Metadata-Version: 2.1
Name: nerblackbox
Version: 0.0.7
Summary: fine-tune transformer-based models for named entity recognition
Home-page: https://pypi.org/project/nerblackbox
Author: Felix Stollenwerk
Author-email: felix.stollenwerk@arbetsformedlingen.se
License: Apache 2.0
Description: 
        .. .. include:: ./docs_source/source/shared/main1.rst
        
        ===========
        nerblackbox
        ===========
        
        A python package to fine-tune transformer-based models for Named Entity Recognition (NER).
        
        .. image:: https://img.shields.io/pypi/v/nerblackbox
            :target: https://pypi.org/project/nerblackbox
            :alt: PyPI
        
        .. image:: https://img.shields.io/pypi/pyversions/nerblackbox
            :target: https://www.python.org/doc/versions/
            :alt: PyPI - Python Version
        
        .. image:: https://travis-ci.org/af-ai-center/nerblackbox.svg?branch=master
            :target: https://travis-ci.org/af-ai-center/nerblackbox
            :alt: Travis CI
        
        .. image:: https://img.shields.io/badge/code%20style-black-000000.svg
            :target: https://github.com/psf/black
        
        .. image:: https://img.shields.io/pypi/l/nerblackbox
            :target: https://github.com/af-ai-center/nerblackbox/blob/latest/LICENSE.txt
            :alt: PyPI - License
        
        Resources
        =========
        
        - Source Code: https://github.com/af-ai-center/nerblackbox
        - Documentation: https://af-ai-center.github.io/nerblackbox
        - PyPI: https://pypi.org/project/nerblackbox
        
        About
        =====
        
        `Transformer-based models <https://arxiv.org/abs/1706.03762>`_ like `BERT <https://arxiv.org/abs/1810.04805>`_ have had a `game-changing impact <https://paperswithcode.com/task/language-modelling>`_ on Natural Language Processing.
        
        In order to utilize the `publicly accessible pretrained models <https://huggingface.co/transformers/pretrained_models.html>`_ for
        `Named Entity Recognition <https://en.wikipedia.org/wiki/Named-entity_recognition>`_,
        one needs to retrain (or "fine-tune") them using labeled text.
        
        **nerblackbox makes this easy.**
        
        .. image:: https://raw.githubusercontent.com/af-ai-center/nerblackbox/master/docs/_static/nerblackbox.png
        
        .. .. include:: ./docs_source/source/shared/main2.rst
        
        You give it
        
        - a **Dataset** (labeled text)
        - a **Pretrained Model** (transformers)
        
        and you get
        
        - the best **Fine-tuned Model**
        - its **Performance** on the dataset
        
        Installation
        ============
        
            ``pip install nerblackbox``
        
        Usage
        =====
        
        .. .. include:: ./docs_source/source/shared/usage.rst
        
        Fine-tuning can be done in a few simple steps using an "experiment configuration file"
        
        .. code-block:: python
        
           # cat <experiment_name>.ini
           dataset_name = swedish_ner_corpus
           pretrained_model_name = af-ai-center/bert-base-swedish-uncased
        
        and either the Command Line Interface (CLI) or the Python API:
        
        .. code-block:: python
        
           # CLI
           nerbb run_experiment <experiment_name>          # fine-tune
           nerbb get_experiment_results <experiment_name>  # get results/performance
           nerbb predict <experiment_name> <text_input>    # apply best model
        
           # Python API
           nerbb = NerBlackBox()
           nerbb.run_experiment(<experiment_name>)         # fine-tune
           nerbb.get_experiment_results(<experiment_name>) # get results/performance
           nerbb.predict(<experiment_name>, <text_input>)  # apply best model
        
Keywords: NLP,NER,named entity recognition,BERT,transformer,pytorch
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: Unix
Classifier: Topic :: Text Processing :: Linguistic
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: dev
