Update README.md

README.md

@@ -1,20 +1,20 @@
 ---
-language: fas
+language: rus
 license: cc-by-4.0
 tags:
 - word2vec
-datasets: Persian_CoNLL17_corpus
+datasets: Russian_CoNLL17_corpus
 ---
 
 ## Information
-A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 966446 corresponding to 1180218836 tokens from the dataset `Persian_CoNLL17_corpus`.
+A word2vec model trained by Andrey Kutuzov (andreku@ifi.uio.no) on a vocabulary of size 3338424 corresponding to 3386127535 tokens from the dataset `Russian_CoNLL17_corpus`.
 The model is trained with the following properties: no lemmatization and postag with the algorith Word2Vec Continuous Skipgram with window of 10 and dimension of 100.
 
 ## How to use?
 ```
 from gensim.models import KeyedVectors
 from huggingface_hub import hf_hub_download
-model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_61", filename="model.bin"), binary=True, unicode_errors="ignore")
+model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/nlpl_65", filename="model.bin"), binary=True, unicode_errors="ignore")
 ```
 
 ## Citation
@@ -22,4 +22,4 @@ Fares, Murhaf; Kutuzov, Andrei; Oepen, Stephan & Velldal, Erik (2017). Word vect
 
 This archive is part of the NLPL Word Vectors Repository (http://vectors.nlpl.eu/repository/), version 2.0, published on Friday, December 27, 2019.
 Please see the file 'meta.json' in this archive and the overall repository metadata file http://vectors.nlpl.eu/repository/20.json for additional information.
-The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/61.zip
+The life-time identifier for this model is: http://vectors.nlpl.eu/repository/20/65.zip