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What is the Open Orca Dataset?

Welcome, researchers and developers, to a brand new world of enriched data - the Open Orca Dataset. Composed of an extensive collection of augmented FLAN data, this dataset is a treasure trove for everyone engaged in Natural Language Processing (NLP). Aligned meticulously with the distributions featured in the Orca paper, this dataset has proven instrumental in generating high-performing model checkpoints and stands as a valuable asset for NLP enthusiasts. This blog post aims to delve deeper into the structure and applications of this dataset, shedding light on its creation, contributors, and use cases.



Who Contributed to the Open Orca Dataset?


Assembled by the dedicated hands and brilliant minds of various contributors, the Open Orca Dataset wouldn't exist without the concerted efforts of several individuals and organizations. Special recognition goes to Teknium, Caseus, Eric Hartford, NanoBit, Pankaj, Winddude, and Rohan, for their dedication. The http://AlignmentLab.ai contributors include Autometa, Entropi, AtlasUnified, NeverendingToast, lightningRalf, NanoBit, and Caseus. We also owe much gratitude to TheBloke, the backbone of our community, and NanoBit and Caseus, the creators of Axolotl, for their expertise in developing and training models like manticore and minotaur.


What Tasks Does the Open Orca Dataset Support?


Supporting a myriad of tasks, including language modeling, text generation, and text augmentation, the Open Orca Dataset provides a robust foundation for a wide array of NLP operations. It has played a critical role in generating several high-performing model checkpoints that have shown extraordinary performance during our unit testing. Information on leaderboards will be provided as they become available.


What Languages Does the Open Orca Dataset Cover?


At present, the Open Orca Dataset primarily encompasses data in English, offering a comprehensive range of augmented text data to empower researchers and developers in the NLP domain.


How is the Open Orca Dataset Structured?


The Open Orca Dataset is structured in a tabular format with data instances, fields, and splits.


Each data instance represents entries from the FLAN collection, which have been augmented by submitting the listed question to either GPT-4 or GPT-3.5. The response from the AI is then entered into the response field.


Data fields include 'id', a unique identifier; 'system_prompt', representing the System Prompt presented to the GPT-3.5 or GPT-4 API for the datapoint; 'question', a question entry as provided by the FLAN Collection; and 'response', a response to that question received from a query to either GPT-3.5 or GPT-4.


The data split is currently at 17.6% test, providing a balance of data for training and evaluation purposes.


Why was the Open Orca Dataset Created?


The Open Orca Dataset was curated with the primary intent of providing an enhancement of the core FLAN Collection data. It leans on the detailed step-by-step reasoning capabilities of GPT-3.5 and GPT-4. The result is a "reasoning trace" augmentation that has shown impressive results, allowing a LLaMA-13B model trained with this data to rival or even surpass GPT-3.5 on broad sets of hard reasoning tasks which models below 100B parameters had previously performed significantly worse on.


What is the Source of Data for the Open Orca Dataset?


The data for the Open Orca Dataset was generated using techniques aligned with the distributions outlined in the Orca paper. The pre-generated FLAN Collection datasets hosted on HuggingFace under conceptofmind, e.g., conceptofmind/flan2021, were used. However, due to certain discrepancies and limitations in the available data, the dataset currently represents partial completion of the full intended set, and the work of completing it is still ongoing.


How Can the Open Orca Dataset be Used?


With a vast array of use cases, the Open Orca Dataset serves as an essential resource for tasks related to language understanding, natural language processing, machine learning model training, and model performance evaluation. It is recommended, though, to regularly check for updates and improvements and to use the data in accordance with the guidelines and recommendations outlined in the Orca paper.


For anyone looking to get started with the Open Orca Dataset, it is designed to be seamlessly loaded via the Hugging Face datasets library. Due to the large size of the files, we recommend using streaming. Regular updates and data generation progress can be monitored through the OpenOrca repository on Hugging Face.


The Open Orca Dataset, with its richness and scope, is indeed a major step forward in the world of NLP. It presents a multitude of opportunities for researchers and developers, making it a game-changer in language modeling, text generation, and augmentation. The dataset's ongoing completion promises an even brighter future, and we eagerly look forward to its full realization.


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