[ci skip] fix link in demos readme

topicnet/demos/README.md

@@ -1,33 +1,37 @@
-# Demo
+# Demos
+
 This section provides demonstrations of how to use this library in NLP tasks.
 
-1. [RTL-Wiki-Preprocessing](RTL-Wiki-Preprocessing.ipynb) --  notebook working with a dataset introduced in [1]. It serves as an example of a typical preprocessing pipeline: getting a dataset, lemmatizing it, extracting n-grams/collocations, writing data in VW format
+1. [RTL-Wiki-Preprocessing](RTL-Wiki-Preprocessing.ipynb) — notebook working with a dataset introduced in [1]. It serves as an example of a typical preprocessing pipeline: getting a dataset, lemmatizing it, extracting n-grams/collocations, writing data in VW format
+
+2. [RTL-Wiki-Building-Topic-Mode](RTL-Wiki-Building-Topic-Model.ipynb) — notebook with first steps to build topic model by consequently tuning its hyperparameters
 
-2. [RTL-Wiki-Building-Topic-Mode](RTL-Wiki-Building-Topic-Model.ipynb) --  notebook with first steps to build topic model by consequently tuning its hyperparameters
+3. [Visualizing-Your-Model-Documents](Visualizing-Your-Model-Documents.ipynb) — notebook providing a fresh outlook on unstructured document collection with the help of a topic model
 
-3. [Visualizing-Your-Model-Documents](Visualizing-Your-Model-Documents.ipynb) -- notebook providing a fresh outlook on unstructured document collection with the help of a topic model
+4. [20NG-Preprocessing](20NG-Preprocessing.ipynb) — preparing data from a well-know 20 Newsgroups dataset
 
-4. [20NG-Preprocessing](20NG-Preprocessing.ipynb) -- preparing data from a well-know 20 Newsgroups dataset
+5. [20NG-GenSim-vs-TopicNet](20NG-GenSim-vs-TopicNet.ipynb) — a comparison between two topic models build by Gensim and TopicNet library. In the notebook we compare model topics by calculating their UMass coherence measure with the help of [Palmetto](https://palmetto.demos.dice-research.org/) and using Jaccard measure to compare topic top-tokens diversity
 
-5. [20NG-GenSim-vs-TopicNet](20NG-GenSim-vs-TopicNet.ipynb) -- a comparison between two topic models build by Gensim and TopicNet library. In the notebook we compare model topics by calculating their UMass coherence measure with the help of [Palmetto](https://palmetto.demos.dice-research.org/) and using Jaccard measure to compare topic top-tokens diversity
+6. [PostNauka-Building-Topic-Model](PostNauka-Building-Topic-Model.ipynb) — an analog of the RTL-Wiki notebook performed on the corpus of Russian pop-science articles given by postnauka.ru
 
-6. [PostNauka-Building-Topic-Model](PostNauka-Building-Topic-Model.ipynb)-- an analog of the RTL-Wiki notebook performed on the corpus of Russian pop-science articles given by postnauka.ru
+7. [PostNauka-Recipe](PostNauka-Recipe.ipynb) — a demonstration of rapid-prototyping methods provided by the library
 
-7. [PostNauka-Recipe](PostNauka-Recipe.ipynb) -- a demonstration of rapid-prototyping methods provided by the library
+8. [Coherence-Maximization-Recipe](Coherence-Maximization-Recipe.ipynb) — a recipe for hyperparameter search in regard to custom Coherence metric
 
-8. [Coherence-Maximization-Recipe](Coherence-Maximization-Recipe.ipynb) -- a recipe for hyperparameter search in regard to custom Coherence metric
+9. [Topic-Prior-Regularizer-Tutorial](Topic-Prior-Regularizer-Tutorial.ipynb) — a demonstration of the approach to learning topics from the unbalanced corpus
 
-9. [Topic-Prior-Regularizer-Tutorial](Topic-Prior-Regularizer-Tutorial.ipynb) -- a demonstration of the approach to learning topics from the unbalanced corpus
+10. [Making-Decorrelation-and-Topic-Selection-Friends](Making-Decorrelation-and-Topic-Selection-Friends.ipynb) — reproduction of a very complicated experiment on automatically learning optimal number of topics from the collection. Hurdle is -- both needed regularizers when working together nullify token-topic matrix.
 
-10. [Making-Decorrelation-and-Topic-Selection-Friends](Making-Decorrelation-and-Topic-Selection-Friends.ipynb) -- reproduction of a very complicated experiment on automatically learning optimal number of topics from the collection. Hurdle is -- both needed regularizers when working together nullify token-topic matrix.
+11. [Topic-Thetaless-Regularizer](Topic-Thetaless-Regularizer.ipynb) — the Additive Regularization of Topic Models (ARTM) formalism views the topic modeling task as an optimization problem and leverages various computational and optimization tricks to make it converge faster and more predictably. This is an example of such optimization (let's reduce the number of inferred parameters), interpreted as a powerful custom regularizer.
 
-11. [Topic-Thetaless-Regularizer](topic_thetaless_regularizer.ipynb) -- The Additive Regularization of Topic Models (ARTM) formalism views the topic modeling task as an optimization problem and leverages various computational and optimization tricks to make it converge faster and more predictably. This is an example of such optimization (let's reduce the number of inferred parameters), interpreted as a powerful custom regularizer.
 
+## References
 
-----
 [1](https://dl.acm.org/doi/10.5555/2984093.2984126) Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang, and David M. Blei. 2009. Reading tea leaves: how humans interpret topic models. In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS’09). Curran Associates Inc., Red Hook, NY, USA, 288–296.
 
-----
-P.S. All the guides are supposed to contain **working** examples of the library code.
+
+## P.S.
+
+All the guides are supposed to contain **working** examples of the library code.
 If you happen to find code that is no longer works, please write about it in the library issues.
 We will try to resolve it as soon as possible and plan to include fixes in the nearest releases.