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Update app.py
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app.py
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@@ -1,16 +1,5 @@
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import torch
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from openprompt.plms import load_plm
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from openprompt import PromptDataLoader
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from openprompt.prompts import ManualVerbalizer
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from openprompt.prompts import ManualTemplate
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from openprompt.data_utils import InputExample
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from openprompt import PromptForClassification
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def sentiment_analysis(sentence, template, positive, neutral, negative):
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model_name = "CCCC/ARCH_tuned_bert"
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template = template.replace('[SENTENCE]', '{"placeholder":"text_a"}')
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template = template.replace('[MASK]', '{"mask"}')
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classes = ['positive', 'neutral', 'negative']
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@@ -67,10 +56,12 @@ def sentiment_analysis(sentence, template, positive, neutral, negative):
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demo = gr.Interface(fn=sentiment_analysis,
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inputs = [gr.Textbox(placeholder="Enter sentence here.",label="sentence"),
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gr.Textbox(placeholder="Your template must have a [SENTENCE] token and a [MASK] token.",label="template"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="positive label words"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="neutral label words"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="negative label words")
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],
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outputs="text"
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demo.launch()
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def sentiment_analysis(sentence, template, model_name, positive, neutral, negative):
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model_name = model_name
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template = template.replace('[SENTENCE]', '{"placeholder":"text_a"}')
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template = template.replace('[MASK]', '{"mask"}')
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classes = ['positive', 'neutral', 'negative']
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demo = gr.Interface(fn=sentiment_analysis,
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inputs = [gr.Textbox(placeholder="Enter sentence here.",label="sentence"),
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gr.Textbox(placeholder="Your template must have a [SENTENCE] token and a [MASK] token.",label="template"),
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gr.Radio(choices=["ARCH_tuned_robert","FNCH_tuned_robert","AREN_tuned_robert","FNEN_tuned_robert","bert-base-uncased"], label="model choics"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="positive label words"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="neutral label words"),
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gr.Textbox(placeholder="Separate words with Spaces.",label="negative label words")
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],
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outputs="text",
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)
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demo.launch()
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