metadata

datasets:
  - tner/conll2003
metrics:
  - f1
  - precision
  - recall
pipeline_tag: token-classification
widget:
  - text: Jacob Collier is a Grammy awarded artist from England.
    example_title: NER Example 1
base_model: roberta-large
model-index:
  - name: tner/roberta-large-conll2003
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: tner/conll2003
          type: tner/conll2003
          args: tner/conll2003
        metrics:
          - type: f1
            value: 0.924769027716674
            name: F1
          - type: precision
            value: 0.9191883855168795
            name: Precision
          - type: recall
            value: 0.9304178470254958
            name: Recall
          - type: f1_macro
            value: 0.9110950780089749
            name: F1 (macro)
          - type: precision_macro
            value: 0.9030546238754271
            name: Precision (macro)
          - type: recall_macro
            value: 0.9197126371122274
            name: Recall (macro)
          - type: f1_entity_span
            value: 0.9619852164730729
            name: F1 (entity span)
          - type: precision_entity_span
            value: 0.9562631210636809
            name: Precision (entity span)
          - type: recall_entity_span
            value: 0.9677762039660056
            name: Recall (entity span)

tner/roberta-large-conll2003

This model is a fine-tuned version of roberta-large on the tner/conll2003 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): 0.924769027716674
Precision (micro): 0.9191883855168795
Recall (micro): 0.9304178470254958
F1 (macro): 0.9110950780089749
Precision (macro): 0.9030546238754271
Recall (macro): 0.9197126371122274

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

location: 0.9390573401380967
organization: 0.9107142857142857
other: 0.8247422680412372
person: 0.9698664181422801

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

F1 (micro):
- 90%: [0.9185189408755685, 0.9309806929048586]
- 95%: [0.9174010190551032, 0.9318590917100465]
F1 (macro):
- 90%: [0.9185189408755685, 0.9309806929048586]
- 95%: [0.9174010190551032, 0.9318590917100465]

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-conll2003")
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/conll2003']
dataset_split: train
dataset_name: None
local_dataset: None
model: roberta-large
crf: True
max_length: 128
epoch: 17
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.",
}