neuro_tagger 0.0.1.1

Text tagger, based on the ELMo embeddings and recurrent neural network, with the simple sklearn-like interface

neuro_tagger
============
Text tagger based on recurrent neural network. It can be used as NER,
dependency parser, morphoanalyzer etc.
The goal of this project is creation of a simple Python package with
the sklearn-like interface for solution of different tasks of text
tagging (named entity recognition, dependency parsing, etc) in case
number of labeled texts is very small (not greater than several
thousands). Special word embeddings named as `ELMo`
<https://arxiv.org/pdf/1802.05365.pdf> (**E**mbeddings from **L**anguage
**Mo**dels) ensure this possibility, because these embeddings are
contextual and they allow to design more simple and separable feature
space for words in texts.
ELMo embeddings are used as features of words in text, and different
variants of neural network architecture (BiLSTM, hybrid BiLSTM-CRF or
pure CRF) can be used as final classifier (tagger). I recommend to
use a special `TensorFlow Hub ELMo`<https://tfhub.dev/google/elmo/2>
for English NLP tasks and a `DeepPavlov ELMo`
<http://docs.deeppavlov.ai/en/master/apiref/models/embedders.html#deeppavlov.models.embedders.elmo_embedder.ELMoEmbedder>
(http://files.deeppavlov.ai/deeppavlov_data/elmo_ru-news_wmt11-16_1.5M_steps.tar.gz)
for for same tasks in Russian.