Metadata-Version: 2.1
Name: tieval
Version: 0.0.5
Summary: This framework facilitates the development and test of temporal-aware models.
Home-page: https://github.com/LIAAD/tieval
Author: Hugo Sousa
Author-email: hugo.o.sousa@inesctec.pt
License: MIT
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# tieval

[![PyPI](https://img.shields.io/pypi/v/tieval)](https://pypi.org/project/tieval/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/tieval)
[![PyPI - License](https://img.shields.io/pypi/l/tieval)](LICENSE)
![GitHub repo size](https://img.shields.io/github/repo-size/LIAAD/tieval)

A framework for the development of temporally aware models.

![](imgs/tieval.png)

## Installation

The package is available on [PyPI](https://pypi.org/project/tieval/):

```shell
pip install tieval
```

It requires Python 3.8 or above.

## Usage

To understand its usability refer to the notebooks available [here]().

## Data

Throughout the last two decades many datasets have been developed to train this task.
tieval provides an easy interface to download the available corpus.

To know more about the module run the following code on the terminal.

```shell
python -m tieval download --help
```

## How to ...

In this section we summarize how to perform the most useful operations in tieval.

### download a dataset.

```python
from pathlib import Path
from tieval import datasets

data_path = Path("data/")
datasets.download("TimeBank", data_path)
```

### load a dataset.

```python
import tieval.datasets
from tieval import datasets

te3 = tieval.datasets.read("TempEval-3")
```

### load a model.

```python
from tieval import models

heideltime = models.identification.HeidelTime()
```

### make predictions.

```python
predictions = heideltime.predict(te3.test)
```

### evaluate predictions.

```python
from tieval import evaluate

evaluator = evaluate.Evaluator(te3.test)
result = evaluator.timex_identification(predictions)
```

## Contributing

1. Fork it (https://github.com/LIAAD/tieval)
2. Create your feature branch (`git checkout -b feature/fooBar`)
3. Commit your changes (`git commit -am 'Add some fooBar'`)
4. Push to the branch (`git push origin feature/fooBar`)
5. Create a new Pull Request

## Meta

Hugo Sousa - hugo.o.sousa@inesctec.pt

This framework is part of the [Text2Story](https://text2story.inesctec.pt/) project which is financed by the ERDF –
European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the
PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia
within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185) 
