refactor: refactor pre-trained vectors, ranking, ODQA

README.md

@@ -141,15 +141,15 @@ You can also specify batch size with `-b` or `--batch-size` parameter.
 | [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. |
+| [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](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.|
 | **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. |
 | **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