Update README.md

README.md

@@ -1,18 +1,14 @@
 ---
 language: 
 - mk
-thumbnail: https://huggingface.co/macedonizer/mk-roberta-base/blaze-koneski.jpg
+thumbnail: https://huggingface.co/macedonizer/blaze-koneski/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/).
+# 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
@@ -32,35 +28,12 @@ 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')
+tokenizer = AutoTokenizer.from_pretrained('macedonizer/blaze-koneski') \\nmodel = AutoModelWithLMHead.from_pretrained('macedonizer/blaze-koneski')
 
-input_text = 'Скопје е '
+input_text = 'Моска '
 
-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, \
-    )
+if len(input_text) == 0: \\n    encoded_input = tokenizer(input_text, return_tensors="pt") \\n    output = model.generate( \\n        bos_token_id=random.randint(1, 50000), \\n        do_sample=True, \\n        top_k=50, \\n        max_length=1024, \\n        top_p=0.95, \\n        num_return_sequences=1, \\n    ) \\nelse: \\n    encoded_input = tokenizer(input_text, return_tensors="pt") \\n    output = model.generate( \\n        **encoded_input, \\n        bos_token_id=random.randint(1, 50000), \\n        do_sample=True, \\n        top_k=50, \\n        max_length=1024, \\n        top_p=0.95, \\n        num_return_sequences=1, \\n    )
 
-decoded_output = [] \
-for sample in output: \
-    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
+decoded_output = [] \\nfor sample in output: \\n    decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
 
 print(decoded_output)