digital skills and data literacy competencies that help protect the privacy and security of students' data, as map their data academic management processes to detect gaps and foster data treatment policy updates. Questionnaire available in languages: English, Croatian, Portuguese, Slovenian, and Spanish.

This page, "DPO", is part of the NIST Chemistry WebBook. This site and its contents are part of the NIST Standard Reference Data Program.

IĮ "DPO" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

MB "Dpo" financial data: profit, annual turnover, paid taxes, sales revenue, equity, assets (long-term and short-term), profitability indicators.

Ultrafeedback Binarized

Predicting Binary Preferences with SFT, PPO and DPO

By Huggingface Hub [source]

About this dataset

This dataset contains data for ultra-fine-grained binary preference learning tasks. It features three distinct datasets - SFT, PPO, and DPO. These datasets provide rich insights into the user preferences via prompts, chosen and rejected messages, as well as scores assigned to each option. This is a great dataset to perform analysis on regarding user sentiment towards different input prompts and which responses they find more desirable or satisfying. Analyzing this data can offer deeper understanding of how people think in order to improve many applications that rely on artificial intelligence such as recommendation systems or automated customer service programs. By delving into this data we are able to gain a better understanding of the human mind with respect to decision making processes thus allowing us to develop more interpretable models in machine learning that operate closer from our own logic

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How to use the dataset

This dataset can be used to train and evaluate models for ultra-fine-grained binary preference learning tasks. The data is organized into three files: SFT, PPO, and DPO. Each file contains a series of prompts, chosen and rejected messages, and scores for each option. With this data, you can train a model that can predict user preferences consistently and accurately across multiple settings.

Here are the steps to work with this dataset: - Read through the prompts in each file and understand what the task is asking of the user. - Review both the chosen and rejected messages based on their accompanying scores to understand how they are influencing or being influenced by other factors such as emotion or sentiment. - Using your understanding of the task at hand from 1 & 2), create a model that accurately predicts user preference for any pair of options given in an ultra-fine grained binary preference learning task (SFT, PPO or DPO).
- Validate your model against other predictions using unseen data sets from all three files (SFT, PPO & DPO). This will help you determine whether your model accurately predicts user preferences within different contexts.

With these steps you should have an understanding of how best to use this dataset in order to build models which reliably predict how users will respond when presented with a choice between two options in an ultra-fine grained binary preference learning scenario!

Research Ideas

• Training a model or algorithm based on machine learning and natural language processing methods to determine user preferences between ultra-fine-grained options.
• Developing a supervised learning algorithm that uses the information from the prompt, chosen option, rejected option, message and score variables to identify factors that influence user preference selection for ultra-fine-grained tasks.
• Utilizing reinforcement learning agents such as PPO (Proximal Policy Optimization) or DPO (Deep Deterministic Policy Gradients) to create policies for effectively selecting between ultra-fine-grained options in different domains, via interactive experiments with real user data collected from this dataset

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: test_sft.csv | Column name | Description | |:-------------------|:------------------------------------------------------| | prompt | The prompt that was given to the user. (String) | | chosen | The message that the user chose. (String) | | rejected | The message that the user rejected. (String) | | messages | The messages that were presented to the user. (List) | | score_chosen | The score assigned to the chosen message. (Integer) | | score_rejected | The score assigned to the rejected message. (Integer) |

File: train_sft.csv | Column name | Description ...

Market Analysis for Data Protection Services The global data protection services market is expected to reach $128,110 million by 2033, growing at a CAGR of 18.0% during the forecast period. The increasing adoption of digital technologies, the rise of remote work, and the stringent regulatory landscape are driving the demand for these services. The market is segmented into services (Designated Data Protection Officer (DPO), Outsourced Data Subject Access (SAR) Services), applications (SMEs, large enterprises), and regions (North America, Europe, Asia Pacific, Middle East & Africa, South America). Leading companies in the market include EY, IBM, PwC, Deloitte, and Data Privacy and Security Services Ltd. The growth of the data protection services market is primarily driven by the increasing data breaches and cyber threats and the adoption of cloud-based services and the Internet of Things (IoT). The increasing awareness of data privacy and security regulations is also contributing to the market's growth. The market is expected to witness significant growth in emerging economies as governments and businesses focus on implementing data protection and privacy regulations. The adoption of artificial intelligence (AI) and machine learning (ML) technologies is expected to further enhance the efficiency and effectiveness of data protection services.

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The DPO as a Service market is rapidly evolving, emerging as a vital solution for organizations navigating the complexities of data protection and privacy compliance. As businesses continue to grapple with stringent regulations like the GDPR and CCPA, outsourcing the role of a Data Protection Officer (DPO) has gaine

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The dataset provides a consolidated list of new datasets planned to be open up according to Consolidated Annual Open Data Plans of Bureaux and Departments.