تقدم هذه الورقة تقديم Duluthnlp إلى المهمة 7 من مسابقة Semeval 2021 بشأن الكشف عن الفكاهة والجريمة تصنيفها.في ذلك، نوضح النهج المستخدم لتدريب النموذج مع عملية ضبط النموذج الخاص بنا في الحصول على النتائج.ونحن نركز على الكشف عن الفكاهة والتصنيف والتصنيف الفاسد، وهو ما يمثل ثلاثة من الأساس الأربع الفرعية التي قدمت.نظهر أن تحسين المعلمات فرطا لمعدل التعلم، يمكن أن يزيد حجم الدفعة وعدد EFOCHs من الدقة ونتيجة F1 للكشف عن الفكاهة
This paper presents the DuluthNLP submission to Task 7 of the SemEval 2021 competition on Detecting and Rating Humor and Offense. In it, we explain the approach used to train the model together with the process of fine-tuning our model in getting the results. We focus on humor detection, rating, and of-fense rating, representing three out of the four subtasks that were provided. We show that optimizing hyper-parameters for learning rate, batch size and number of epochs can increase the accuracy and F1 score for humor detection
References used
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