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README.md

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+---
+language: 
+- mk
+thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg
+license: apache-2.0
+datasets:
+- wiki-mk
+- time-mk-news-2010-2015
+---
+
+# mk-gpt2
+Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
+Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
+[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
+and first released at [this page](https://openai.com/blog/better-language-models/).
+
+## 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.
+
+### How to use
+Here is how to use this model to get the features of a given text in PyTorch:
+
+import random
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained('macedonizer/mk-gpt2') \
+model = AutoModelWithLMHead.from_pretrained('macedonizer/mk-gpt2')
+
+input_text = 'Скопје е '
+
+if len(input_text) == 0: \
+    encoded_input = tokenizer(input_text, return_tensors="pt") \
+    output = model.generate( \
+        bos_token_id=random.randint(1, 50000), \
+        do_sample=True, \
+        top_k=50, \
+        max_length=1024, \
+        top_p=0.95, \
+        num_return_sequences=1, \
+    ) \
+else: \
+    encoded_input = tokenizer(input_text, return_tensors="pt") \
+    output = model.generate( \
+        **encoded_input, \
+        bos_token_id=random.randint(1, 50000), \
+        do_sample=True, \
+        top_k=50, \
+        max_length=1024, \
+        top_p=0.95, \
+        num_return_sequences=1, \
+    )
+
+decoded_output = [] \
+for sample in output: \
+    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
+
+print(decoded_output)