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Intel Orca Dialogue Pairs

Orca style for preference training (Intel's DPO dataset)

By Huggingface Hub [source]

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About this dataset

> The Intel/Orca/DPO Dialogue Pairs dataset is a unique resource for Natural language processing (NLP) research, combining AI and human conversations collected from online sources. This dataset is invaluable for exploring how human conversations can inform the development of conversational AI models. With columns such as System and Question extracted from chat logs, this dataset can help researchers understand more about how to better connect people with technology using meaningful dialogue. Furthermore, the data also includes columns for ChatGPT and Llama2–13b-Chat, two of the most widely used conversational AI models. By leveraging this data set, researchers have an exceptional opportunity to explore conversational techniques that enable humans and machines to communicate in natural languages

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How to use the dataset

> This guide will provide an overview of how to use the Intel/Orca/DPO Dialogue Pairs dataset efficiently for human-centric natural language processing research. > > ##### Step 1: Understand the dataset > The Intel/Orca/DPO Dialogue Pairs dataset is composed of two main columns: System and Question. The System column contains responses from AI systems, and the Question column contains questions asked by humans. Additionally, this dataset also contains columns for ChatGPT and Llama2–13b-Chat, two models used in developing conversational AI systems. > > ##### Step 2: Prepare your environment > Before getting started with analyzing data from this dataset, you should first prepare your environment accordingly. Make sure that any necessary libraries or services are installed on your machine before attempting to work with the data from this dataset in order to avoid potential issues or errors during usage. > > ##### Step 3: Access the data > In order to access and start working with the data contained in this Dataset, you can either download it directly via a Kaggle account or alternatively access it through one of its REST Endpoints if available on other services (i.e Databricks). > > ##### Step 4: Exploring & Analyzing the Data > > ##### Step 5 : Reporting Results > Lastly ,once explorations and analyses have been completed its highly important that results are reported accurately especially when dealing with ethical datasets such as dialogue pairs since consequences could be dire if misinformation is disseminated .Reporting results should usually involve standard relevant indicators being declared while taking care conducting appropriate statistical tests ruling out incorrect anomalous outcomes

Research Ideas

> - Developing and improving natural language processing algorithms for AI-human conversation. > - Building user-friendly chatbots that are better at recognizing and understanding human intent by training the model using this dataset. > - Designing recommendation systems to predict user questions and generate more accurate responses based on previous conversations in the 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: train.csv

Column name	Description
system	Contains the AI system's response to the user's question. (Text)
chatgpt	Contains the ChatGPT model's response to the user's question. (Text)
llama2-13b-chat	Contains the Llama2-13b-Chat model's response to the user's question. (Text)

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit Huggingface Hub.