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37d1022
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Update app.py

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  1. app.py +8 -9
app.py CHANGED
@@ -38,19 +38,18 @@ def predict(text):
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  markdown_text = '''
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  <h1>Structured Sentiment Analysis for Norwegian</h1>
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  <p align="left">
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- This space provides a gradio demo of a <a href="https://huggingface.co/ltg/ssa-perin">pretrained model</a> for structured sentiment analysis (SSA) of Norwegian text, trained on the [NoReC_fine](https://github.com/ltgoslo/norec_fine) dataset by the <a href"https://www.mn.uio.no/ifi/english/research/groups/ltg/">Language Technology Group</a> at the University of Oslo. It implements a method described in the paper <a href="https://aclanthology.org/2022.acl-short.51/">Direct parsing to sentiment graphs</a> by Samuel et al. 2022.
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- <br><br>
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- The model will attempt to identify the following components for a given sentence it deems to be sentiment-bearing:
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  <ul>
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- <li> <it>source expressions</it> (the opinion holder), </li>
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- <li> <it>target expressions</it> (what the opinion is directed towards), </li>
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- <li> <it>polar expressions</it> (the part of the text indicating that an opinion is expressed), </li>
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- <li> and finally the <it>polarity</it> (positive or negative). </li>
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  </ul>
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- <br><br>
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  To download the model and find more in-depth documentation, please see <a href="https://huggingface.co/ltg/ssa-perin">https://huggingface.co/ltg/ssa-perin</a>
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  </p>
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- <br>
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  '''
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  with gr.Blocks() as demo:
 
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  markdown_text = '''
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  <h1>Structured Sentiment Analysis for Norwegian</h1>
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  <p align="left">
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+ This space provides a gradio demo of a <a href="https://huggingface.co/ltg/ssa-perin">pretrained model</a> for structured sentiment analysis (SSA) of Norwegian text, trained on the <a href="https://github.com/ltgoslo/norec_fine">NoReC_fine</a> dataset by the <a href"https://www.mn.uio.no/ifi/english/research/groups/ltg/">Language Technology Group</a> at the University of Oslo. It implements a method described in the paper <a href="https://aclanthology.org/2022.acl-short.51/">Direct parsing to sentiment graphs</a> by Samuel et al. 2022.
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+ <br>
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+ For a given sentence, the model will attempt to identify the following components if it is found to be sentiment-bearing:
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  <ul>
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+ <li> <i>source expressions</i> (the opinion holder), </li>
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+ <li> <i>target expressions</i> (what the opinion is directed towards), </li>
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+ <li> <i>polar expressions</i> (the part of the text indicating that an opinion is expressed), </li>
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+ <li> and finally the <i>polarity</i> (positive or negative). </li>
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  </ul>
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+ <br>
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  To download the model and find more in-depth documentation, please see <a href="https://huggingface.co/ltg/ssa-perin">https://huggingface.co/ltg/ssa-perin</a>
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  </p>
 
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  '''
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  with gr.Blocks() as demo: