Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
finance
File size: 3,741 Bytes
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---
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/). |