Update README.md

README.md

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 # blaze-koneski
 GPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.
 
-## Model description
-mk-gpt2 is a transformers model pretrained on a very large corpus of Macedonian data in a self-supervised fashion. This
-means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
-of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
-it was trained to guess the next word in sentences.
-More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
-shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
-predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
-This way, the model learns an inner representation of the Macedonian language that can then be used to extract features
-useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
-prompt.
+## About Blaze Koneski
+Born in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, corresponding member of the Yugoslav Academy of Sciences and Arts, as well as of the Serbian and Slovene Academies, and honorary doctor of the Universities of Chicago and Krakow.
+
+Wrote poetry, short stories, and essays, as well as scholarly works, many of them on the Macedonian language. Editor of the Dictionarv of the Macedonian Language, translator of Heine and Shakespeare. His works have been translated into Serbian, Croatian, Slovene, Albanian, Turkish, Hungarian, French, Russian, Italian, Greek, Polish, Romanian, German, and English.
+
+Winner of numerous prizes, including the Golden Wreath of the Struga Poetry Evenings.
 
 ### How to use
 Here is how to use this model to get the features of a given text in PyTorch: