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        <title>T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition</title>
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        <namePart type="given">Asahi</namePart>
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            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations</title>
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        <name type="personal">
            <namePart type="given">Dimitra</namePart>
            <namePart type="family">Gkatzia</namePart>
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            <namePart type="given">Djamé</namePart>
            <namePart type="family">Seddah</namePart>
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    <abstract>Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-demos.7</identifier>
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%0 Conference Proceedings
%T T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
%A Ushio, Asahi
%A Camacho-Collados, Jose
%Y Gkatzia, Dimitra
%Y Seddah, Djamé
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F ushio-camacho-collados-2021-ner
%X Language model (LM) pretraining has led to consistent improvements in many NLP downstream tasks, including named entity recognition (NER). In this paper, we present T-NER (Transformer-based Named Entity Recognition), a Python library for NER LM finetuning. In addition to its practical utility, T-NER facilitates the study and investigation of the cross-domain and cross-lingual generalization ability of LMs finetuned on NER. Our library also provides a web app where users can get model predictions interactively for arbitrary text, which facilitates qualitative model evaluation for non-expert programmers. We show the potential of the library by compiling nine public NER datasets into a unified format and evaluating the cross-domain and cross- lingual performance across the datasets. The results from our initial experiments show that in-domain performance is generally competitive across datasets. However, cross-domain generalization is challenging even with a large pretrained LM, which has nevertheless capacity to learn domain-specific features if fine- tuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
%R 10.18653/v1/2021.eacl-demos.7
%U https://aclanthology.org/2021.eacl-demos.7
%U https://doi.org/10.18653/v1/2021.eacl-demos.7
%P 53-62

Markdown (Informal)

[T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition](https://aclanthology.org/2021.eacl-demos.7) (Ushio & Camacho-Collados, EACL 2021)

• T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (Ushio & Camacho-Collados, EACL 2021)

ACL

• Asahi Ushio and Jose Camacho-Collados. 2021. T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations, pages 53–62, Online. Association for Computational Linguistics.