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

arXiv: https://arxiv.org/abs/2209.12616 · HTML: https://mlsanity.com/paper/2209.12616/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 9 Sep 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://mlsanity.com/paper/2005.00052/) (2020)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://mlsanity.com/paper/2010.01057/) (2020)
- [Calibration of Pre-trained Transformers](https://mlsanity.com/paper/2003.07892/) (2020)
- [Pretrained Transformers Improve Out-of-Distribution Robustness](https://mlsanity.com/paper/2004.06100/) (2020)
- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [Dice Loss for Data-imbalanced NLP Tasks](https://mlsanity.com/paper/1911.02855/) (2019)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [CrossWeigh: Training Named Entity Tagger from Imperfect Annotations](https://mlsanity.com/paper/1909.01441/) (2019)
- [Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models](https://mlsanity.com/paper/2004.04123/) (2020)
- [Multi-Domain Adversarial Learning for Slot Filling in Spoken Language Understanding](https://mlsanity.com/paper/1711.11310/) (2017)

## Built on by (most cited first, 30 of 37)

- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022)
- [VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning](https://mlsanity.com/paper/2403.13164/) (2024)
- [Named Entity Recognition in Twitter: A Dataset and Analysis on Short-Term Temporal Shifts](https://mlsanity.com/paper/2210.03797/) (2022)
- [Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTI](https://mlsanity.com/paper/2211.01753/) (2022)
- [Named Entity Recognition in Indian court judgments](https://mlsanity.com/paper/2211.03442/) (2022)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding](https://mlsanity.com/paper/2308.10529/) (2023)
- [SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)](https://mlsanity.com/paper/2305.06586/) (2023)
- [MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks](https://mlsanity.com/paper/2404.00457/) (2024)
- [Generalists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks](https://mlsanity.com/paper/2410.22296/) (2024)
- [KPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents](https://mlsanity.com/paper/2210.09163/) (2022)
- [HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools](https://mlsanity.com/paper/2402.12372/) (2024)
- [Enhancing Human-Computer Interaction in Chest X-ray Analysis using Vision and Language Model with Eye Gaze Patterns](https://mlsanity.com/paper/2404.02370/) (2024)
- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)
- [Stylebreeder: Exploring and Democratizing Artistic Styles through Text-to-Image Models](https://mlsanity.com/paper/2406.14599/) (2024)
- [Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets](https://mlsanity.com/paper/2403.20056/) (2024)
- [LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature](https://mlsanity.com/paper/2312.07110/) (2023)
- [MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble Techniques and Data Augmentation for Climate Activism Stance and Hate Event Identification](https://mlsanity.com/paper/2402.01976/) (2024)
- [BeanCounter: A low-toxicity, large-scale, and open dataset of business-oriented text](https://mlsanity.com/paper/2409.17827/) (2024)
- [Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models](https://mlsanity.com/paper/2506.22813/) (2025)
- [PickleBall: Secure Deserialization of Pickle-based Machine Learning Models (Extended Report)](https://mlsanity.com/paper/2508.15987/) (2025)
- [Machine Reading Comprehension using Case-based Reasoning](https://mlsanity.com/paper/2305.14815/) (2023)
- [A Multilingual Evaluation of NER Robustness to Adversarial Inputs](https://mlsanity.com/paper/2305.18933/) (2023)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [MSNER: A Multilingual Speech Dataset for Named Entity Recognition](https://mlsanity.com/paper/2405.11519/) (2024)
- [AlbNER: A Corpus for Named Entity Recognition in Albanian](https://mlsanity.com/paper/2309.08741/) (2023)
- [Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution](https://mlsanity.com/paper/2503.03201/) (2025)
- [GlobalNER: Incorporating Non-local Information into Named Entity Recognition](https://mlsanity.com/paper/2303.02915/) (2023)
- [CUNI Submission to MRL 2023 Shared Task on Multi-lingual Multi-task Information Retrieval](https://mlsanity.com/paper/2310.16528/) (2023)

## Abstract (arXiv)

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.