readme: ---
  datasets:
  - tner/tweebank_ner
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: tner/roberta-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: 0.7439490445859872
      - name: Precision
        type: precision
        value: 0.7121951219512195
      - name: Recall
        type: recall
        value: 0.7786666666666666
      - name: F1 (macro)
        type: f1_macro
        value: 0.7354319457314183
      - name: Precision (macro)
        type: precision_macro
        value: 0.712928566565599
      - name: Recall (macro)
        type: recall_macro
        value: 0.7620465365030582
      - 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/roberta-large-tweebank-ner
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the 
  [tner/tweebank_ner](https://huggingface.co/datasets/tner/tweebank_ner) 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): 0.7439490445859872
  - Precision (micro): 0.7121951219512195
  - Recall (micro): 0.7786666666666666
  - F1 (macro): 0.7354319457314183
  - Precision (macro): 0.712928566565599
  - Recall (macro): 0.7620465365030582
  
  The per-entity breakdown of the F1 score on the test set are below:
  - location: 0.7782805429864253
  - organization: 0.7377049180327869
  - other: 0.5520581113801453
  - person: 0.8736842105263157 
  
  For F1 scores, the confidence interval is obtained by bootstrap as below:
  - F1 (micro): 
      - 90%: [0.7156413818791614, 0.771698046498159]
      - 95%: [0.7063867669973017, 0.7763088810979543] 
  - F1 (macro): 
      - 90%: [0.7156413818791614, 0.771698046498159]
      - 95%: [0.7063867669973017, 0.7763088810979543] 
  
  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweebank-ner/raw/main/eval/metric.json) 
  and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweebank-ner/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/roberta-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: roberta-large
   - crf: True
   - max_length: 128
   - epoch: 15
   - 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](https://huggingface.co/tner/roberta-large-tweebank-ner/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, which facilitates qualitative model evaluation for non-expert pro