readme: ---
  datasets:
  - tner/tweetner7
  metrics:
  - f1
  - precision
  - recall
  model-index:
  - name: tner/roberta-large-tweetner7-selflabel2020-continuous
    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: 0.6514522821576764
      - name: Precision (test_2021)
        type: precision
        value: 0.6323753537992598
      - name: Recall (test_2021)
        type: recall
        value: 0.6717160037002775
      - name: Macro F1 (test_2021)
        type: f1_macro
        value: 0.6022910652688035
      - name: Macro Precision (test_2021)
        type: precision_macro
        value: 0.5829347583676058
      - name: Macro Recall (test_2021)
        type: recall_macro
        value: 0.6268182581614908
      - name: Entity Span F1 (test_2021)
        type: f1_entity_span
        value: 0.787304435596927
      - name: Entity Span Precision (test_2020)
        type: precision_entity_span
        value: 0.7642064010450685
      - name: Entity Span Recall (test_2021)
        type: recall_entity_span
        value: 0.8118422574303227
      - name: F1 (test_2020)
        type: f1
        value: 0.667024993281376
      - name: Precision (test_2020)
        type: precision
        value: 0.6917502787068004
      - name: Recall (test_2020)
        type: recall
        value: 0.6440062272963155
      - name: Macro F1 (test_2020)
        type: f1_macro
        value: 0.6285598697810462
      - name: Macro Precision (test_2020)
        type: precision_macro
        value: 0.649215603090582
      - name: Macro Recall (test_2020)
        type: recall_macro
        value: 0.6128675304056594
      - name: Entity Span F1 (test_2020)
        type: f1_entity_span
        value: 0.7711750470556602
      - name: Entity Span Precision (test_2020)
        type: precision_entity_span
        value: 0.8002232142857143
      - name: Entity Span Recall (test_2020)
        type: recall_entity_span
        value: 0.7441619097042034
  
  pipeline_tag: token-classification
  widget:
  - text: "Get the all-analog Classic Vinyl Edition of `Takin' Off` Album from {@herbiehancock@} via {@bluenoterecords@} link below: {{URL}}"
    example_title: "NER Example 1"
  ---
  # tner/roberta-large-tweetner7-selflabel2020-continuous
  
  This model is a fine-tuned version of [tner/roberta-large-tweetner-2020](https://huggingface.co/tner/roberta-large-tweetner-2020) on the 
  [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) dataset (`train` split). This model is fine-tuned on self-labeled dataset which is the `extra_2020` split of the [tner/tweetner7](https://huggingface.co/datasets/tner/tweetner7) annotated by [tner/roberta-large](https://huggingface.co/tner/roberta-large-tweetner7-2020)). Please check [https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling](https://github.com/asahi417/tner/tree/master/examples/tweetner7_paper#model-fine-tuning-self-labeling) for more detail of reproducing the model.  The model is first fine-tuned on `train_2020`, and then continuously fine-tuned on the self-labeled 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 of 2021:
  - F1 (micro): 0.6514522821576764
  - Precision (micro): 0.6323753537992598
  - Recall (micro): 0.6717160037002775
  - F1 (macro): 0.6022910652688035
  - Precision (macro): 0.5829347583676058
  - Recall (macro): 0.6268182581614908
  
  
  
  The per-entity breakdown of the F1 score on the test set are below:
  - corporation: 0.5252837977296182
  - creative_work: 0.4650306748466258
  - event: 0.46176911544227883
  - group: 0.608667941363926
  - location: 0.6666666666666666
  - person: 0.8382696104828578
  - product: 0.6503496503496504 
  
  For F1 scores, the confidence interval is obtained by bootstrap as below:
  - F1 (micro): 
      - 90%: [0.6429569959405362, 0.6605302879870334]
      - 95%: [0.6410815271146394, 0.6628490227012314] 
  - F1 (macro): 
      - 90%: [0.6429569959405362, 0.6605302879870334]
      - 95%: [0.6410815271146394, 0.6628490227012314] 
  
  Full evaluation can be found at [metric file of NER](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous/raw/main/eval/metric.json) 
  and [metric file of entity span](https://huggingface.co/tner/roberta-large-tweetner7-selflabel2020-continuous/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
  ```
  [TweetNER7](https://huggingface.co/datasets/tner/tweetner7) pre-processed tweets where the account name and URLs are 
  converted into special formats (see the dataset page for more detail), so we process tweets accordingly and then run the model prediction as below.  
  
  ```python
  import re
  from urlextract import URLExtract
  from tner import TransformersNER
  
  extractor = URLExtract()
  
  de