nifty-rl / README.md
raeidsaqur's picture
Update README.md
d197b31 verified
metadata
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

RH The News-Informed Financial Trend Yield (NIFTY) Dataset.

The News-Informed Financial Trend Yield (NIFTY) Dataset. Details of the dataset, including data procurement and filtering can be found in the paper here: https://arxiv.org/abs/2405.09747.

πŸ“‹ Table of Contents

πŸ“– 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.

πŸ“ Citing

If you use the NIFTY Financial dataset in your work, please consider citing our paper:

@article{raeidsaqur2024NiftyRL,
    title        = {NIFTY-RL: Financial News Headlines Dataset for LLM Alignment using Reinforcement Learning.},
    author       = {Raeid Saqur},
    year         = 2024,
    journal      = {ArXiv},
    url          = {https://arxiv.org/abs/2024.5599314}
}

πŸ™ Acknowledgements

The authors acknowledge and thank the generous computing provided by the Vector Institute, Toronto.