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Update app.py
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app.py
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@@ -1,7 +1,7 @@
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# -*- coding: utf-8 -*-
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# file: deploy_demo.py
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# time: 2021/10/10
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# author: yangheng <
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# github: https://github.com/yangheng95
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# Copyright (C) 2021. All Rights Reserved.
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@@ -10,7 +10,7 @@ import pandas as pd
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from pyabsa import APCCheckpointManager
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sentiment_classifier = APCCheckpointManager.get_sentiment_classifier(checkpoint='
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auto_device=True # False means load model on CPU
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)
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@@ -18,6 +18,7 @@ sentiment_classifier = APCCheckpointManager.get_sentiment_classifier(checkpoint=
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def inference(text):
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result = sentiment_classifier.infer(text=text,
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print_result=True,
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clear_input_samples=True)
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result = pd.DataFrame({
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@@ -36,19 +37,16 @@ if __name__ == '__main__':
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fn=inference,
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inputs=["text"],
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examples=[
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['
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['
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['I have had my [ASP]computer[ASP] for 2 weeks already and it [ASP]works[ASP] perfectly . !sent! Positive, Positive'],
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['
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['Use [ASP] aspect [ASP] to wrap target aspects. And you can use "!sent!" to tell the model the true sentiment'],
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['This demo is trained on the laptop and restaurant and other review datasets from [ASP]ABSADatasets[ASP] (https://github.com/yangheng95/ABSADatasets)'],
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['To fit on your data, please train the model on your own data, see the [ASP]PyABSA[ASP] (https://github.com/yangheng95/PyABSA)'],
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],
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outputs="dataframe",
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description='This demo is trained on the public and community shared datasets from ABSADatasets (https://github.com/yangheng95/ABSADatasets),'
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' please feel free to share your data to improve this work. To fit on your data, please train our ATEPC models on your own data,'
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' see the PyABSA (https://github.com/yangheng95/PyABSA/tree/release/demos/aspect_polarity_classification)',
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title='Multilingual Aspect Sentiment Classification for Short Texts (powered by PyABSA)'
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)
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iface.launch()
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# -*- coding: utf-8 -*-
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# file: deploy_demo.py
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# time: 2021/10/10
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# author: yangheng <hy345@exeter.ac.uk>
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# github: https://github.com/yangheng95
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# Copyright (C) 2021. All Rights Reserved.
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from pyabsa import APCCheckpointManager
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sentiment_classifier = APCCheckpointManager.get_sentiment_classifier(checkpoint='multilingual',
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auto_device=True # False means load model on CPU
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)
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def inference(text):
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result = sentiment_classifier.infer(text=text,
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print_result=True,
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ignore_error=False,
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clear_input_samples=True)
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result = pd.DataFrame({
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fn=inference,
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inputs=["text"],
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examples=[
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['Strong build though which really adds to its [ASP]durability[ASP] .'], # !sent! Positive
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['Strong [ASP]build[ASP] though which really adds to its durability . !sent! Positive'],
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['The [ASP]battery life[ASP] is excellent - 6-7 hours without charging . !sent! Positive'],
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['I have had my [ASP]computer[ASP] for 2 weeks already and it [ASP]works[ASP] perfectly . !sent! Positive, Positive'],
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['And I may be the only one but I am really liking [ASP]Windows 8[ASP] . !sent! Positive'],
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['This demo is trained on the laptop and restaurant and other review datasets from [ASP]ABSADatasets[ASP] (https://github.com/yangheng95/ABSADatasets)'],
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['To fit on your data, please train the model on your own data, see the [ASP]PyABSA[ASP] (https://github.com/yangheng95/PyABSA)'],
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],
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outputs="dataframe",
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title='Multilingual Aspect Sentiment Classification for Short Texts (powered by PyABSA)'
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)
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iface.launch(share=True)
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