MarSan at SemEval-2022 Task 11: Multilingual complex named entity recognition using T5 and transformer encoder

The multilingual complex named entity recog- nition task of SemEval2020 required partici- pants to detect semantically ambiguous and complex entities in 11 languages. In order to participate in this competition, a deep learning model is being used with the T5 text-to-text lan- guage model and its multilingual version, MT5, along with the transformer’s encoder module. The subtoken check has also been introduced, resulting in a 4% increase in the model F1- score in English. We also examined the use of the BPEmb model for converting input tokens to representation vectors in this research. A performance evaluation of the proposed entity detection model is presented at the end of this paper. Six different scenarios were defined, and the proposed model was evaluated in each sce- nario within the English development set. Our model is also evaluated in other languages.

the paper for this implementation can be found here

Usages

Train Model:

python trainer.py

Test Model

python inferencer.py