semvec/pypi-packages huggingface dataset:
powerset-calibration 0.1.3
Companion package to the 'On the calibration of powerset speaker diarization models' paper published at Interspeech 2024.
Requires Python: >=3.8.1

# powerset_calibration

This package contains the code used to perform the experiments conducted in the paper '*On the calibration of powerset speaker diarization models*' published at Interspeech 2024.

- [ 📄 Browse the original paper repository ](https://github.com/FrenchKrab/IS2024-powerset-calibration?tab=readme-ov-file)
- [ 📘 Read the docs ](https://frenchkrab.github.io/powerset_calibration/)

This package builds upon the pyannote suite, and thus heavily depends on `pyannote.audio`, `pyannote.core` and `pyannote.database` functionalities.

# Installing

### 🐍 Using pip

```bash
pip install powerset_calibration
```

### 🏠 Using a local installation

```bash
git clone https://github.com/FrenchKrab/powerset_calibration
pip install -e powerset_calibration
```

# Usage

Most of the functionalities of the library are easy to access and just require you to plug in the right parameters.

## ⏯️ Notebook

To learn how to use this library, please refer to the notebooks which should give you 90% of the informations you need.

- Essential features
  - [A1_model_inference](notebooks/A1_model_inference.ipynb): Generate and evaluate an 'inference file' from your segmentation model
  - [A2_active_learning_protocol](notebooks/A2_active_learning_protocol.ipynb): Create subsets from an existing protocol using active learning-like criterions (e.g. select the 10% least confident data)
- Advanced usage
  - [B1_subset_one_file](notebooks/B1_subset_one_file.ipynb): Manually do all the steps to find the regions of interest in one file (instead of relying on `ActiveLearningProtocol`).

## 📘 Documentation

If you want more detail about function/method arguments, please refer to the documentation: https://frenchkrab.github.io/powerset_calibration
onnx-embedding-models 0.0.16
Python utility for text embeddings in ONNX.
Requires Python: >=3.9

# onnx_embedding_models
utilities for loading and running text embeddings with onnx

## CHANGELOG

0.0.14 - 2024-06-19
- support for loading from any huggingface repo with from_pretrained (provided it has a tokenizer and some onnx model in it)
- added mxbai-embed-large to the model registry
- embedding model `.encode` method now returns numpy arrays by default instead of lists
license-selector 0.1.2
A tool to help select an appropriate open-source license for your project
Requires Python: >=3.6

# License Selector

A tool to help select an appropriate open-source license for your project.

## Installation

You can install the License Selector using pip:

```bash
pip install license-selector
```

## Usage

To use the License Selector, simply run the following command:

```bash
license-selector
```

This will guide you through a series of questions to help you select an appropriate license for your project.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Disclaimer

This tool is intended to help you make an informed decision about which license to use for your project. It is not a substitute for legal advice or professional advice. Use this tool at your own discretion.

## Contributing

Contributions are welcome! If you have any suggestions or improvements, please open an issue or submit a pull request.

## Acknowledgments

The License Selector was inspired by the [Choose a License](https://choosealicense.com/) website, which is a great resource for choosing open-source licenses.