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ae832b8
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Parent(s):
a657537
Update app.py
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app.py
CHANGED
@@ -272,14 +272,16 @@ def topics(output_file, input_checks):
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return gr.Plot.update(value=plot, visible=True) # no next_button becomes available
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with gr.Blocks() as demo:
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gr.Markdown("""
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<div style="text-align: center"><h1>EmotioNL: A framework for Dutch emotion detection</h1></div>
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<div style="display: block;margin-left: auto;margin-right: auto;width: 60%;"><img alt="EmotioNL logo" src="https://users.ugent.be/~lundbruy/EmotioNL.png" width="100%"></div>
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This demo was made to demonstrate the EmotioNL model, a transformer-based classification model that analyses emotions in Dutch texts. The model uses [RobBERT](https://github.com/iPieter/RobBERT), which was further fine-tuned on the [EmotioNL dataset](https://lt3.ugent.be/resources/emotionl/). The resulting model is a classifier that, given a sentence, predicts one of the following emotion categories: _anger_, _fear_, _joy_, _love_, _sadness_ or _neutral_. The demo can be used either in **sentence mode**, which allows you to enter a sentence for which an emotion will be predicted; or in **dataset mode**, which allows you to upload a dataset or see the full functuonality of with example data.
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""")
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with gr.Row():
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with gr.Column():
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input = gr.Textbox(
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return gr.Plot.update(value=plot, visible=True) # no next_button becomes available
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with gr.Blocks() as demo:
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gr.Markdown("""
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<div style="text-align: center"><h1>EmotioNL: A framework for Dutch emotion detection</h1></div>
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<div style="display: block;margin-left: auto;margin-right: auto;width: 60%;"><img alt="EmotioNL logo" src="https://users.ugent.be/~lundbruy/EmotioNL.png" width="100%"></div>
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This demo was made to demonstrate the EmotioNL model, a transformer-based classification model that analyses emotions in Dutch texts. The model uses [RobBERT](https://github.com/iPieter/RobBERT), which was further fine-tuned on the [EmotioNL dataset](https://lt3.ugent.be/resources/emotionl/). The resulting model is a classifier that, given a sentence, predicts one of the following emotion categories: _anger_, _fear_, _joy_, _love_, _sadness_ or _neutral_. The demo can be used either in **sentence mode**, which allows you to enter a sentence for which an emotion will be predicted; or in **dataset mode**, which allows you to upload a dataset or see the full functuonality of with example data.
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""")
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with gr.Tab("Sentence"):
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gr.Markdown("""
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""")
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with gr.Row():
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with gr.Column():
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input = gr.Textbox(
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