Transformer Model to Evaluate Subjective Script
DOI:
https://doi.org/10.5281/zenodo.14557335Keywords:
Positional Encoding, Word vector Embedding, Attention Mechanism, Feed Forward neural networkAbstract
Boards and institutions frequently provide offline subjective exams to a significant student body. It would be highly time-consuming to manually look into such a big volume of papers. There have been initiatives in the past to grade student responses utilising computer science. However, most of these efforts rely on typical counts or particular word counts to achieve the goal. On the contrary, the proposed method employs a Transformer Neural network model is used to process natural language. Components of Transformer model are position encoding, Word vector embedding, Attentionmechanism and Feed forward neural network. Various Natural Language Processing techniques such as Tokenization,Word2Vec, Lemmatization and Semantic Checking.
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Copyright (c) 2024 Madhushree, Lavanya N L, Shashi Rekha G, Nagarathna C
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