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。

T-NER：基于Transformer的命名实体识别通用Python库