tieval#

A framework for the development of temporally aware models.

_images/tieval.png

Installation#

The package is available on PyPI:

pip install tieval

Note

tieval requires Python 3.8 or above.

Usage#

To understand its usability refer to the notebooks available in hands on section.

Data#

Throughout the last two decades many datasets have been developed to train this task. text2timeline provides an easy interface to download the available corpus.

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

python -m tieval download --help

How to …#

In this section we summarize how to perform the most useful operations in text2timeline.

download a dataset.#

from tieval import datasets
datasets.download("TimeBank")

load a dataset.#

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

load a model.#

from tieval import models
heideltime = models.identification.HeidelTime()

make predictions.#

predictions = heideltime.predict(te3.test)

evaluate predictions.#

from tieval import evaluate
evaluator = evaluate.Evaluator(te3.test)
result = evaluator.timex_identification(predictions)

Contributing#

  1. Fork github repository

  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 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)