metadata
language:
- en
size_categories:
- 100K<n<1M
Complete-it
Dataset Summary
The Complete-it dataset is designed for training and evaluating text auto-completion models. It provides structured prompt–completion pairs that can be used to fine-tune language models for predictive typing, code completion, or general text continuation tasks.
This dataset is intended for researchers, developers, and practitioners who want to explore completion-based modeling in natural language processing.
Supported Tasks and Benchmarks
- Text Auto-Completion: Predicting the next sequence of tokens given a partial input.
- Language Modeling: Training models to generate coherent continuations of text.
- Instruction Tuning (optional): Can be adapted for instruction–response style fine-tuning if reformatted.
Languages
- The dataset is primarily in English.
- It can be extended or adapted for multilingual completion tasks.
Dataset Structure
- Input: A partial text sequence (prompt).
- Output: The expected continuation (completion).
Example:
{
"prompt": "The quick brown fox",
"completion": " jumps over the lazy dog."
}
Data Splits
- train: Main split for model training.
- validation: For hyperparameter tuning and evaluation.
- test: For final benchmarking.
(Adjust based on your actual splits — if you only have one file, note that here.)
Usage
You can load the dataset directly with the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("Parveshiiii/Complete-it")
Intended Use
- Research on auto-completion and predictive text.
- Fine-tuning small to medium language models for lightweight completion tasks.
- Educational purposes in demonstrating dataset formatting for completion models.
Limitations
- The dataset may not cover all domains or writing styles.
- Quality of completions depends on the diversity of the source material.
- Not intended for safety-critical applications without further filtering and evaluation.
Citation
If you use this dataset, please cite it as:
@dataset{Parveshiiii_Complete-it,
title = {Complete-it},
author = {Parvesh Rawal},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/Parveshiiii/Complete-it}
}