T-NER: An all-round python library for transformer-based named entity recognition

47Citations
Citations of this article
95Readers
Mendeley users who have this article in their library.

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-NER1 (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 finetuned on a combined dataset. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.

Cite

CITATION STYLE

APA

Ushio, A., & Camacho-Collados, J. (2021). T-NER: An all-round python library for transformer-based named entity recognition. In EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the System Demonstrations (pp. 53–62). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.eacl-demos.7

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free