azaad@BND at SemEval-2023 Task 2: How to Go from a Simple Transformer Model to a Better Model to Get Better Results in Natural Language Processing
Reza Ahmadi, Shiva Arefi, Mohammad Jafarabad
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 2: multiconer ii multilingual complex named entity recognition Paper
TLDR:
In this article, which was prepared for the sameval2023 competition (task number 2), information about the implementation techniques of the transformer model and the use of the pre-trained BERT model in order to identify the named entity (NER) in the English language, has been collected and also the
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Abstract:
In this article, which was prepared for the sameval2023 competition (task number 2), information about the implementation techniques of the transformer model and the use of the pre-trained BERT model in order to identify the named entity (NER) in the English language, has been collected and also the implementation method is explained.Finally, it led to an F1 score of about 57\% for Fine-grained and 72\% for Coarse-grained in the dev data.In the final test data, F1 score reached 50\%.