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1986392
Update app.py
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app.py
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@@ -24,7 +24,10 @@ 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 or selecting an example prompt below.
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Generation takes <b>15-20 seconds</b> on average.
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Enjoy!
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#<p>NOTE: Examples can sometimes generate ERROR. When you see ERROR on the screen <b>just click SUBMIT</b>. Model will generate text in 15-20 secs.</p>
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article = """<p style='text-align: center'>On YouTube:</p>
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@@ -32,10 +35,12 @@ 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_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
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"Geçen ay sipariş verdiğim",
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"
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demo = gr.Interface(fn=generate_review,
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@@ -52,5 +57,5 @@ demo = gr.Interface(fn=generate_review,
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)
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demo.launch()
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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 or selecting an example prompt below.
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Generation takes <b>15-20 seconds</b> on average.
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Enjoy!
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"""
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#<p>NOTE: Examples can sometimes generate ERROR. When you see ERROR on the screen <b>just click SUBMIT</b>. Model will generate text in 15-20 secs.</p>
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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_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 çalışmıyor.",
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"Tatil için yaptığım rezervasyonu iptal edemiyorum.",
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"Geçen ay sipariş verdiğim ayakkabı gelmedi.",
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"Abone olduğum spor salonu kapandı.",
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"Buzdolabından garip sesler geliyor.",
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"Otel tam bir fiyasko."]
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demo = gr.Interface(fn=generate_review,
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)
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demo.launch()
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