T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

• URL: http://arxiv.org/abs/2209.12616v1
• Date: Fri, 9 Sep 2022 15:00:38 GMT
• Title: T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition
• Authors: Asahi Ushio, Jose Camacho-Collados
• Abstract summary: T-NER is a Python library for NER LM finetuning. We show the potential of the library by compiling nine public NER datasets into a unified format. To facilitate future research, we also release all our LM checkpoints via the Hugging Face model hub.
• Score: 9.928025283928282
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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