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Duplicate from ArefSadeghian/arabert-finetuned-caner
Browse files- .gitattributes +34 -0
- README.md +14 -0
- app.py +90 -0
- requirements.txt +3 -0
.gitattributes
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README.md
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---
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title: ArefSadeghian Arabert Finetuned Caner
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emoji: 🦀
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colorFrom: red
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colorTo: blue
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sdk: gradio
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sdk_version: 3.21.0
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app_file: app.py
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pinned: false
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license: gpl
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duplicated_from: ArefSadeghian/arabert-finetuned-caner
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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from transformers import pipeline
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import re
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HTML_WRAPPER = """<div dir="rtl" style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
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# Replace this with above latest checkpoint
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model_checkpoint = "ArefSadeghian/arabert-finetuned-caner"
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token_classifier = pipeline(
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"token-classification", model=model_checkpoint, aggregation_strategy="simple"
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)
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import re
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import unicodedata
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diacritics = {
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'\u064B': None, # FATHATAN
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'\u064C': None, # DAMMATAN
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'\u064D': None, # KASRATAN
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'\u064E': None, # FATHA
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'\u064F': None, # DAMMA
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'\u0650': None, # KASRA
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'\u0651': None, # SHADDA
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'\u0652': None, # SUKUN
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}
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def remove_diacritics(text):
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normalized_text = unicodedata.normalize('NFKD', text)
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return normalized_text.translate(dict.fromkeys(map(ord, diacritics)))
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def remove_punctuation(text):
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return re.sub(r'[^\w\s]', '', text)
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def preprocess_arabic_text(text):
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# Remove diacritics
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text = remove_diacritics(text)
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# Remove punctuation
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text = remove_punctuation(text)
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# Normalize whitespace
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text = re.sub(r'\s+', ' ', text)
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# Convert to lowercase
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text = text.lower()
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return text
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# Define a function to highlight different labels in the text
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def highlight_text(text, entities):
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entity_colors = {"Allah": "#ffe5cc", "Book": "#b3daff", "Clan": "#faedcb", "Crime": "#ffb3d9",
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"Date": "#cce6ff", "Day": "#cce6ff", "Hell": "#d9d9d9", "Loc": "#d9b3ff",
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"Meas": "#e6ccff", "Mon": "#ffd6cc", "Month": "#ffd6cc", "NatOb": "#ffe0b3",
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"Number": "#ffe0cc", "Org": "#c1ffb3", "Para": "#f2f2f2", "Pers": "#b3ffb3",
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"Prophet": "#e6ccff", "Rlig": "#ffff80", "Sect": "#b3d9ff", "Time": "#ffb3ba"}
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highlighted = []
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i = 0
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for entity in entities:
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highlighted.extend(text[i:int(entity['start'])].split())
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entity_group = entity['entity_group']
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score = entity['score']
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marked_text = f'<mark class="{entity_group}" style="background-color: {entity_colors[entity_group]}">{entity["word"]}<sub>{entity_group}</sub><sup>{score:.2f}</sup></mark>'
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highlighted.append(marked_text)
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i = int(entity['end']) + 1
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highlighted.extend(text[i:].split())
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return HTML_WRAPPER.format(' '.join(highlighted))
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# Create the Gradio interface
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def predict_ner(text):
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try:
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text = preprocess_arabic_text(text)
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entities = token_classifier(text)
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highlighted_text = highlight_text(text, entities)
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return highlighted_text + '\n\n' + str(entities)
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except Exception as e:
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print(e)
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return str(e)
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iface = gr.Interface(
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fn=predict_ner,
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inputs=gr.inputs.Textbox(label="Enter Hadith in Arabic"),
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outputs=gr.outputs.HTML(label="Predicted Labels"),
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title="Hadith Analysis"
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)
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# Launch the interface
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iface.launch()
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requirements.txt
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transformers
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torch
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tashaphyne
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