T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
This work provides a practical tool for researchers and practitioners in NLP to study NER generalization, but it is incremental as it builds on existing transformer methods without introducing new algorithmic innovations.
The authors tackled the problem of evaluating cross-domain and cross-lingual generalization in named entity recognition by developing T-NER, a Python library for transformer-based NER fine-tuning, and found that cross-domain generalization remains challenging even with large pretrained models, though performance is competitive within domains.
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.