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roberta-base-tweetner7-all

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metadata
datasets:
  - tner/tweetner7
metrics:
  - f1
  - precision
  - recall
model-index:
  - name: tner/roberta-base-tweetner7-all
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: tner/tweetner7
          type: tner/tweetner7
          args: tner/tweetner7
        metrics:
          - name: F1 (test_2021)
            type: f1
            value: 0.6515831894070236
          - name: Precision (test_2021)
            type: precision
            value: 0.6488190781930749
          - name: Recall (test_2021)
            type: recall
            value: 0.6543709528214616
          - name: Macro F1 (test_2021)
            type: f1_macro
            value: 0.6081318073591985
          - name: Macro Precision (test_2021)
            type: precision_macro
            value: 0.6024892144112918
          - name: Macro Recall (test_2021)
            type: recall_macro
            value: 0.6155807376978756
          - name: Entity Span F1 (test_2021)
            type: f1_entity_span
            value: 0.7893373251194657
          - name: Entity Span Precision (test_2020)
            type: precision_entity_span
            value: 0.7859435909195138
          - name: Entity Span Recall (test_2021)
            type: recall_entity_span
            value: 0.7927604949693535
          - name: F1 (test_2020)
            type: f1
            value: 0.6531839300355288
          - name: Precision (test_2020)
            type: precision
            value: 0.6899538106235565
          - name: Recall (test_2020)
            type: recall
            value: 0.6201349247535028
          - name: Macro F1 (test_2020)
            type: f1_macro
            value: 0.6166186507300974
          - name: Macro Precision (test_2020)
            type: precision_macro
            value: 0.6523781324413148
          - name: Macro Recall (test_2020)
            type: recall_macro
            value: 0.5860926262979317
          - name: Entity Span F1 (test_2020)
            type: f1_entity_span
            value: 0.7523236741388737
          - name: Entity Span Precision (test_2020)
            type: precision_entity_span
            value: 0.7949162333911034
          - name: Entity Span Recall (test_2020)
            type: recall_entity_span
            value: 0.7140633108458744
pipeline_tag: token-classification
widget:
  - text: >-
      Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from
      {{@Herbie Hancock@}} via {{USERNAME}} link below: {{URL}}
    example_title: NER Example 1

tner/roberta-base-tweetner7-all

This model is a fine-tuned version of roberta-base on the tner/tweetner7 dataset (train_all split). 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 of 2021:

  • F1 (micro): 0.6515831894070236
  • Precision (micro): 0.6488190781930749
  • Recall (micro): 0.6543709528214616
  • F1 (macro): 0.6081318073591985
  • Precision (macro): 0.6024892144112918
  • Recall (macro): 0.6155807376978756

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

  • corporation: 0.5174234424498415
  • creative_work: 0.466403162055336
  • event: 0.46727272727272723
  • group: 0.6071197411003236
  • location: 0.6832786885245901
  • person: 0.8377301195672804
  • product: 0.6776947705442904

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

  • F1 (micro):
    • 90%: [0.6426248846161623, 0.6611146727643068]
    • 95%: [0.6408583849998567, 0.6629609445072536]
  • F1 (macro):
    • 90%: [0.6426248846161623, 0.6611146727643068]
    • 95%: [0.6408583849998567, 0.6629609445072536]

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-base-tweetner7-all")
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/tweetner7']
  • dataset_split: train_all
  • dataset_name: None
  • local_dataset: None
  • model: roberta-base
  • crf: True
  • max_length: 128
  • epoch: 30
  • batch_size: 32
  • lr: 1e-05
  • random_seed: 0
  • gradient_accumulation_steps: 1
  • weight_decay: 1e-07
  • lr_warmup_step_ratio: 0.3
  • max_grad_norm: 1

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.",
}