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Alpaca Cleaned Instruction-Following Dataset
Data Science and Analytics
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About
Improving the ability of pretrained language models to interpret and act upon specific commands is the core objective of this resource. By providing over 52,000 expertly crafted instruction-demonstration pairs, it enables models to move beyond standard Natural Language Processing capabilities. The collection consists of data generated by the text-davinci-003 engine, which has undergone rigorous cleaning to remove errors and biases, ensuring a high-quality foundation for model comprehension and performance.
Columns
- instruction: Contains the specific guidance or command for the language model to follow.
- output: Represents the expected and correct response or action the model should produce.
- input: Provides additional context or data relevant to the instruction, though this field may be empty if no extra information is required.
Distribution
The data is delivered as a CSV file named
train.csv with a total size of 39.98 MB. It comprises approximately 51,800 valid records structured into three distinct columns. The file has a perfect usability score of 10.00, indicating high integrity with no missing values in the primary instruction and output fields. No future updates are planned for this specific version.Usage
This resource is perfectly suited for fine-tuning Large Language Models (LLMs) to enhance their instruction-following capabilities. It can be used to develop conversational AI, automate human-given tasks, or train robotic agents to understand natural language commands. Additionally, it serves as a valuable tool for creating systems that provide personalised feedback or for researching methods to better interpret instructions given by humans.
Coverage
The scope is focused on general English language instructions (BCP-47 en). While not bound by a specific geographic or temporal range, it reflects the linguistic patterns and knowledge base of the text-davinci-003 model. The demographic scope is broad, covering a vast array of topics from everyday events to complex general statements used to test common sense.
License
CC0: Public Domain
Who Can Use It
Machine learning engineers can utilise these pairs to refine the performance of pretrained models for specific niche applications. AI researchers can leverage the cleaned data to study instruction interpretation and response generation. Furthermore, developers building intelligent agents or automated assistants will find the structured demonstrations essential for creating reliable user interactions.
Dataset Name Suggestions
- Alpaca Cleaned Instruction-Following Dataset
- Expert-Crafted 52k Instruction-Demonstration Pairs
- NLP Fine-Tuning Registry for Model Comprehension
- Cleaned Alpaca LLM Training Corpus
- Refined Instruction-Output Linguistic Pairs
Attributes
Original Data Source: Alpaca Cleaned Instruction-Following Dataset
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Free
Download Dataset in CSV Format
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