@inproceedings{ahmadi-etal-2023-azaad,
title = "azaad@{BND} at {S}em{E}val-2023 Task 2: How to Go from a Simple Transformer Model to a Better Model to Get Better Results in Natural Language Processing",
author = "Ahmadi, Reza and
Arefi, Shiva and
Jafarabad, Mohammad",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.303",
doi = "10.18653/v1/2023.semeval-1.303",
pages = "2184--2187",
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{\%}.",
}
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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%.</abstract>
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%0 Conference Proceedings
%T 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
%A Ahmadi, Reza
%A Arefi, Shiva
%A Jafarabad, Mohammad
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F ahmadi-etal-2023-azaad
%X 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%.
%R 10.18653/v1/2023.semeval-1.303
%U https://aclanthology.org/2023.semeval-1.303
%U https://doi.org/10.18653/v1/2023.semeval-1.303
%P 2184-2187
Markdown (Informal)
[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](https://aclanthology.org/2023.semeval-1.303) (Ahmadi et al., SemEval 2023)
ACL