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.

本文介绍了一种基于 Transformer 的 python 库 T-NER，用于命名实体识别的 LM finetuning，并展示了其在跨领域和跨语言泛化方面的研究和应用潜力，同时提供了一个 Web 应用程序，使得非专业编程人员能够与模型进行交互，进一步促进模型质量的定性评估。研究结果表明，即使利用预训练的大型 LM 进行 fine-tuning，跨领域泛化仍然有挑战，但与数据集的相关性更强的特定领域的性能通常具有可比性。此外，我们还通过 Hugging Face 模型沙盒发布了所有的 LM checkpoints 来促进未来研究。