---
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](https://github.com/AI4Finance-Foundation/FinGPT).
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](https://flower.ai/benchmarks/llm-leaderboard/)).


### Direct Use in FL
This dataset can be used in FL settings. We recommend using [Flower Datasets](https://flower.ai/docs/datasets/) (flwr-datasets) and [Flower](https://flower.ai/docs/framework/) (flwr).

To partition the dataset, do the following. 
1. Install the package.
```bash
pip install flwr-datasets
```
2. Use the HF Dataset under the hood in Flower Datasets.
```python
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](https://github.com/AI4Finance-Foundation/FinGPT).

#### 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](https://flower.ai/).