metadata

task_categories:
  - text-classification
language:
  - en
tags:
  - finance
dataset_info:
  features:
    - name: input
      dtype: string
    - name: output
      dtype: string
    - name: instruction
      dtype: string
  splits:
    - name: train
      num_bytes: 18860715
      num_examples: 76772
  download_size: 6417302
  dataset_size: 18860715
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

Dataset Card for fingpt-sentiment-train

This dataset originates from the FinGPT repository. The fingpt-sentiment-train dataset is specifically used for financial sentiment analysis model training.

Dataset Details

Dataset Description

Each sample is comprised of three columns: instruction, input and output.

Language(s): English

Dataset Sources

The code from the original repository was adopted to post it here.

Repository: https://github.com/AI4Finance-Foundation/FinGPT

Uses

This dataset is primarily used for models training of financial sentiment analysis. It can also be utilized in Federated Learning settings by partitioning the data into multiple shards (e.g. FlowerTune LLM Leaderboard).

Direct Use in FL

This dataset can be used in FL settings. We recommend using Flower Datasets (flwr-datasets) and Flower (flwr).

To partition the dataset, do the following.

Install the package.

pip install flwr-datasets

Use the HF Dataset under the hood in Flower Datasets.

from flwr_datasets import FederatedDataset
from flwr_datasets.partitioner import IidPartitioner
fds = FederatedDataset(
    dataset="flwrlabs/fingpt-sentiment-train",
    partitioners={"train": IidPartitioner(num_partitions=50)}
)
partition = fds.load_partition(partition_id=0)

Dataset Structure

The dataset contains only train split. Each sample is comprised of columns:

instruction: str - description of financial sentiment task the model should perform.
input: str - text of financial news.
output: str - answer of the financial sentiment analysis, e.g., negative/neutral/positive.

Dataset Creation

Curation Rationale

This dataset was created as a part of the FinGPT repository.

Data Collection and Processing

For the preprocessing details, please refer to the source code.

Citation

When working on the this dataset, please cite the original paper. If you're using this dataset with Flower Datasets, you can cite Flower.

BibTeX:

@article{yang2023fingpt,
  title={FinGPT: Open-Source Financial Large Language Models},
  author={Yang, Hongyang and Liu, Xiao-Yang and Wang, Christina Dan},
  journal={FinLLM Symposium at IJCAI 2023},
  year={2023}
}

@article{DBLP:journals/corr/abs-2007-14390,
  author       = {Daniel J. Beutel and
                  Taner Topal and
                  Akhil Mathur and
                  Xinchi Qiu and
                  Titouan Parcollet and
                  Nicholas D. Lane},
  title        = {Flower: {A} Friendly Federated Learning Research Framework},
  journal      = {CoRR},
  volume       = {abs/2007.14390},
  year         = {2020},
  url          = {https://arxiv.org/abs/2007.14390},
  eprinttype    = {arXiv},
  eprint       = {2007.14390},
  timestamp    = {Mon, 03 Aug 2020 14:32:13 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Dataset Card Contact

In case of any doubts, please contact Flower Labs.