readme: ---
  datasets:
  - tner/wnut2017
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: tner/deberta-v3-large-wnut2017
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: tner/wnut2017
        type: tner/wnut2017
        args: tner/wnut2017
      metrics:
      - name: F1
        type: f1
        value: 0.5047353760445682
      - name: Precision
        type: precision
        value: 0.63268156424581
      - name: Recall
        type: recall
        value: 0.4198331788693234
      - name: F1 (macro)
        type: f1_macro
        value: 0.4165125500830091
      - name: Precision (macro)
        type: precision_macro
        value: 0.5356144444686111
      - name: Recall (macro)
        type: recall_macro
        value: 0.3573954549633822
      - name: F1 (entity span)
        type: f1_entity_span
        value: 0.6249999999999999
      - name: Precision (entity span)
        type: precision_entity_span
        value: 0.7962697274031564
      - name: Recall (entity span)
        type: recall_entity_span
        value: 0.5143651529193698
  
  pipeline_tag: token-classification
  widget:
  - text: "Jacob Collier is a Grammy awarded artist from England."
    example_title: "NER Example 1"
  ---
  # tner/deberta-v3-large-wnut2017
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
  [tner/wnut2017](https://huggingface.co/datasets/tner/wnut2017) dataset.
  Model fine-tuning is done via [T-NER](https://github.com/asahi417/tner)'s hyper-parameter search (see the repository
  for more detail). It achieves the following results on the test set:
  - F1 (micro): 0.5047353760445682
  - Precision (micro): 0.63268156424581
  - Recall (micro): 0.4198331788693234
  - F1 (macro): 0.4165125500830091
  - Precision (macro): 0.5356144444686111
  - Recall (macro): 0.3573954549633822
  
  The per-entity breakdown of the F1 score on the test set are below:
  - corporation: 0.25477707006369427
  - group: 0.34309623430962344
  - location: 0.6187050359712232
  - person: 0.6721763085399448
  - product: 0.18579234972677597
  - work_of_art: 0.42452830188679247 
  
  For F1 scores, the confidence interval is obtained by bootstrap as below:
  - F1 (micro): 
      - 90%: [0.4752384997212858, 0.5329114690850492]
      - 95%: [0.46929053844001617, 0.537282841423422] 
  - F1 (macro): 
      - 90%: [0.4752384997212858, 0.5329114690850492]
      - 95%: [0.46929053844001617, 0.537282841423422] 
  
  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric.json) 
  and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/eval/metric_span.json).
  
  ### Usage
  This model can be used through the [tner library](https://github.com/asahi417/tner). Install the library via pip   
  ```shell
  pip install tner
  ```
  and activate model as below.
  ```python
  from tner import TransformersNER
  model = TransformersNER("tner/deberta-v3-large-wnut2017")
  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/wnut2017']
   - dataset_split: train
   - dataset_name: None
   - local_dataset: None
   - model: microsoft/deberta-v3-large
   - crf: False
   - 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](https://huggingface.co/tner/deberta-v3-large-wnut2017/raw/main/trainer_config.json).
  
  ### Reference
  If you use any resource from T-NER, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
  
  ```
  
  @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 interactivel