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SlimOrca

OpenOrca (Reproduction of Orca) - Cleverly Sampled

By Huggingface Hub [source]

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About this dataset

This dataset offers you the power to achieve maximum performance with minimal data. Curated from the OpenOrca dataset, this dataset contains around 500K GPT-4 completions, with an additional pass done using GPT-4 to remove any erroneous answers that may have been identified by FLAN's human annotations. This means that you get robust and high-performance GPT-4 completions with reduced compute requirements and still maintain comparable performance to larger datasets in terms of accuracy. Use this dataset to unlock the potential of your machine learning models and bring unprecedented efficiency into solving complex language problems

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How to use the dataset

How to Use Open-Orca SlimOrca GPT-4 Completions

• Download the dataset: The dataset can be downloaded as a CSV file directly from the Kaggle website.
• Examine the data columns: The data contains two primary columns - conversations and conversations_cleaned, which provide cleaned and uncooked versions of conversational dialogs that have been used to train a GPT-4 model for robust completions with reduced compute requirements.
• Slice and dice the data: The goal is to identify meaningful patterns or trends in user conversation behavior based on their responses to processing particular contexts or topics using this training set as input data for more efficient analysis using natural language processing techniques, such as clustering or topic modeling algorithms. Furthermore, these contextualized prompt-response insights can then be used directly in creating interactive applications with domain-specific conversations tailored towards desirable user experience outcomes.
• Model robust completions with reduced compute requirements: To start modeling with this dataset, select an appropriate learning algorithm according to your own understanding of what kind of output should be generated by the model when it completes a conversational prompt — this will largely determine what type of effort is required in combination with rigorous testing/evaluating models performance against new unseen inputs for continual refinement possible gains in accuracy/performance using open source libraries such as Tensorflow/PyTorch for building your own end-toend framework orGoogle Cloud Platform services like AI Platform/Cloud AutoML Vision etc., which offer an automated means of optimizing neural networks while staying cloud natively agnostic..

5 Final Notes on Using this Dataset: It's important that you carefully select all forms of input into your model if are aiming manual tuning capabilities ‒ so make sure that only useful information canbe utilized optimally within reasonable bounds when preparing it such datasets embedding semantics which lead inference decisions reliably intuitive results needed our specific use cases depending size complexity evaluates accordingly either continuous preprocess latent space optimization (e g bayesian parameter search) batching ensembling existing optimization methodologies achieved desired scalability intelligence magnification wherever applicable contexture technologies development ✨

Research Ideas

• Using this dataset to develop intelligent virtual assistants that can better understand natural language queries and provide more relevant responses.
• Using the dataset for automatic text generation models, which can generate contextually accurate response for open-ended questions.
• Training generative chatbots that can engage in meaningful and realistic conversations with humans, using the conversational data as a starting point to build up systems capable of conversational AI tasks such as question answering or dialogue understanding

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

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.