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

EACL 2021  ·  Asahi Ushio, Jose Camacho-Collados ·

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.

PDF Abstract EACL 2021 PDF EACL 2021 Abstract
Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Named Entity Recognition (NER) WNUT 2017 TNER -xlm-r-large F1 58.5 # 4