Spaces:
Building
on
L4
Building
on
L4
Update app.py
Browse files
app.py
CHANGED
@@ -12,6 +12,8 @@ This model is based on an encoder-decoder T5 architecture with 1.1B parameters.
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For more details, please refer to our paper.
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"""
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@@ -107,10 +109,11 @@ with gr.Blocks(theme="soft") as demo:
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gr.Markdown("TURNA fine-tuned on part-of-speech-tagging. Enter text to parse parts of speech and pick the model.")
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with gr.Column():
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with gr.Row():
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pos_submit.click(pos, inputs=[pos_input, pos_choice], outputs=pos_output)
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pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice], outputs=pos_output, fn=pos)
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@@ -118,9 +121,10 @@ with gr.Blocks(theme="soft") as demo:
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gr.Markdown("TURNA fine-tuned on named entity recognition. Enter text to parse named entities and pick the model.")
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with gr.Column():
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with gr.Row():
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ner_output = gr.Textbox(label="NER Output")
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ner_submit.click(ner, inputs=[ner_input, ner_choice], outputs=ner_output)
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@@ -129,20 +133,22 @@ with gr.Blocks(theme="soft") as demo:
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gr.Markdown("TURNA fine-tuned on paraphrasing. Enter text to paraphrase and pick the model.")
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with gr.Column():
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with gr.Row():
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paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model")
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paraphrasing_input = gr.Textbox(label = "Paraphrasing Input")
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paraphrasing_submit = gr.Button()
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paraphrase_examples = gr.Examples(examples = long_text, inputs = [paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output, fn=paraphrase)
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with gr.Tab("Summarization"):
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gr.Markdown("TURNA fine-tuned on summarization. Enter text to summarize and pick the model.")
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with gr.Column():
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with gr.Row():
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sum_output = gr.Textbox(label = "Summarization Output")
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sum_submit.click(summarize, inputs=[sum_input, sum_choice], outputs=sum_output)
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For more details, please refer to our paper.
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Note: First inference might take time as the models are downloaded on-the-go.
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"""
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gr.Markdown("TURNA fine-tuned on part-of-speech-tagging. Enter text to parse parts of speech and pick the model.")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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pos_choice = gr.Radio(choices = ["turna_pos_imst", "turna_pos_boun"], label ="Model")
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pos_input = gr.Textbox(label="POS Input")
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pos_output = gr.Textbox(label="POS Output")
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pos_submit = gr.Button()
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pos_submit.click(pos, inputs=[pos_input, pos_choice], outputs=pos_output)
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pos_examples = gr.Examples(examples = ner_example, inputs = [pos_input, pos_choice], outputs=pos_output, fn=pos)
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gr.Markdown("TURNA fine-tuned on named entity recognition. Enter text to parse named entities and pick the model.")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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ner_choice = gr.Radio(choices = ["turna_ner_wikiann", "turna_ner_milliyet"], label ="Model")
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ner_input = gr.Textbox(label="NER Input")
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ner_submit = gr.Button()
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ner_output = gr.Textbox(label="NER Output")
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ner_submit.click(ner, inputs=[ner_input, ner_choice], outputs=ner_output)
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gr.Markdown("TURNA fine-tuned on paraphrasing. Enter text to paraphrase and pick the model.")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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paraphrasing_choice = gr.Radio(choices = ["turna_paraphrasing_tatoeba", "turna_paraphrasing_opensubtitles"], label ="Model")
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paraphrasing_input = gr.Textbox(label = "Paraphrasing Input")
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paraphrasing_submit = gr.Button()
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paraphrasing_output = gr.Text(label="Paraphrasing Output")
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paraphrasing_submit.click(paraphrase, inputs=[paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output)
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paraphrase_examples = gr.Examples(examples = long_text, inputs = [paraphrasing_input, paraphrasing_choice], outputs=paraphrasing_output, fn=paraphrase)
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with gr.Tab("Summarization"):
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gr.Markdown("TURNA fine-tuned on summarization. Enter text to summarize and pick the model.")
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with gr.Column():
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with gr.Row():
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with gr.Column():
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sum_choice = gr.Radio(choices = ["turna_summarization_mlsum", "turna_summarization_tr_news"], label ="Model")
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sum_input = gr.Textbox(label = "Summarization Input")
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sum_submit = gr.Button()
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sum_output = gr.Textbox(label = "Summarization Output")
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sum_submit.click(summarize, inputs=[sum_input, sum_choice], outputs=sum_output)
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