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Financial datasets for LLMs 🧪
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| 2026-11-04 | 438 |
Today
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Momentum
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# Financial Datasets 🧪
Financial Datasets is an open-source Python library
that lets you create question & answer financial datasets
using Large Language Models (LLMs). With this library,
you can easily generate realistic financial datasets from a 10-K,
10-Q, PDF, and other financial texts.
[](https://twitter.com/virattt)
## Usage
**Example generated dataset:**
```json
[
{
"question": "What was Airbnb's revenue in 2023?",
"answer": "$9.9 billion",
"context": "In 2023, revenue increased by 18% to $9.9 billion compared to 2022, primarily due to a 14% increase in Nights and Experiences Booked of 54.5 million combined with higher average daily rates driving a 16% increase in Gross Booking Value of $10.0 billion."
},
{
"question": "By what percentage did Airbnb's net income increase in 2023 compared to the prior year?",
"answer": "153%",
"context": "Net income in 2023 increased by 153% to $4.8 billion, compared to the prior year, driven by our revenue growth, increased interest income, discipline in managing our cost structure, and the release of a portion of our valuation allowance on deferred tax assets of $2.9 billion."
}
]
```
**Example #1 - generate from any text**
Most flexible option. Generates dataset using a list of string `texts`. Colab code
example [here](https://colab.research.google.com/gist/virattt/f9b5a0ae82cc0caab57df5dedc2927c9/intro-financial-datasets.ipynb).
```python
from financial_datasets.generator import DatasetGenerator
# Your list of texts
texts = ...
# Create dataset generator
generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key")
# Generate dataset from texts
dataset = generator.generate_from_texts(
texts=texts,
max_questions=100,
)
```
**Example #2 - generate from PDF**
Generate a dataset using a PDF `url` only. Colab code
example [here](https://colab.research.google.com/gist/virattt/b04442ee7c6c0d0bb3c9371af2283a20/intro-financial-datasets.ipynb).
```python
from financial_datasets.generator import DatasetGenerator
# Create dataset generator
generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key")
# Generate dataset from PDF url
dataset = generator.generate_from_pdf(
url="https://www.berkshirehathaway.com/letters/2023ltr.pdf",
max_questions=100,
)
```
**Example #3 - generate from 10-K**
Generate a dataset using a `ticker` and `year`. Colab code
example [here](https://colab.research.google.com/gist/virattt/743872e143034987d20e6a6c7bb9d0a1/intro-financial-datasets.ipynb).
```python
from financial_datasets.generator import DatasetGenerator
# Create dataset generator
generator = DatasetGenerator(model="gpt-4-turbo", api_key="your-openai-key")
# Generate dataset from 10-K
dataset = generator.generate_from_10K(
ticker="AAPL",
year=2023,
max_questions=100,
item_names=["Item 1", "Item 7"], # optional - specify Item names to use
)
```
## Installation
### Using pip
You can install the Financial Datasets library using pip:
```
pip install financial-datasets
```
### Using Poetry
If you prefer to use Poetry for dependency management, you can add Financial Datasets to your project:
```
poetry add financial-datasets
```
### From the Repository
If you want to install the library directly from the repository, follow these steps:
1. Clone the repository:
```
git clone https://github.com/virattt/financial-datasets.git
```
2. Navigate to the project directory:
```
cd financial-datasets
```
3. Install the dependencies using Poetry:
```
poetry install
```
4. You can now use the library in your Python projects.
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements,
please open an issue or submit a pull request.
## License
This project is licensed under the [MIT License](link-to-license-file).
## Contributors
<a href="https://githubExcerpt of 4,135 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2cc79064d1ad787c, name:datasets, desc:datasets
matched fp:2cc79064d1ad787c, name:financial, desc:financial