readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  - named-entity-recognition
  - token-classification
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: fine_tune_bertweet-base-lp-ft
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        args: semval
      metrics:
      - name: Precision
        type: precision
        value: 0.6154830454254638
      - name: Recall
        type: recall
        value: 0.49844559585492226
      - name: F1
        type: f1
        value: 0.5508159175493844
      - name: Accuracy
        type: accuracy
        value: 0.9499198834668608
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Bertweet-base finetuned on wnut17_ner
  
  This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on the [wnut_17](https://huggingface.co/datasets/wnut_17) dataset.
  
  It achieves the following results on the evaluation set:
  - Loss: 0.3376
  - Overall Precision: 0.6803
  - Overall Recall: 0.6096
  - Overall F1: 0.6430
  - Overall Accuracy: 0.9509
  - Corporation F1: 0.2975
  - Creative-work F1: 0.4436
  - Group F1: 0.3624
  - Location F1: 0.6834
  - Person F1: 0.7902
  - Product F1: 0.3887
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 1e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Corporation F1 | Creative-work F1 | Group F1 | Location F1 | Person F1 | Product F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:--------:|:-----------:|:---------:|:----------:|
  | 0.0215        | 1.0   | 213  | 0.2913          | 0.7026            | 0.5905         | 0.6417     | 0.9507           | 0.2832         | 0.4444           | 0.2975   | 0.6854      | 0.7788    | 0.4015     |
  | 0.0213        | 2.0   | 426  | 0.3052          | 0.6774            | 0.5772         | 0.6233     | 0.9495           | 0.2830         | 0.3483           | 0.3231   | 0.6857      | 0.7728    | 0.3794     |
  | 0.0288        | 3.0   | 639  | 0.3378          | 0.7061            | 0.5507         | 0.6188     | 0.9467           | 0.3077         | 0.4184           | 0.3529   | 0.6222      | 0.7532    | 0.3910     |
  | 0.0124        | 4.0   | 852  | 0.2712          | 0.6574            | 0.6121         | 0.6340     | 0.9502           | 0.3077         | 0.4842           | 0.3167   | 0.6809      | 0.7735    | 0.3986     |
  | 0.0208        | 5.0   | 1065 | 0.2905          | 0.7108            | 0.6063         | 0.6544     | 0.9518           | 0.3063         | 0.4286           | 0.3419   | 0.7052      | 0.7913    | 0.4223     |
  | 0.0071        | 6.0   | 1278 | 0.3189          | 0.6756            | 0.5847         | 0.6269     | 0.9494           | 0.2759         | 0.4380           | 0.3256   | 0.6744      | 0.7781    | 0.3779     |
  | 0.0073        | 7.0   | 1491 | 0.3593          | 0.7330            | 0.5540         | 0.6310     | 0.9476           | 0.3061         | 0.4388           | 0.3784   | 0.6946      | 0.7631    | 0.3374     |
  | 0.0135        | 8.0   | 1704 | 0.3564          | 0.6875            | 0.5482         | 0.6100     | 0.9471           | 0.34           | 0.4179           | 0.3088   | 0.6632      | 0.7486    | 0.3695     |
  | 0.0097        | 9.0   | 1917 | 0.3085          | 0.6598            | 0.6395         | 0.6495     | 0.9516           | 0.3111         | 0.4609           | 0.3836   | 0.7090      | 0.7906    | 0.4083     |
  | 0.0108        | 10.0  | 2130 | 0.3045          | 0.6605            | 0.6478         | 0.6541     | 0.9509           | 0.3529         | 0.4580           | 0.3649   | 0.6897      | 0.7843    | 0.4387     |
  | 0.013         | 11.0  | 2343 | 0.3383          | 0.6788            | 0.6179         | 0.6470     | 0.9507           | 0.2783         | 0.4248           | 0.3358   | 0.7368      | 0.7958    | 0.3655     |
  | 0.0076        | 12.0  | 2556 | 0.3617          | 0.6920            | 0.5523         | 0.6143     | 0.9474           | 0.2708         | 0.3985           | 0.3333   | 0.6740      | 0.7566    | 0.3525     |
  | 0.0042        | 13.0  | 2769 | 0.3747          | 0.6896            | 0.5664         | 0.6220     | 0.9473           | 0.2478         | 0.3915           | 0.3521   | 0.6561      | 0.7742    | 0.3539     |
  | 0.0049        | 14.0  | 2982 | 0.3376 