NALP: Natural Adversarial Language Processing
Welcome to NALP.
Have you ever wanted to create natural text from raw sources? If yes, NALP is for you! This package is an innovative way of dealing with natural language processing and adversarial learning. From bottom to top, from embeddings to neural networks, we will foster all research related to this new trend.
Use NALP if you need a library or wish to:
- Create your embeddings;
- Design or use pre-loaded state-of-art neural networks;
- Mix-and-match different strategies to solve your problem;
- Because it is cool to play with text.
Read the docs at nalp.readthedocs.io.
NALP is compatible with: Python 3.6+.
Package guidelines
- The very first information you need is in the very next section.
- Installing is also easy if you wish to read the code and bump yourself into, follow along.
- Note that there might be some additional steps in order to use our solutions.
- If there is a problem, please do not hesitate, call us.
Getting started: 60 seconds with NALP
First of all. We have examples. Yes, they are commented. Just browse to examples/, chose your subpackage, and follow the example. We have high-level examples for most tasks we could think of.
Alternatively, if you wish to learn even more, please take a minute:
NALP is based on the following structure, and you should pay attention to its tree:
- nalp
- core
- corpus
- dataset
- encoder
- model
- corpus
- audio
- text
- datasets
- image
- language_modeling
- encoders
- integer
- word2vec
- models
- discriminators
- conv
- embedded_text
- linear
- lstm
- text
- generators
- bi_lstm
- conv
- gru
- gumbel_lstm
- gumbel_rmc
- linear
- lstm
- rmc
- rnn
- stacked_rnn
- layers
- gumbel_softmax
- multi_head_attention
- relational_memory_cell
- dcgan
- gan
- gsgan
- maligan
- relgan
- seqgan
- wgan
- utils
- constants
- loader
- logging
- preprocess
Core
The core is the core. Essentially, it is the parent of everything. You should find parent classes defining the basis of our structure. They should provide variables and methods that will help to construct other modules.
Corpus
Every pipeline has its first step, right? The corpus package serves as a basic class to load raw text and audio.
Datasets
Because we need data, right? Datasets are composed of classes and methods that allow preparing data for further neural networks.
Encoders
Text or Numbers? Encodings are used to make embeddings. Embeddings are used to feed into neural networks. Remember that networks cannot read raw text; therefore, you might want to pre-encode your data using well-known encoders.
Models
Each neural network architecture is defined in this package. From naรฏve RNNs to BiLSTMs, from GANs to TextGANs, you can use whatever suits your needs.
Utils
This is a utility package. Common things shared across the application should be implemented here. It is better to implement once and use it as you wish than re-implementing the same thing over and over again.
Installation
We believe that everything has to be easy. Not tricky or daunting, NALP will be the one-to-go package that you will need, from the very first installation to the daily-tasks implementing needs. If you may just run the following under your most preferred Python environment (raw, conda, virtualenv, whatever)!:
pip install nalp
Alternatively, if you prefer to install the bleeding-edge version, please clone this repository and use:
pip install -e .
Environment configuration
Note that sometimes, there is a need for additional implementation. If needed, from here, you will be the one to know all of its details.
Ubuntu
No specific additional commands needed.
Windows
No specific additional commands needed.
MacOS
No specific additional commands needed.
Support
We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository or [email protected].
1k Jan 02, 2023
1 Oct 05, 2021
16 Dec 03, 2021
94 Dec 26, 2022
179 Jan 07, 2023
66 Dec 29, 2022
33 Dec 27, 2022
49 Aug 24, 2022
1 Apr 17, 2022
132 Nov 25, 2022
42 Mar 16, 2022
1.3k Jan 01, 2023
286 Dec 06, 2022
18 Dec 17, 2022
1.2k Dec 26, 2022
261 Nov 12, 2022
90 Dec 19, 2022
27 Dec 22, 2022
1 Jan 16, 2022
29 Oct 28, 2022