metadata

datasets:
  - tner/tweebank_ner
metrics:
  - f1
  - precision
  - recall
model-index:
  - name: tner/deberta-v3-large-tweebank-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: tner/tweebank_ner
          type: tner/tweebank_ner
          args: tner/tweebank_ner
        metrics:
          - name: F1
            type: f1
            value: 0.7253474520185308
          - name: Precision
            type: precision
            value: 0.7201051248357424
          - name: Recall
            type: recall
            value: 0.7306666666666667
          - name: F1 (macro)
            type: f1_macro
            value: 0.701874697798745
          - name: Precision (macro)
            type: precision_macro
            value: 0.7043005470796733
          - name: Recall (macro)
            type: recall_macro
            value: 0.706915721861374
          - name: F1 (entity span)
            type: f1_entity_span
            value: 0.8178343949044585
          - name: Precision (entity span)
            type: precision_entity_span
            value: 0.7829268292682927
          - name: Recall (entity span)
            type: recall_entity_span
            value: 0.856
pipeline_tag: token-classification
widget:
  - text: Jacob Collier is a Grammy awarded artist from England.
    example_title: NER Example 1

tner/deberta-v3-large-tweebank-ner

This model is a fine-tuned version of microsoft/deberta-v3-large on the tner/tweebank_ner 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): 0.7253474520185308
Precision (micro): 0.7201051248357424
Recall (micro): 0.7306666666666667
F1 (macro): 0.701874697798745
Precision (macro): 0.7043005470796733
Recall (macro): 0.706915721861374

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

location: 0.7289719626168224
organization: 0.7040816326530612
other: 0.5182926829268293
person: 0.856152512998267

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

F1 (micro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]
F1 (macro):
- 90%: [0.6978100031831928, 0.7529703029130037]
- 95%: [0.691700704571692, 0.7582901338971108]

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/deberta-v3-large-tweebank-ner")
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/tweebank_ner']
dataset_split: train
dataset_name: None
local_dataset: None
model: microsoft/deberta-v3-large
crf: True
max_length: 128
epoch: 15
batch_size: 16
lr: 1e-05
random_seed: 42
gradient_accumulation_steps: 4
weight_decay: 1e-07
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.",
}