CoEdIT
Enhancing AI Text Editing Through 69,000 Instances
By Huggingface Hub [source]
About this dataset
This dataset provides 69,000 instances of natural language processing (NLP) editing tasks to help researchers develop more effective AI text-editing models. Compiled into a convenient JSON format, this collection offers easy access so that researchers have the tools they need to create groundbreaking AI models that efficiently and effectively redefine natural language processing. This is your chance to be at the forefront of NLP technology and make history through innovative AI capabilities. So join in and unlock a world of possibilities with CoEdIT's Text Editing Dataset!
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How to use the dataset
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Familiarize yourself with the format of the dataset by taking a look at the columns: task, src, tgt. You’ll see that each row in this dataset contains a specific NLP editing task as well as source text (src) and target text (tgt) which displays what should result from that editing task.
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Import the JSON file of this dataset into your machine learning environment or analyses software toolbox of choice. Some popular options include Python's Pandas library and BigQuery on Google Cloud Platforms for larger datasets like this one oryoou can also import them into Excel Toolboxes .
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Once you've imported the data into your chosen program, you can now start exploring! Take a look around at various rows to get an idea of how different types of edits need to be made on source text in order to produce target text successfully meeting given criteria depending on needs/ tasks come together; Make sure you read any documents associated with each column helps understand better context before beginning your analysis or coding part
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Test out coding solutions which process different types and scales of edits - if understanding how punctuation impacts sentence similarity measures gives key insight into meaning being conveyed then develop code accordingly ,playing around with different methods utilizing common ML/NLP algorithms & libraries like NLTK , etc
5 Finally – now that have tested conceptual ideas begin work creating efficient & effective AI-powered models system using training data specifically catered towards given tasks at hand; Evaluate performance with validation & test datasets prior getting production ready
Research Ideas
- Automated Grammar Checking Solutions: This dataset can be used to train machine learning models to detect grammatical errors and suggest proper corrections.
- Text Summarization: Using this dataset, researchers can create AI-powered summarization algorithms that summarize long-form passages into shorter summaries while preserving accuracy and readability
- Natural Language Generation: This dataset could be used to develop AI solutions that generate accurately formatted natural language sentences when given a prompt or some other form of input
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: validation.csv
| Column name |
Description |
| Task |
This column describes the task that the dataset is intended to be used for. (String) |
| src |
This column contains the source text input. (String) |
| tgt |
This column contains the target text output. (String) |
File: train.csv
| Column name |
Description |
| Task |
This column describes the task that the dataset is intended to be used for. (String) |
| src |
This column contains the source text input. (String) |
| tgt |
This column contains the target text output. (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.