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import gradio as gr |
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import model_wrapper |
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model = model_wrapper.PredictionModel() |
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def pretty_print_opinion(opinion_dict): |
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res = [] |
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maxlen = max([len(key) for key in opinion_dict.keys()]) + 2 |
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maxlen = 0 |
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for key, value in opinion_dict.items(): |
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if key == 'Polarity': |
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res.append(f'{(key + ":").ljust(maxlen)} {value}') |
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else: |
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res.append(f'{(key + ":").ljust(maxlen)} \'{" ".join(value[0])}\'') |
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return '\n'.join(res) + '\n' |
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def predict(text): |
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predictions = model.predict([text]) |
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prediction = predictions[0] |
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results = [] |
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if not prediction['opinions']: |
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return 'No opinions detected' |
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for opinion in prediction['opinions']: |
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results.append(pretty_print_opinion(opinion)) |
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return '\n'.join(results) |
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markdown_text = ''' |
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<br> |
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<br> |
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This space provides a gradio demo and an easy-to-run wrapper of the pre-trained model for structured sentiment analysis in Norwegian language, pre-trained on the [NoReC dataset](https://huggingface.co/datasets/norec). |
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This model is an implementation of the paper "Direct parsing to sentiment graphs" (Samuel _et al._, ACL 2022). The main repository that also contains the scripts for training the model, can be found on the project [github](https://github.com/jerbarnes/direct_parsing_to_sent_graph). |
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The current model uses the 'labeled-edge' graph encoding, and achieves the following results on the NoReC dataset: |
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| Unlabeled sentiment tuple F1 | Target F1 | Relative polarity precision | |
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|:----------------------------:|:----------:|:---------------------------:| |
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| 0.393 | 0.468 | 0.939 | |
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The model can be easily used for predicting sentiment tuples as follows: |
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```python |
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>>> import model_wrapper |
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>>> model = model_wrapper.PredictionModel() |
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>>> model.predict(['vi liker svart kaffe']) |
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[{'sent_id': '0', |
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'text': 'vi liker svart kaffe', |
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'opinions': [{'Source': [['vi'], ['0:2']], |
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'Target': [['svart', 'kaffe'], ['9:14', '15:20']], |
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'Polar_expression': [['liker'], ['3:8']], |
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'Polarity': 'Positive'}]}] |
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``` |
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''' |
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with gr.Blocks() as demo: |
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with gr.Row(equal_height=False) as row: |
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text_input = gr.Textbox(label="input") |
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text_output = gr.Textbox(label="output") |
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with gr.Row(scale=4) as row: |
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text_button = gr.Button("submit").style(full_width=True) |
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text_button.click(fn=predict, inputs=text_input, outputs=text_output) |
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gr.Markdown(markdown_text) |
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demo.launch() |
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