second iteration

Author: Evgeny-Egorov-Projects

README.md

@@ -159,7 +159,8 @@ Here we can finally get on the main part: making your own, best of them all, man
 We need to load our data prepared previously with Dataset:
 
 ```python
-dataset = Dataset('/Wiki_raw_set/wiki_data.csv')
+DATASET_PATH = '/Wiki_raw_set/wiki_data.csv'
+dataset = Dataset(DATASET_PATH)
 ```
 
 ### Make initial model
@@ -247,9 +248,10 @@ best_model = experiment.select(perplexity_criterion)
 ```
 
 ### Alternatively: Use Recipes
-If you need a topic model now, you can use one ofthe code snippets we call recipes.
+If you need a topic model now, you can use one of the code snippets we call *recipes*.
 ```python
 from topicnet.cooking_machine.recipes import BaselineRecipe
+
 training_pipeline = BaselineRecipe()
 EXPERIMENT_PATH = '/home/user/experiment/'
 

topicnet/demos/5-20NG-GenSim-vs-TopicNet.ipynb renamed to topicnet/demos/20NG-GenSim-vs-TopicNet.ipynb

@@ -1,27 +1,27 @@
-### Demo
+# Demo
 This section provides demonstrations of how to use this library in NLP tasks.
 
-`1-RTL-Wiki-Preprocessing` --  notebook describing how to get a wikipedia dataset and write data in VW format.
+1. [RTL-Wiki-Preprocessing](RTL-Wiki-Preprocessing.ipynb) --  notebook describing how to get a Wikipedia dataset and write data in VW format.
 
-`2-RTL-Wiki-Building-Topic-Model` --  notebook with first steps to build topic model by consequently tuning it's 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` -- notebook providing a fres 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` -- 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` -- a comparisson between two topic models build by Gensim and TopicNet library. In the notebook we compare model topics by calculating their [UMass coherence measure](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](https://palmetto.demos.dice-research.org/) and using Jaccard measure to compare topic top-tokens diversity
 
-`6-Postnauka-Building-Topic-Model` -- 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` -- a demonstration of rapid-prototyping methods provided by the library
+7. [PostNauka-Recipe](PostNauka-Recipe) -- a demonstration of rapid-prototyping methods provided by the library
 
-`8-Coherence-Maximization-Recipe` -- 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` -- 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` -- 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.
 
 ----
 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.
+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.