feat: Read The Docs integration

* docs: initial refactor documentation for RTD

* docs: initial refactor documentation for RTD

* feat: explicit settings for RTD builds

* docs: check slotfill

* docs: check ner

* docs: typing and example of docstring for NerNetwork

* docs: typing and example of docstring for GoalOrientedBotNetwork

* refactor: switch to new classifiers readme

* refactor: Google style guide for docstrings

* refactor: refactor seq2seq-gobot and speller

* refactor: refactor pre-trained vectors, ranking, ODQA

* docs: refactor morphological tagger

* docs: refactor squad

* docs: remove converted MD-files add evolution

* docs: refactor structure, add tutorials

README.md

@@ -135,21 +135,21 @@ You can also specify batch size with `-b` or `--batch-size` parameter.
 
 | Component | Description |
 | --------- | ----------- |
-| [NER component](deeppavlov/models/ner/README.md) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. |
-| [Slot filling components](deeppavlov/models/slotfill/README.md) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.|
-| [Classification component](deeppavlov/models/classifiers/README.md) | Component for classification tasks (intents, sentiment, etc) on word-level. Shallow-and-wide CNN, Deep CNN, BiLSTM,  BiLSTM with self-attention and other models are presented. The model allows multilabel classification of sentences. |
-| [Goal-oriented bot](deeppavlov/models/go_bot/README.md) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand.  |
-| [Seq2seq goal-oriented bot](deeppavlov/models/seq2seq_go_bot/README.md) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. |
-| [Automatic spelling correction component](deeppavlov/models/spelling_correction/README.md) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. |
-| [Ranking component](deeppavlov/models/ranking/README.md) |  Based on [LSTM-based deep learning models for non-factoid answer selection](https://arxiv.org/abs/1511.04108). The model performs ranking of responses or contexts from some database by their relevance for the given context. |
-| [Question Answering component](deeppavlov/models/squad/README.md) | Based on [R-NET: Machine Reading Comprehension with Self-matching Networks](https://www.microsoft.com/en-us/research/publication/mrc/). The model solves the task of looking for an answer on a question in a given context ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) task format). |
-| [Morphological tagging component](deeppavlov/models/morpho_tagger/README.md) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.|
+| [NER component](docs/userdocs/ner.rst) | Based on neural Named Entity Recognition network. The NER component reproduces architecture from the paper [Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition](https://arxiv.org/pdf/1709.09686.pdf) which is inspired by Bi-LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. |
+| [Slot filling components](docs/userdocs/slot_filling.rst) | Based on fuzzy Levenshtein search to extract normalized slot values from text. The components either rely on NER results or perform needle in haystack search.|
+| [Classification component](docs/userdocs/classifiers.rst) | Component for classification tasks (intents, sentiment, etc) on word-level. Shallow-and-wide CNN, Deep CNN, BiLSTM,  BiLSTM with self-attention and other models are presented. The model allows multilabel classification of sentences. |
+| [Goal-oriented bot](docs/userdocs/go_bot.rst) | Based on Hybrid Code Networks (HCNs) architecture from [Jason D. Williams, Kavosh Asadi, Geoffrey Zweig, Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning – 2017](https://arxiv.org/abs/1702.03274). It allows to predict responses in goal-oriented dialog. The model is customizable: embeddings, slot filler and intent classifier can switched on and off on demand.  |
+| [Seq2seq goal-oriented bot](docs/userdocs/seq2seq_go_bot.rst) | Dialogue agent predicts responses in a goal-oriented dialog and is able to handle multiple domains (pretrained bot allows calendar scheduling, weather information retrieval, and point-of-interest navigation). The model is end-to-end differentiable and does not need to explicitly model dialogue state or belief trackers. |
+| [Automatic spelling correction component](docs/userdocs/spelling_correction.rst) | Pipelines that use candidates search in a static dictionary and an ARPA language model to correct spelling errors. |
+| [Ranking component](docs/userdocs/neural_ranking.rst) |  Based on [LSTM-based deep learning models for non-factoid answer selection](https://arxiv.org/abs/1511.04108). The model performs ranking of responses or contexts from some database by their relevance for the given context. |
+| [Question Answering component](docs/userdocs/squad.rst) | Based on [R-NET: Machine Reading Comprehension with Self-matching Networks](https://www.microsoft.com/en-us/research/publication/mrc/). The model solves the task of looking for an answer on a question in a given context ([SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) task format). |
+| [Morphological tagging component](docs/userdocs/morphotagger.rst) | Based on character-based approach to morphological tagging [Heigold et al., 2017. An extensive empirical evaluation of character-based morphological tagging for 14 languages](http://www.aclweb.org/anthology/E17-1048). A state-of-the-art model for Russian and several other languages. Model assigns morphological tags in UD format to sequences of words.|
 | **Skills** |  |
-|[ODQA](deeppavlov/skills/odqa/README.md) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.|
+|[ODQA](docs/userdocs/odqa.rst) | An open domain question answering skill. The skill accepts free-form questions about the world and outputs an answer based on its Wikipedia knowledge.|
 | **Parameters Evolution** |  |
-| [Parameters evolution for models](deeppavlov/models/evolution/README.md) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. |
+| [Parameters evolution for models](docs/userdocs/parameters_evolution.rst) | Implementation of parameters evolution for DeepPavlov models that requires only some small changes in a config file. |
 | **Embeddings** |  |
-| [Pre-trained embeddings for the Russian language](pretrained-vectors.md) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/wiki/%D0%97%D0%B0%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B8%D1%86%D0%B0) and [Lenta.ru](https://lenta.ru/) corpora. |
+| [Pre-trained embeddings for the Russian language](docs/userdocs/pretrained_vectors.rst) | Word vectors for the Russian language trained on joint [Russian Wikipedia](https://ru.wikipedia.org/) and [Lenta.ru](https://lenta.ru/) corpora. |
 
 
 # Examples of some components

deeppavlov/__init__.py

@@ -13,7 +13,7 @@
 See the License for the specific language governing permissions and
 limitations under the License.
 """
-from .package_meta import __version__, __author__
+from .package_meta import __author__, __version__, __description__, __keywords__
 
 # check version
 import sys

deeppavlov/models/__init__.py

@@ -1,15 +0,0 @@
-"""
-Copyright 2017 Neural Networks and Deep Learning lab, MIPT
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-"""