LongAlpaca 16K-Length by Kaggle | Other

About this Dataset

LongAlpaca 16K-Length

Investigating Natural Language Processing Performance

By Huggingface Hub [source]

About this dataset

This dataset offers a comprehensive analysis of 16K-Length Yukang/LongAlpaca text instructions. It provides users with the information needed to effectively understand and utilize these instructions in order to maximize their data analytical capabilities. Each record contains an output and file fields with detailed descriptions of what is expected from these instructions. By engaging this dataset, users can gain valuable knowledge about the structure, syntax, strengths, weaknesses, and application possibilities of 16K-Length Yukang/LongAlpaca text instructions. Dive into this data now and unlock your inner data explorer!

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How to use the dataset

This dataset contains a collection of 16K-Length Yukang/LongAlpaca text instructions that can be used to develop various models for text analysis. Now let's learn how to use this dataset efficiently.

First, you should consider the context in which you'd like to use this data for text analysis applications. Understand what type of tasks or projects it is best suited for and determine what kind of model you would need to build from the utility sets provided in the dataset. When looking at each example record in 'train.csv', take note of features like 'text' and 'output', as they provide valuable information about the data points, and ultimately how these could be leveraged within your project or task.

Next, create an outline for your project or task using the data point utility sets provided in the dataset (e.g., output) as reference points so that you can accurately structure the necessary components needed for a successful outcome. If necessary, create additional extractable files from specific columns such as language labels, date/time stamps etc.. Leverage those extracted features by testing them out with other input parameters such as different file types and formats sent into analytics tools via APIs etc.. Test any newly created artefacts with estimation methods like cross-validation accuracy scores before running them through production pipelines so that anomalies can be resolved before deployment!
At last but not least visualise your predictive results through powerful dashboard tools such as Tableau which will help illustrate key metrics from insights easily consumable by non technical users if needed!

Research Ideas

Analyzing the sentiment of text written using the 16K-Length Yukang/LongAlpaca instruction, such as identifying positive or negative phrases.

Comparing different instruction files across various topics and use cases in order to determine which instructions yield better understanding and accuracy for readers.

Training machine learning models to automatically generate new instructions based on a given input from the dataset so that users can quickly create customized solutions for their needs

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
output	The output of the instruction. (String)
file	The file associated with the instruction. (String)

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.

Tables

Train

@kaggle.thedevastator_16k_length_yukang_text_instructions.train

119.5 MB
6284 rows
3 columns


CREATE TABLE train (
  "output" VARCHAR,
  "file" VARCHAR,
  "instruction" VARCHAR
);