tner/roberta-large-ontonotes5

This model is a fine-tuned version of roberta-large on the tner/ontonotes5 dataset. Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set:

• F1 (micro): 0.908632361399938
• Precision (micro): 0.905148095909732
• Recall (micro): 0.9121435551212579
• F1 (macro): 0.8265477704565624
• Precision (macro): 0.8170668848546687
• Recall (macro): 0.8387672780349001

The per-entity breakdown of the F1 score on the test set are below:

• cardinal_number: 0.8605277329025309
• date: 0.872996300863132
• event: 0.7424242424242424
• facility: 0.7732342007434945
• geopolitical_area: 0.9687148323205043
• group: 0.9470588235294117
• language: 0.7499999999999999
• law: 0.6666666666666666
• location: 0.7593582887700535
• money: 0.901098901098901
• ordinal_number: 0.85785536159601
• organization: 0.9227360841872057
• percent: 0.9171428571428571
• person: 0.9556004036326943
• product: 0.7857142857142858
• quantity: 0.7945205479452055
• time: 0.6870588235294116
• work_of_art: 0.7151515151515151

For F1 scores, the confidence interval is obtained by bootstrap as below:

• F1 (micro):
  • 90%: [0.9039454247544766, 0.9128956119702822]
  • 95%: [0.9030263216115454, 0.9138350859566045]
• F1 (macro):
  • 90%: [0.9039454247544766, 0.9128956119702822]
  • 95%: [0.9030263216115454, 0.9138350859566045]

Full evaluation can be found at metric file of NER and metric file of entity span.

Usage

This model can be used through the tner library. Install the library via pip

pip install tner

and activate model as below.

from tner import TransformersNER
model = TransformersNER("tner/roberta-large-ontonotes5")
model.predict(["Jacob Collier is a Grammy awarded English artist from London"])

It can be used via transformers library but it is not recommended as CRF layer is not supported at the moment.

Training hyperparameters

The following hyperparameters were used during training:

• dataset: ['tner/ontonotes5']
• dataset_split: train
• dataset_name: None
• local_dataset: None
• model: roberta-large
• crf: True
• max_length: 128
• epoch: 15
• batch_size: 64
• lr: 1e-05
• random_seed: 42
• gradient_accumulation_steps: 1
• weight_decay: None
• lr_warmup_step_ratio: 0.1
• max_grad_norm: 10.0

The full configuration can be found at fine-tuning parameter file.

Reference

If you use any resource from T-NER, please consider to cite our paper.

@inproceedings{ushio-camacho-collados-2021-ner,
    title = "{T}-{NER}: An All-Round Python Library for Transformer-based Named Entity Recognition",
    author = "Ushio, Asahi  and
      Camacho-Collados, Jose",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.7",
    doi = "10.18653/v1/2021.eacl-demos.7",
    pages = "53--62",
    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.",
}