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2021-04, EACL, T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

2021-04, EACL, T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

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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.

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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2021-04, EACL, T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition

  1. 1. T-NER An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio Jose Camacho-Collados Cardiff University School of Computer Science and Informatics Presented at EACL 2021 https://github.com/asahi417/tner https://pypi.org/project/tner
  2. 2. Language Model Pretraining & Finetuning T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (Devlin, Jacob, et al., 2018) Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Raffel, Colin, et al. 2020) Improving language understanding by generative pre-training (Radford, Alec, et al., 2018) 2
  3. 3. Named Entity Recognition T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Jacob Collier is an English artist. Person Location 3
  4. 4. Named Entity Recognition T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Jacob Collier is an English artist. 4
  5. 5. Named Entity Recognition T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Jacob Collier is an English artist. Jacob is English an artist. Collier Tokenization 5
  6. 6. Named Entity Recognition T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Jacob Collier is an English artist. BERT + Linear Projection Jacob is English an artist. PJacob PCollier Pan PEnglish Partist Pis Collier Tokenization Location Person 6
  7. 7. Implement NER System T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Unify Tagging Scheme - IOB, IOB2, IOBES, etc Jean Auguste Dominique Ingres B-Person I-Person I-Person I-Person Jean Auguste Dominique Ingres Person B-Person I-Person I-Person E-Person IOBES IOB 7
  8. 8. Implement NER System T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Unify Tagging Scheme - IOB, IOB2, IOBES, etc Fix Sequence Mismatch - Algine label sequence to model tokenization Jean Auguste Dominique Ingres was a French painter. B-Person I-Person I-Person I-Person Jean August e Dominique Ingres was a French painter. B-Location Dataset RoBERTa Tokenization B-Person I-Person I-Person I-Person I-Person I-Person I-Person B-Location Jean Auguste Dominique Ingres Jean Auguste Dominique Ingres Person B-Person I-Person I-Person I-Person B-Person I-Person I-Person E-Person IOBES IOB 8
  9. 9. T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados Unify Tagging Scheme - IOB, IOB2, IOBES, etc Fix Sequence Mismatch - Algine label sequence to model tokenization Jean Auguste Dominique Ingres was a French painter. B-Person I-Person I-Person I-Person Jean August e Dominique Ingres was a French painter. B-Location Dataset B-Person I-Person I-Person I-Person I-Person I-Person I-Person B-Location Jean Auguste Dominique Ingres Jean Auguste Dominique Ingres Person Evaluate in Cross-domain - Dataset specific entity definition BioNLP2004 ● Protein ● Cell type ● RNA WNUT2017 ● Person ● Corporation ● Creative work Implement NER System B-Person I-Person I-Person I-Person B-Person I-Person I-Person E-Person IOBES IOB RoBERTa Tokenization 9
  10. 10. NLP Open Source Softwares T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados 10
  11. 11. T-NER🌳
  12. 12. Overall T-NER Design T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados LM finetuning OntoNotes 5 CoNLL 2003 BioNLP 2004 WNUT 2017 WikiAnn ... Datasets NER model web APP LM evaluation *cross-domain *cross-lingual 46 finetuned NER models released in model hub !! ● IOB format ● Sequence mismatch fixed Upload/download model Notebook link ● Finetuning ● Evaluation ● Model prediction ● Multilingual NER 12
  13. 13. Web Application T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados # SETUP >>> git clone https://github.com/asahi417/tner >>> cd tner >>> pip install . # RUN APPLICATION at http://0.0.0.0:8000/ >>> export NER_MODEL=’asahi417/tner-xlm-roberta-large-ontonotes5’ >>> uvicorn app:app --reload --log-level debug --host 0.0.0.0 --port 8000 13
  14. 14. T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados 14
  15. 15. Experimental Results T-NER: An All-Round Python Library for Transformer-based Named Entity Recognition Asahi Ushio and Jose Camacho-Collados 15
  16. 16. 🌳Thank you!🌳

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