Metadata-Version: 2.1
Name: deep-ner
Version: 0.0.2
Summary: Deep-NER: named entity recognizer based on ELMo or BERT as embeddings and CRF as final classifier
Home-page: https://github.com/bond005/deep_ner
Author: Ivan Bondarenko
Author-email: bond005@yandex.ru
License: Apache License Version 2.0
Keywords: elmo,bert,ner,crf,nlp,tensorflow,scikit-learn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Text Processing
Classifier: Topic :: Text Processing :: Linguistic
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Dist: nltk (==3.4.5)
Requires-Dist: numpy (==1.18.1)
Requires-Dist: scikit-learn (==0.22.1)
Requires-Dist: scipy (==1.4.1tensorboard==2.1.0)
Requires-Dist: tensorflow (==1.15.0)
Requires-Dist: tensorflow-hub (==0.8.0)
Requires-Dist: bert-tensorflow (==1.0.1)


Deep-NER
============

Named entity recognizer based on ELMo or BERT as feature extractor and
CRF as final classifier.

The goal of this project is creation of a simple Python package with
the sklearn-like interface for solution of different named entity 
recognition tasks in case number of labeled texts is very small (not 
greater than several thousands). Special neural network language models 
named as ELMo (Embeddings from Language Models) and BERT (Bidirectional
Encoder Representations from Transformers) ensure this possibility,
because these language model were pre-trained on large text corpora and
so they can select deep semantic features from text.


