RamiIbrahim
commited on
Commit
•
55cd195
1
Parent(s):
488621e
Stable version 2
Browse files
app.py
CHANGED
@@ -26,7 +26,7 @@ def get_example_predictions(examples):
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# Example texts
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examples = [
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["
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["ma7abitch el mekla mte3 el restaurant"],
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["el jaw fi tounes a7la 7aja"],
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["ennes el kol te3ba w ma3andhomch flous"],
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@@ -72,28 +72,25 @@ iface = gr.Interface(
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</ul>
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<p>Try the examples below or enter your own text!</p>
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<p>!جرب الأمثلة
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""",
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article="""
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<h3>About the Model
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<p>This sentiment analysis model was trained on a combined dataset from TuniziDataset and the Tunisian Dialect Corpus.
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It uses TF-IDF vectorization for feature extraction and Logistic Regression for classification.</p>
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<p>تم تدريب نموذج تحليل المشاعر هذا على مجموعة بيانات مجمعة من TuniziDataset ومجموعة اللهجة التونسية.
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يستخدم التجزئة TF-IDF لاستخراج الميزات والانحدار اللوجستي للتصنيف.</p>
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<p>The model accepts
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<p>يقبل النموذج كلاً من النص العربيزي التونسي والنص العربي القياسي.</p>
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<h3>Limitations
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<p>Due to dataset limitations, neutral sentiment data was removed to achieve maximum performance.
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This model is open-source, and contributions of additional datasets are welcome to improve its capabilities.</
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<p>The model may not perform well on very colloquial expressions or new slang terms not present in the training data.
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Sentiment can be nuanced and context-dependent, which may not always be captured accurately by this model.</p>
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<p>قد لا يؤدي النموذج بشكل جيد مع التعبيرات العامية جدًا أو المصطلحات الجديدة غير الموجودة في بيانات التدريب.
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يمكن أن تكون المشاعر دقيقة ومعتمدة على السياق، وهو ما قد لا يتم التقاطه بدقة دائمًا بواسطة هذا النموذج.</p>
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"""
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)
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# Example texts
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examples = [
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["3jebni barcha el film hedha"],
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["ma7abitch el mekla mte3 el restaurant"],
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["el jaw fi tounes a7la 7aja"],
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["ennes el kol te3ba w ma3andhomch flous"],
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</ul>
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<p>Try the examples below or enter your own text!</p>
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<p>!جرب الأمثلة أو أدخل نصك الخاص</p>
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""",
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article="""
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<h3>About the Model</h3>
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<p>This sentiment analysis model was trained on a combined dataset from TuniziDataset and the Tunisian Dialect Corpus.
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It uses TF-IDF vectorization for feature extraction and Logistic Regression for classification.</p>
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<p>The model accepts Tunisian Arabiz written with Latin and Arabic script.</p>
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<h3>Limitations</h3>
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<p>Due to dataset limitations, neutral sentiment data was removed to achieve maximum performance. </p>
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<h3>This model is open-source, and contributions of additional datasets are welcome to improve its capabilities.</h3>
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<h3>هذا النموذج مفتوح المصدر، ونرحب بمساهمات مجموعات البيانات الإضافية لتحسين قدراته.</h3>
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<p>The model may not perform well on very colloquial expressions or new slang terms not present in the training data.
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Sentiment can be nuanced and context-dependent, which may not always be captured accurately by this model.</p>
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"""
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)
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