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.- Anthology ID:
- 2021.eacl-demos.7
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 53–62
- Language:
- URL:
- https://aclanthology.org/2021.eacl-demos.7
- DOI:
- 10.18653/v1/2021.eacl-demos.7
- Cite (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.
- Cite (Informal):
- T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition (Ushio & Camacho-Collados, EACL 2021)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2021.eacl-demos.7.pdf
- Code
- asahi417/tner
- Data
- CoNLL++, CoNLL-2003, FIN, WNUT 2017