kmkarakaya commited on
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a90174f
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1 Parent(s): 78111c9

Update app.py

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  1. app.py +8 -11
app.py CHANGED
@@ -24,31 +24,28 @@ def generate_review(prompt):
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  title="Turkish Review Generator: A GPT2 based Text Generator Trained with a Custom Dataset"
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  description= """Generate a review in Turkish by providing a prompt.
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  Generation takes 15-20 seconds on average.
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- You can share your experience by Flagging.
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- Enjoy!
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- NOTE: Examples can sometimes generate ERROR. When you see ERROR on the screen just click SUBMIT. Model will generate text in 15-20 secs."""
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  article = """<p style='text-align: center'>On YouTube:</p>
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  <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v9d9w-L0S8XUXXdNks7vPBL' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p>
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  <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v8aajw6m9MRNbAAbL63flKD' target='_blank'>Hugging Face kütüphanesini kullanarak bir GPT2 Transformer Dil Modelini Kendi Veri Setimizle nasıl eğitip kullanabiliriz? (in Turkish)</a></p>
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  <p style='text-align: center'>On Medium:</p>
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  <p style='text-align: center'><a href='https://medium.com/deep-learning-with-keras/how-to-train-a-hugging-face-causal-language-model-from-scratch-8d08d038168f' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p>"""
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- examples1=["Bir hafta önce aldığım cep telefonu",
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  "Tatil için rezervasyon yaptırdım",
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  "Geçen ay sipariş verdiğim",
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  "Spor salonuna abone oldum"]
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- examples=["Bir hafta önce aldığım cep telefonu",
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- "Tatil için rezervasyon yaptırdım"]
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  demo = gr.Interface(fn=generate_review,
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- #inputs= gr.Textbox(lines=5, placeholder="enter or select a prompt below..."),
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- #outputs= gr.Textbox(lines=5, placeholder="genereated review will be here..."),
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- inputs="text",
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- outputs="text",
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- examples=examples,
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  title=title,
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  description= description,
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  article = article
 
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  #allow_flagging="manual",
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  #flagging_options=["good","moderate", "non-sense", ]
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  #flagging_dir='./flags'
 
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  title="Turkish Review Generator: A GPT2 based Text Generator Trained with a Custom Dataset"
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  description= """Generate a review in Turkish by providing a prompt.
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  Generation takes 15-20 seconds on average.
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+ Enjoy!"""
 
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+ #NOTE: Examples can sometimes generate ERROR. When you see ERROR on the screen just click SUBMIT. Model will generate text in 15-20 secs.
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  article = """<p style='text-align: center'>On YouTube:</p>
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  <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v9d9w-L0S8XUXXdNks7vPBL' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p>
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  <p style='text-align: center'><a href='https://youtube.com/playlist?list=PLQflnv_s49v8aajw6m9MRNbAAbL63flKD' target='_blank'>Hugging Face kütüphanesini kullanarak bir GPT2 Transformer Dil Modelini Kendi Veri Setimizle nasıl eğitip kullanabiliriz? (in Turkish)</a></p>
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  <p style='text-align: center'>On Medium:</p>
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  <p style='text-align: center'><a href='https://medium.com/deep-learning-with-keras/how-to-train-a-hugging-face-causal-language-model-from-scratch-8d08d038168f' target='_blank'>How to Train a Hugging Face Causal Language Model from Scratch with a Custom Dataset and a Custom Tokenizer?</a></p>"""
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+ examples=["Bir hafta önce aldığım cep telefonu",
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  "Tatil için rezervasyon yaptırdım",
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  "Geçen ay sipariş verdiğim",
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  "Spor salonuna abone oldum"]
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+
 
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  demo = gr.Interface(fn=generate_review,
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+ inputs= gr.Textbox(lines=5, label="Prompt", placeholder="enter or select a prompt below..."),
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+ outputs= gr.Textbox(lines=5, label="Generated Review", placeholder="genereated review will be here..."),
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+ #examples=examples,
 
 
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  title=title,
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  description= description,
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  article = article
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+ default= "Geçen ay sipariş verdiğim"
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  #allow_flagging="manual",
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  #flagging_options=["good","moderate", "non-sense", ]
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  #flagging_dir='./flags'