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"""Gradio app that showcases Danish offensive text models.""" |
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import gradio as gr |
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from transformers import pipeline |
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from shap import Explainer |
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import numpy as np |
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def main(): |
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pipe = pipeline( |
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task="text-classification", |
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model="alexandrainst/da-offensive-detection-small", |
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) |
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examples = [ |
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"Din store idiot.", |
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"Jeg er glad for at være her.", |
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"Hvem tror du, du er?", |
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"Har du hæklefejl i kysen?", |
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"Hej med dig, jeg hedder Peter.", |
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"Fuck hvor er det dejligt, det her :)", |
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"🍆", |
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"😊", |
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] |
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def classification(text) -> tuple[dict[str, float], dict]: |
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output: list[dict] = pipe(text)[0] |
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print(output) |
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explainer = Explainer(pipe) |
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explanation = explainer([text]) |
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shap_values = explanation.values[0].sum(axis=1) |
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boundary = 0.03 |
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if np.abs(shap_values).max() <= boundary: |
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boundary = np.abs(shap_values).max() - 1e-6 |
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words: list[str] = explanation.data[0] |
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records = list() |
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char_idx = 0 |
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for word, shap_value in zip(words, shap_values): |
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if abs(shap_value) <= boundary: |
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entity = 'O' |
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else: |
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entity = output['label'].lower().replace(' ', '-') |
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if len(word): |
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start = char_idx |
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char_idx += len(word) |
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end = char_idx |
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records.append(dict( |
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entity=entity, |
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word=word, |
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score=abs(shap_value), |
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start=start, |
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end=end, |
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)) |
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print(list(zip(words, shap_values))) |
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print(records) |
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return ({output["label"]: output["score"]}, dict(text=text, entities=records)) |
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color_map = {"offensive": "red", "not-offensive": "green", 'O': 'white'} |
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demo = gr.Interface( |
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fn=classification, |
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inputs=gr.Textbox(placeholder="Enter sentence here...", value=examples[0]), |
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outputs=[gr.Label(), gr.HighlightedText(color_map=color_map)], |
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examples=examples, |
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title="Danish Offensive Text Detection", |
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description=""" |
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Detect offensive text in Danish. Write any text in the box below, and the model will predict whether the text is offensive or not: |
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_Also, be patient, as this demo is running on a CPU!_""", |
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) |
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demo.launch() |
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if __name__ == "__main__": |
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main() |
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