license: mit
tags:
- nifty
- stock-movement
- news-and-events
- RLMF
task_categories:
- multiple-choice
- time-series-forecasting
- document-question-answering
task_ids:
- topic-classification
- semantic-similarity-classification
- multiple-choice-qa
- univariate-time-series-forecasting
- document-question-answering
language:
- en
pretty_name: nifty-rl
size_categories:
- 1K<n<100k
configs:
- config_name: nifty-rl
data_files:
- split: train
path: train.jsonl
- split: test
path: test.jsonl
- split: valid
path: valid.jsonl
default: true
The News-Informed Financial Trend Yield (NIFTY) Dataset.
The News-Informed Financial Trend Yield (NIFTY) Dataset.
📋 Table of Contents
📖 Usage
Downloading and using this dataset should be straight-forward following the Huggingface datasets framework.
Downloading the dataset
The NIFTY dataset is available on huggingface here and can be downloaded with the following python snipped:
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("raeidsaqur/nifty-rl")
Dataset structure
The dataset is split into 3 partition, train, valid and test and each partition is a jsonl file where a single row has the following keys.
['prompt', 'chosen', 'rejected', 'chosen_label', 'chosen_value']
Currently, the dataset has 2111 examples in total, the dates randing from 2010-01-06 to 2020-09-21.
✍️ Contributing
We welcome contributions to this repository (noticed a typo? a bug?). To propose a change:
git clone https://huggingface.co/datasets/raeidsaqur/nifty-rl
cd nifty-rl
git checkout -b my-branch
pip install -r requirements.txt
pip install -e .
Once your changes are made, make sure to lint and format the code (addressing any warnings or errors):
isort .
black .
flake8 .
Then, submit your change as a pull request.
📝 Citing
If you use the NIFTY Financial dataset in your work, please consider citing our paper:
@article{raeidsaqur2024Nifty,
title = {NIFTY Financial News Headlines Dataset},
author = {Raeid Saqur},
year = 2024,
journal = {ArXiv},
url = {https://arxiv.org/abs/2024.5599314}
}
🙏 Acknowledgements
The authors acknowledge and thank the generous computing provided by the Vector Institute, Toronto.