semvec/pypi-packages huggingface dataset:
intentional-ai 0.0.1
TBD
Requires Python: >=3.8

# intentional-ai

[![PyPI - Version](https://img.shields.io/pypi/v/intentional-ai.svg)](https://pypi.org/project/intentional-ai)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/intentional-ai.svg)](https://pypi.org/project/intentional-ai)

-----

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install intentional-ai
```

## License

`intentional-ai` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
pg2s 0.1.0
pg2s metric implementation
Requires Python: >=3.9

# PG2S
*A full Python library for the PG2S metric [(paper)](https://arxiv.org/abs/2408.05478).*


## Quickstart
#### Clone & Install
```shell
git clone https://github.com/Lab-RoCoCo-Sapienza/pg2s
cd pg2s
pip install .
```
or from pip:
```
pip install pg2s
```
#### Usage
To use the pg2s_score, you need to define:

1. a tuple of list tuples, containing the tasks and for each of them the truth and prediction actions;
2. an hyperparameter alpha that can be tuned to give more importance to 
    the goal-wise similarity or to the sentence-wise similarity. Default value is 0.5
Here is how to call the function in your Python code:

```
from pg2s.metric import pg2s_score

plans = {
    "name-task-1": {
        'truth':[
            'Action 1',
            'Action 2',
            ...
            'Action N'
                    ],
        'predict':[
            'Action 1',
            'Action 2',
            ...
            'Action M'
        ]
    },
}

# Calculate the similarity score with a custom alpha value
score = pg2s_score(plans, alpha=0.7)
print(f"PG2S Score: {score}")
```
py-pg2s 0.1.0
pg2s metric implementation
Requires Python: >=3.9

# PG2S
*A full Python library for the PG2S metric [(paper)](https://arxiv.org/abs/2408.05478).*


## Quickstart
#### Clone & Install
```shell
git clone https://github.com/Lab-RoCoCo-Sapienza/pg2s
cd pg2s
pip install .
```
or from pip:
```
pip install pg2s
```
#### Usage
To use the pg2s_score, you need to define:

1. a tuple of list tuples, containing the tasks and for each of them the truth and prediction actions;
2. an hyperparameter alpha that can be tuned to give more importance to 
    the goal-wise similarity or to the sentence-wise similarity. Default value is 0.5
Here is how to call the function in your Python code:

```
from pg2s.metric import pg2s_score

plans = {
    "name-task-1": {
        'truth':[
            'Action 1',
            'Action 2',
            ...
            'Action N'
                    ],
        'predict':[
            'Action 1',
            'Action 2',
            ...
            'Action M'
        ]
    },
}

# Calculate the similarity score with a custom alpha value
score = pg2s_score(plans, alpha=0.7)
print(f"PG2S Score: {score}")
```