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19c6000
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Parent(s):
b4075da
Behind The Model tab
Browse files- gradio_demo.py +18 -1
gradio_demo.py
CHANGED
@@ -198,7 +198,24 @@ with demo:
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gr.Markdown("<h3> Generation History <h3>")
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# Displays a dataframe with the history of moves generated, with parameters
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history = gr.Dataframe(headers=["Move Name", "Move Description", "Generation Type", "Parameters"])
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-
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text_button_baseline.click(create_move, inputs=[text_input_baseline, history],
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outputs=[text_output_baseline, history])
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text_button_greedy.click(create_greedy_search_move, inputs=[text_input_greedy, history],
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gr.Markdown("<h3> Generation History <h3>")
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# Displays a dataframe with the history of moves generated, with parameters
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history = gr.Dataframe(headers=["Move Name", "Move Description", "Generation Type", "Parameters"])
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with gr.Row():
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gr.Markdown("<h3>How did you make this?<h3>")
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gr.Markdown("""
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I collected the dataset from Serebii (https://www.serebii.net) , a news source and aggregator of Pokemon info.
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I then added a seed phrase "This move is called" just before each move in order to assist the model in generation.
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I then followed HuggingFace's handy language_modeling.ipynb for fine-tuning distillgpt2 on this tiny dataset, and
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it surprisingly worked!
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I learned all about text generation using the book Natural Language Processing with Transformers by Lewis Turnstall,
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Leandro von Werra and Thomas Wolf, as well as this fantastic article (https://huggingface.co/blog/how-to-generate) by
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Patrick von Platen. Thanks to all of these folks for creating these learning materials, and thanks to the
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Hugging Face team for developing this product!
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""")
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text_button_baseline.click(create_move, inputs=[text_input_baseline, history],
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outputs=[text_output_baseline, history])
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text_button_greedy.click(create_greedy_search_move, inputs=[text_input_greedy, history],
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