semvec/pypi-packages huggingface dataset:
whisperjf 0.1.3
A compatibility fix to for whisperx for use with gogadget
License: MIT
Requires Python: <3.13,>=3.10

A compatibility fix to allow [whisperx](https://github.com/m-bain/whisperX) to work with [gogadget](https://github.com/jonathanfox5/gogadget).

Pypi link: https://pypi.org/project/whisperjf/
safenudge 0.1.0
A Python library with the implementation for the algorithms used in 'Safeguarding large language models in real-time with tunable safety-performance trade-offs', by J. Fonseca, A. Bell and J. Stoyanovich.
License: MIT

# SafeNudge

A Python library with the implementation for the algorithms used in
"Safeguarding large language models in real-time with tunable safety-performance
trade-offs", by J. Fonseca, A. Bell and J. Stoyanovich.

`CTG` provides methods to guide model responses based on various criteria,
helping ensure safe, high-quality, and controllable text generation.

## Implemented methods

- **Controlled Text Generation (CTG)**: The SafeNudge implementation.
- **WildGuard Integration (WildguardCTG)**: SafeNudge using the WildGuard classifier
- **Token Masking (TokenMaskingCTG)**: c-FUDGE, as described in the paper

## Installation

A Python distribution of version >= 3.12 is required to run this project.
Earlier Python versions might work in most cases, but they were never tested.


### From Source

```bash
# Clone the repository
git clone https://github.com/joaopfonseca/SafeNudge.git
cd Output-Steering

# Install in development mode
pip install -e .
```

### Using pip

```bash
pip install git+https://github.com/joaopfonseca/SafeNudge.git
```

## Examples

Check the [notebooks directory](https://github.com/joaopfonseca/SafeNudge/tree/main/notebooks) 
for some examples Andrew and I developed while working on SafeNudge and setting
up the experiments!

## Project Structure

```
Output-Steering/
├── ctg/                    # Core library code
├── experiments/            # Experimental code and evaluation
└── notebooks/              # Jupyter notebooks with examples
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Citation

If you use this code in your research, please cite:

```
@article{fonseca2025safeguarding,
  title={Safeguarding large language models in real-time with tunable safety-performance trade-offs},
  author={Fonseca, Joao and Bell, Andrew and Stoyanovich, Julia},
  journal={arXiv preprint arXiv:2501.02018},
  year={2025}
}
```
pycrea 0.1.3
A Python package to parse and manage CREA word vectors. Learn more about this project on GitHub: https://github.com/WiredBrains-Lab/
License: MIT

# CREA-Vectors

This is a package to access CREA vectors via pycrea. Various data collection methods are also included.
Future updates will allow users to add their collected vectors to the main set.

## How to run the code
1. pip install pycrea in your terminal
2. In python import the library
   - from crea import CREA
   - c = CREA() # Automatically loads main dictionary or
   - c = CREA('path_to_file.json') # Load custom json file
      * c_cols = CREA(col_names = True) loads feature names 
   ### Usage
   - all_vectors = c.get_all_vectors()
   - vector = c.get_vector('word1')
   - vectors = c.get_vectors(['word1', 'word2'])
   - vectors_and_cols = c_cols.select_cols(['word1', 'word2'], [feature_1, feature_2])
   - similarity = c.cosine_similarity('word1', 'word2')
   - n_similar = c.top_n_similar('word1', 5) # defaults to 5

   
## PsychoPy Data Collection
- Results are saved as a JSON file named 'results.json'
- Currently it is overwritten each time
- JSON file has the structure {'target_word': {'Rating Category 1': rating_1, ... 'Rating Category n': rating_n}}
- CREA.get_raw('results.json') gets the new raw data from results.json and adds a new row to raw results.csv
- CREA.calculate_averages('results.csv') calculates the average of results.csv and returns a DataFrame of the averages