Metadata-Version: 2.1
Name: zensols.deepnlp
Version: 0.0.5
Summary: Deep learning utility library for natural language processing that aids in feature engineering and embedding layers.
Home-page: https://github.com/plandes/deepnlp
Author: Paul Landes
Author-email: landes@mailc.net
License: UNKNOWN
Download-URL: https://github.com/plandes/deepnlp/releases/download/v0.0.5/zensols.deepnlp-0.0.5-py3-none-any.whl
Description: # DeepZensols Natural Language Processing
        
        [![PyPI][pypi-badge]][pypi-link]
        [![Python 3.7][python37-badge]][python37-link]
        [![Python 3.8][python38-badge]][python38-link]
        [![Python 3.9][python39-badge]][python39-link]
        
        Deep learning utility library for natural language processing that aids in
        feature engineering and embedding layers (see the [full documentation]).
        
        Features:
        * Configurable layers with little to no need to write code.
        * [Natural language specific layers](doc/md/layers.md):
          * Easily configurable word embedding layers for [Glove], [Word2Vec],
            [fastText].
          * Huggingface transformer ([BERT]) context based word vector layer.
          * Full [Embedding+BiLSTM-CRF] implementation using easy to configure
        	constituent layers.
        * [NLP specific vectorizers] that generate [zensols deeplearn] encoded and
          decoded [batched tensors] for [spaCy] parsed features, dependency tree
          features, overlapping text features and others.
        * Easily swapable during runtime embedded layers as [batched tensors] and other
          linguistic vectorized features.
        * Support for token, document and embedding level vectorized features.
        * Transformer word piece to linguistic token mapping.
        * Two full documented examples provided as both command line and [Jupyter
          notebooks](#usage-and-examples).
        * Command line support for training, testing, debugging, and creating
          predictions.
        
        
        ## Documentation
        
        See the [full documentation].
        
        
        ## Obtaining
        
        The easiest way to install the command line program is via the `pip` installer:
        ```bash
        pip3 install zensols.deepnlp
        ```
        
        Binaries are also available on [pypi].
        
        
        ## Usage and Examples
        
        If you're in a rush, you can dive right in to the [Movie Review Sentiment]
        example, which is a working project that uses this library.  However, you'll
        either end up reading up on the [zensols deeplearn] library before or during
        the tutorial.
        
        The usage of this library is explained in terms of two examples:
        * The [Movie Review Sentiment] trained and tested on the [Stanford movie
          review] and [Cornell sentiment polarity] data sets, which assigns a positive
          or negative score to a natural language movie review by critics.  Also see
          the [Jupyter movie notebook].
        
        * The [Named Entity Recognizer] trained and tested on the [CoNLL 2003 data set]
          to label named entities on natural language text.  Also see the [Jupyter NER
          notebook].
        
        
        ## Attribution
        
        This project, or example code, uses:
        * [Gensim] for [Glove], [Word2Vec] and [fastText] word embeddings.
        * [Huggingface Transformers] for [BERT] contextual word embeddings.
        * [bcolz] for fast read access to word embedding vectors.
        * [zensols nlparse] for feature generation from [spaCy] parsing.
        * [zensols deeplearn] for deep learning network libraries.
        
        Corpora used include:
        * [Stanford movie review]
        * [Cornell sentiment polarity]
        * [CoNLL 2003 data set]
        
        
        ## Changelog
        
        An extensive changelog is available [here](CHANGELOG.md).
        
        
        ## License
        
        [MIT License](LICENSE.md)
        
        Copyright (c) 2020 - 2021 Paul Landes
        
        
        <!-- links -->
        [pypi]: https://pypi.org/project/zensols.deepnlp/
        [pypi-link]: https://pypi.python.org/pypi/zensols.deepnlp
        [pypi-badge]: https://img.shields.io/pypi/v/zensols.deepnlp.svg
        [python37-badge]: https://img.shields.io/badge/python-3.7-blue.svg
        [python37-link]: https://www.python.org/downloads/release/python-370
        [python38-badge]: https://img.shields.io/badge/python-3.8-blue.svg
        [python38-link]: https://www.python.org/downloads/release/python-380
        [python39-badge]: https://img.shields.io/badge/python-3.9-blue.svg
        [python39-link]: https://www.python.org/downloads/release/python-390
        
        [Gensim]: https://radimrehurek.com/gensim/
        [Huggingface Transformers]: https://huggingface.co
        [Glove]: https://nlp.stanford.edu/projects/glove/
        [Word2Vec]: https://code.google.com/archive/p/word2vec/
        [fastText]: https://fasttext.cc
        [BERT]: https://huggingface.co/transformers/model_doc/bert.html
        [bcolz]: https://pypi.org/project/bcolz/
        [spaCy]: https://spacy.io
        [Pandas]: https://pandas.pydata.org
        
        [Stanford movie review]: https://nlp.stanford.edu/sentiment/
        [Cornell sentiment polarity]: https://www.cs.cornell.edu/people/pabo/movie-review-data/
        [CoNLL 2003 data set]: https://www.clips.uantwerpen.be/conll2003/ner/
        
        [zensols deeplearn]: https://github.com/plandes/deeplearn
        [zensols nlparse]: https://github.com/plandes/nlparse
        
        [full documentation]: https://plandes.github.io/deepnlp/index.html
        [Movie Review Sentiment]: doc/movie-example.md
        [Named Entity Recognizer]: doc/ner-example.md
        [Embedding+BiLSTM-CRF]: https://plandes.github.io/deepnlp/doc/ner-example.html#bilstm-crf
        [batched tensors]: https://plandes.github.io/deeplearn/doc/preprocess.html#batches
        [deep convolution layer]: https://plandes.github.io/deepnlp/api/zensols.deepnlp.layer.html#zensols.deepnlp.layer.conv.DeepConvolution1d
        [NLP specific vectorizers]: doc/vectorizers.md
        [Jupyter NER notebook]: https://github.com/plandes/deepnlp/blob/master/example/ner/notebook/ner.ipynb
        [Jupyter movie notebook]: https://github.com/plandes/deepnlp/blob/master/example/movie/notebook/movie.ipynb
        
Keywords: tooling
Platform: UNKNOWN
Description-Content-Type: text/markdown
