semvec/pypi-packages huggingface dataset:
text2timeline 0.0.2
This framework facilitates the development of temporal aware models
License: MIT
Requires Python: >=3.8

# text2timeline

A framework for the development of temporally aware models.

![](imgs/text2timeline.png)

## Installation

The package is available on [PyPI]():

```shell
pip install text2timeline
```

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.
text2timeline 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 text2timeline download --help
```

## Contributing

1. Fork it (https://github.com/hmosousa/text2timeline)
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)
bagofvectorvae 0.0.11
A weighted VAE layer for transformers
License: MIT
Keywords: transformers,vae,non-parametric,dirichlet,layer

This is a Pytorch layer intended to be used between a transformer encoder and transformer decoder.


Change Log
==========

0.0.1 (15/09/2021)
-------------------
- First release

0.0.2 (15/09/2021)
-------------------
- Test change

0.0.3 (16/09/2021)
-------------------
- Reverted change

0.0.4 (17/09/2021)
-------------------
- Incorporated masks and vectorised the KL divergences

0.0.5 (18/09/2021)
-------------------
- Fixed gradient error by adding an epsilon to masks

0.0.6 (18/09/2021)
-------------------
- Greatly improved speed by implementing the KL divergence between multivariate Gaussians with a diagonal covariance

0.0.7 (22/09/2021)
-------------------
- Fixed epsilon problem and improved documentation

0.0.8 (30/09/2021)
-------------------
- Changed the dirichlet KL and clipped the variables that could become negative 

0.0.9 (08/10/2021)
-------------------
- Changed the Gaussian epsilon. Hopefully more numerical stability.

0.0.10 (12/10/2021)
-------------------
- Created a mask to include both reparameterisations gauss and invcdf depending on the value of alpha. No longer need the bovvae_reparm parameter.

0.0.11 (13/10/2021)
-------------------
- Included a sampling function. This samples from the prior N(0,1) and Dir(1, ... ,1)