Metadata-Version: 2.1
Name: folktexts
Version: 0.0.4
Summary: A benchmark on LLM calibration to human populations.
Author: Andre Cruz, Ricardo Dominguez-Olmedo, Celestine Mendler-Dunner, Moritz Hardt
License: MIT License
        
        Copyright (c) 2024 Social Foundations of Computation, at MPI-IS
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
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Project-URL: homepage, https://github.com/socialfoundations/folktexts
Project-URL: documentation, https://socialfoundations.github.io/folktexts/
Project-URL: repository, https://github.com/socialfoundations/folktexts
Keywords: language-model,risk-estimation,benchmark,machine-learning
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: folktables~=0.0.12
Requires-Dist: numpy
Requires-Dist: pandas
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Requires-Dist: accelerate
Requires-Dist: transformers
Requires-Dist: torch
Requires-Dist: sentencepiece
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Requires-Dist: netcal
Requires-Dist: cloudpickle
Requires-Dist: matplotlib
Requires-Dist: seaborn
Provides-Extra: tests
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Provides-Extra: cli
Requires-Dist: htcondor; extra == "cli"

# :book: folktexts   <!-- omit in toc -->
> :construction: Package under construction

![Tests status](https://github.com/socialfoundations/folktexts/actions/workflows/python-tests.yml/badge.svg)
![PyPI status](https://github.com/socialfoundations/folktexts/actions/workflows/python-publish.yml/badge.svg)
![Documentation status](https://github.com/socialfoundations/folktexts/actions/workflows/python-docs.yml/badge.svg)
![PyPI version](https://badgen.net/pypi/v/folktexts)
![OSI license](https://badgen.net/pypi/license/folktexts)
![Python compatibility](https://badgen.net/pypi/python/folktexts)

Repo to host the `folktexts` project.

Package documentation can be found [here](https://socialfoundations.github.io/folktexts/)!

**Table of contents:**
- [Installing](#installing)
- [Basic setup](#basic-setup)
- [Usage](#usage)
- [License and terms of use](#license-and-terms-of-use)


## Installing

Install package from [PyPI](https://pypi.org/project/folktexts/):

```
pip install folktexts
```

## Basic setup

1. Create condo environment

```
$ conda create -n folktexts python=3.11      
$ conda activate folktexts
```

2. Install folktexts package

```
$ pip install folktexts
```

3. Create models dataset and results folder

```
mkdir results
mkdir models
mkdir datasets
```

3. Download transformers models into models folder

```
python -m folktexts.cli.download_models --model "google/gemma-2b" --save-dir models
```

4. Run benchmark

```
python -m folktexts.cli.run_acs_benchmark --results-dir results --data-dir datasets --acs-task-name "ACSIncome" --model models/google--gemma-2b [other-optional-flags]
```

Run `python -m folktexts.cli.run_acs_benchmark --help` to get a list of all
available benchmark flags.


## Usage

```py
from folktexts.acs import ACSDataset, ACSTaskMetadata
acs_task_name = "ACSIncome"

# Create an object that classifies data using an LLM
clf = LLMClassifier(
    model=model,
    tokenizer=tokenizer,
    task=ACSTaskMetadata.get_task(acs_task_name),
)

# Use a dataset or feed in your own data
dataset = ACSDataset(acs_task_name)

# Get risk score predictions out of the model
y_scores = clf.predict_proba(dataset)

# Optionally, can fit the threshold based on a small portion of the data
clf.fit(dataset[0:100])

# ...in order to get more accurate binary predictions
clf.predict(dataset)

# Compute a variety of evaluation metrics on calibration and accuracy
from folktexts.benchmark import CalibrationBenchmark
benchmark_results = CalibrationBenchmark(clf, dataset, results_dir="results").run()
```


## License and terms of use

Code licensed under the [MIT license](LICENSE).

The American Community Survey (ACS) Public Use Microdata Sample (PUMS) is
governed by the U.S. Census Bureau [terms of service](https://www.census.gov/data/developers/about/terms-of-service.html).
