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Update app.py
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app.py
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@@ -44,7 +44,7 @@ gr.Interface(fn=code_summarizer,
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label='CODE SUMMARY'),
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title='Python Code Summarizer From CodeTrans',
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description='Summarize any Python source code',
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article='''<h2><b>Additional Information</b></h2><p style='text-align: justify'>This CodeTrans model is based on the T5-large model architecture pretrained on the Python programming language.
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theme='dark-peach',
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layout='horizontal',
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examples=call_examples(),
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label='CODE SUMMARY'),
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title='Python Code Summarizer From CodeTrans',
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description='Summarize any Python source code',
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article='''<h2><b>Additional Information</b></h2><p style='text-align: justify'>This CodeTrans model is based on the T5-large model architecture pretrained on the Python programming language. It is trained on tokenized python code functions, which is why it works best with tokenized python functions. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets. It is then fine-tuned on the source code summarization task for the python code snippets. Further information about the employed model (<b>SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune</b>) such as metrics, data set used, etc. are found in <b><a href='https://huggingface.co/SEBIS/code_trans_t5_large_source_code_summarization_python_multitask_finetune' target='_blank'>this link</a></b> and the original GitHub repository is found in <b><a href='https://github.com/agemagician/CodeTrans' target='_blank'>this link</a></b>.</p>''',
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theme='dark-peach',
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layout='horizontal',
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examples=call_examples(),
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