readme: ---
  datasets:
  - tner/bionlp2004
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: tner/deberta-v3-large-bionlp2004
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: tner/bionlp2004
        type: tner/bionlp2004
        args: tner/bionlp2004
      metrics:
      - name: F1
        type: f1
        value: 0.758624442267929
      - name: Precision
        type: precision
        value: 0.7174763277068753
      - name: Recall
        type: recall
        value: 0.8047794966520434
      - name: F1 (macro)
        type: f1_macro
        value: 0.7195387988303987
      - name: Precision (macro)
        type: precision_macro
        value: 0.681309505763584
      - name: Recall (macro)
        type: recall_macro
        value: 0.7691804743892025
      - name: F1 (entity span)
        type: f1_entity_span
        value: 0.796539152201121
      - name: Precision (entity span)
        type: precision_entity_span
        value: 0.7533710756562018
      - name: Recall (entity span)
        type: recall_entity_span
        value: 0.8449549757561764
  
  pipeline_tag: token-classification
  widget:
  - text: "Jacob Collier is a Grammy awarded artist from England."
    example_title: "NER Example 1"
  ---
  # tner/deberta-v3-large-bionlp2004
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the 
  [tner/bionlp2004](https://huggingface.co/datasets/tner/bionlp2004) 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): 0.758624442267929
  - Precision (micro): 0.7174763277068753
  - Recall (micro): 0.8047794966520434
  - F1 (macro): 0.7195387988303987
  - Precision (macro): 0.681309505763584
  - Recall (macro): 0.7691804743892025
  
  The per-entity breakdown of the F1 score on the test set are below:
  - cell_line: 0.6465517241379309
  - cell_type: 0.7562483203439935
  - dna: 0.7449506810709253
  - protein: 0.7757859652283577
  - rna: 0.6741573033707865 
  
  For F1 scores, the confidence interval is obtained by bootstrap as below:
  - F1 (micro): 
      - 90%: [0.7508836679942893, 0.7667327003308145]
      - 95%: [0.7498144548458301, 0.7680807868080707] 
  - F1 (macro): 
      - 90%: [0.7508836679942893, 0.7667327003308145]
      - 95%: [0.7498144548458301, 0.7680807868080707] 
  
  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/deberta-v3-large-bionlp2004/raw/main/eval/metric.json) 
  and [metric file of entity span](https://huggingface.co/tner/deberta-v3-large-bionlp2004/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-bionlp2004")
  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/bionlp2004']
   - 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: 8
   - weight_decay: 1e-07
   - lr_warmup_step_ratio: 0.1
   - max_grad_norm: None
  
  The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/tner/deberta-v3-large-bionlp2004/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 interactively for arbitrary text