Update README.md

README.md

@@ -13,16 +13,18 @@ _name_ is pre-trained word vectors for the Ukrainian language, trained with fast
 
 Our model increases Accuracy by 6.3% compared to the [Facebook Ukrainian word vectors](https://fasttext.cc/docs/en/crawl-vectors.html) on the word analogy task. The dataset for Ukrainian word analogy is available [here](https://github.com/lang-uk/vecs/).
 
-Extrinsic evaluations were performed on two sequence labeling tasks: NER and POS tagging. NER-UK dataset was released by the lang-uk, and Ukrainian (UD) corpus was developed by a non-profit organization Institute for Ukrainian.
+Extrinsic evaluations were performed on two sequence labeling tasks: NER and POS tagging. [NER-UK dataset](https://github.com/lang-uk/ner-uk) was released by the [lang-uk](https://lang.org.ua/), and [Ukrainian (UD) corpus](https://github.com/brown-uk/corpus) was developed by a [non-profit organization Institute for Ukrainian](https://mova.institute/).
 
 Results:
-1) spaCy NER F-score 0.818
-2) POS Flair Accuracy 0.824
-3) POS spaCy Accuracy 0.911
+1) spaCy NER F-score: **0.818**
+2) POS Flair Accuracy: **0.824**
+3) POS spaCy Accuracy: **0.911**
 
 Usage
 ```
 import fasttext.util
-ft = fasttext.load_model('cc.en.300.bin')
+ft = fasttext.load_model('skipgram.uk.300.bin')
 ft.get_word_vector('hello')
 ```
+
+Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk](https://lang.org.ua) project, Natalya Romanyshyn, (Ukrainian Catholic University)[https://ucu.edu.ua/en/], 2022