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Running
on
CPU Upgrade
Merge branch 'main' of https://huggingface.co/spaces/pszemraj/summarize-long-text
Browse files
app.py
CHANGED
@@ -40,7 +40,7 @@ def proc_submission(
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token_batch_length (int): the length of the token batches to use
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length_penalty (float): the length penalty to use
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repetition_penalty (float): the repetition penalty to use
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-
no_repeat_ngram_size (int): the no
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max_input_length (int, optional): the maximum input length to use. Defaults to 1024.
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Returns:
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@@ -181,13 +181,13 @@ if __name__ == "__main__":
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gr.Markdown("# Long-Form Summarization: LED & BookSum")
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gr.Markdown(
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"
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)
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with gr.Column():
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gr.Markdown("## Load Inputs & Select Parameters")
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gr.Markdown(
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"Enter
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)
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with gr.Row():
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model_size = gr.Radio(
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@@ -198,7 +198,7 @@ if __name__ == "__main__":
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label="Beam Search: # of Beams",
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value=2,
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)
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-
gr.Markdown("Load
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with gr.Row():
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example_name = gr.Dropdown(
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_examples,
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@@ -285,7 +285,7 @@ if __name__ == "__main__":
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"- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. "
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)
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gr.Markdown(
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"- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a notebook for a tutorial."
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)
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gr.Markdown("---")
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token_batch_length (int): the length of the token batches to use
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length_penalty (float): the length penalty to use
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repetition_penalty (float): the repetition penalty to use
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+
no_repeat_ngram_size (int): the no-repeat ngram size to use
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max_input_length (int, optional): the maximum input length to use. Defaults to 1024.
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Returns:
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gr.Markdown("# Long-Form Summarization: LED & BookSum")
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gr.Markdown(
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"LED models ([model card](https://huggingface.co/pszemraj/led-large-book-summary)) fine-tuned to summarize long-form text. A [space with other models can be found here](https://huggingface.co/spaces/pszemraj/document-summarization)"
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)
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with gr.Column():
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gr.Markdown("## Load Inputs & Select Parameters")
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gr.Markdown(
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"Enter or upload text below, and it will be summarized [using the selected parameters](https://huggingface.co/blog/how-to-generate). "
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)
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with gr.Row():
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model_size = gr.Radio(
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label="Beam Search: # of Beams",
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value=2,
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)
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gr.Markdown("Load a a .txt - example or your own (_You may find [this OCR space](https://huggingface.co/spaces/pszemraj/pdf-ocr) useful_)")
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with gr.Row():
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example_name = gr.Dropdown(
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_examples,
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"- The two most important parameters-empirically-are the `num_beams` and `token_batch_length`. "
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)
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gr.Markdown(
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+
"- The model can be used with tag [pszemraj/led-large-book-summary](https://huggingface.co/pszemraj/led-large-book-summary). See the model card for details on usage & a Colab notebook for a tutorial."
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)
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gr.Markdown("---")
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