Add ruscorpora model. Fix #3 (#13)

* add ruscorpora-300

* add ruscorpora to README

README.md

@@ -107,6 +107,7 @@ To load a model or corpus, use either the Python or command line interface:
 | glove-wiki-gigaword-300 | 400000 | 376 MB | Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) | <ul><li>https://nlp.stanford.edu/projects/glove/</li> <li>https://nlp.stanford.edu/pubs/glove.pdf</li></ul> | Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/). | <ul><li>dimension - 300</li></ul> | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-300.txt`. | http://opendatacommons.org/licenses/pddl/ |
 | glove-wiki-gigaword-50 | 400000 | 65 MB | Wikipedia 2014 + Gigaword 5 (6B tokens, uncased) | <ul><li>https://nlp.stanford.edu/projects/glove/</li> <li>https://nlp.stanford.edu/pubs/glove.pdf</li></ul> | Pre-trained vectors based on Wikipedia 2014 + Gigaword, 5.6B tokens, 400K vocab, uncased (https://nlp.stanford.edu/projects/glove/). | <ul><li>dimension - 50</li></ul> | Converted to w2v format with `python -m gensim.scripts.glove2word2vec -i <fname> -o glove-wiki-gigaword-50.txt`. | http://opendatacommons.org/licenses/pddl/ |
 | word2vec-google-news-300 | 3000000 | 1662 MB | Google News (about 100 billion words) | <ul><li>https://code.google.com/archive/p/word2vec/</li> <li>https://arxiv.org/abs/1301.3781</li> <li>https://arxiv.org/abs/1310.4546</li> <li>https://www.microsoft.com/en-us/research/publication/linguistic-regularities-in-continuous-space-word-representations/?from=http%3A%2F%2Fresearch.microsoft.com%2Fpubs%2F189726%2Frvecs.pdf</li></ul> | Pre-trained vectors trained on a part of the Google News dataset (about 100 billion words). The model contains 300-dimensional vectors for 3 million words and phrases. The phrases were obtained using a simple data-driven approach described in 'Distributed Representations of Words and Phrases and their Compositionality' (https://code.google.com/archive/p/word2vec/). | <ul><li>dimension - 300</li></ul> | - | not found |
+| word2vec-ruscorpora-300 | 184973 | 198 MB | Russian National Corpus (about 250M words) | <ul><li>https://www.academia.edu/24306935/WebVectors_a_Toolkit_for_Building_Web_Interfaces_for_Vector_Semantic_Models</li> <li>http://rusvectores.org/en/</li> <li>https://github.com/RaRe-Technologies/gensim-data/issues/3</li></ul> | Word2vec Continuous Skipgram vectors trained on full Russian National Corpus (about 250M words). The model contains 185K words. | <ul><li>window_size - 10</li> <li>dimension - 300</li></ul> | The corpus was lemmatized and tagged with Universal PoS | not found |
 
 (table generated automatically by [generate_table.py](https://github.com/RaRe-Technologies/gensim-data/blob/master/generate_table.py) based on [list.json](https://github.com/RaRe-Technologies/gensim-data/blob/master/list.json))
 

list.json

@@ -122,6 +122,23 @@
 		}
 	},
 	"models": {
+		"word2vec-ruscorpora-300": {
+			"num_records": 184973,
+			"file_size": 208427381,
+			"base_dataset": "Russian National Corpus (about 250M words)",
+			"reader_code": "https://github.com/RaRe-Technologies/gensim-data/releases/download/word2vec-ruscorpora-300/__init__.py",
+			"license": "not found",
+			"parameters": {
+				"dimension": 300,
+				"window_size": 10
+			},
+			"description": "Word2vec Continuous Skipgram vectors trained on full Russian National Corpus (about 250M words). The model contains 185K words.",
+			"preprocessing": "The corpus was lemmatized and tagged with Universal PoS",
+			"read_more": ["https://www.academia.edu/24306935/WebVectors_a_Toolkit_for_Building_Web_Interfaces_for_Vector_Semantic_Models", "http://rusvectores.org/en/", "https://github.com/RaRe-Technologies/gensim-data/issues/3"],
+			"checksum": "9bdebdc8ae6d17d20839dd9b5af10bc4",
+			"file_name": "word2vec-ruscorpora-300.gz",
+			"parts": 1
+		},
 		"word2vec-google-news-300": {
 			"num_records": 3000000,
 			"file_size": 1743563840,