readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: twitter-roberta-base-dec2021-CoNLL
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9552512940390716
      - name: Recall
        type: recall
        value: 0.9628071356445641
      - name: F1
        type: f1
        value: 0.9590143324113654
      - name: Accuracy
        type: accuracy
        value: 0.9926599431486313
  ---
  
  <!-- 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. -->
  
  # twitter-roberta-base-dec2021-CoNLL
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0412
  - Precision: 0.9553
  - Recall: 0.9628
  - F1: 0.9590
  - Accuracy: 0.9927
  
  ## 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: 5e-05
  - train_batch_size: 64
  - eval_batch_size: 1024
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 0.11  | 25   | 0.2126          | 0.5639    | 0.6067 | 0.5845 | 0.9349   |
  | No log        | 0.23  | 50   | 0.0849          | 0.8259    | 0.8612 | 0.8431 | 0.9765   |
  | No log        | 0.34  | 75   | 0.0640          | 0.8752    | 0.8957 | 0.8853 | 0.9820   |
  | No log        | 0.45  | 100  | 0.0572          | 0.8848    | 0.9034 | 0.8940 | 0.9832   |
  | No log        | 0.57  | 125  | 0.0469          | 0.9071    | 0.9239 | 0.9155 | 0.9866   |
  | No log        | 0.68  | 150  | 0.0442          | 0.9198    | 0.9278 | 0.9238 | 0.9877   |
  | No log        | 0.8   | 175  | 0.0424          | 0.9192    | 0.9322 | 0.9256 | 0.9881   |
  | No log        | 0.91  | 200  | 0.0407          | 0.9170    | 0.9414 | 0.9291 | 0.9891   |
  | No log        | 1.02  | 225  | 0.0402          | 0.9264    | 0.9403 | 0.9333 | 0.9894   |
  | No log        | 1.14  | 250  | 0.0399          | 0.9329    | 0.9446 | 0.9387 | 0.9897   |
  | No log        | 1.25  | 275  | 0.0384          | 0.9278    | 0.9413 | 0.9345 | 0.9897   |
  | No log        | 1.36  | 300  | 0.0363          | 0.9379    | 0.9477 | 0.9427 | 0.9906   |
  | No log        | 1.48  | 325  | 0.0362          | 0.9380    | 0.9493 | 0.9436 | 0.9905   |
  | No log        | 1.59  | 350  | 0.0364          | 0.9397    | 0.9497 | 0.9447 | 0.9905   |
  | No log        | 1.7   | 375  | 0.0367          | 0.9324    | 0.9475 | 0.9399 | 0.9899   |
  | No log        | 1.82  | 400  | 0.0372          | 0.9350    | 0.9460 | 0.9404 | 0.9899   |
  | No log        | 1.93  | 425  | 0.0339          | 0.9411    | 0.9514 | 0.9462 | 0.9909   |
  | No log        | 2.05  | 450  | 0.0336          | 0.9419    | 0.9529 | 0.9474 | 0.9911   |
  | No log        | 2.16  | 475  | 0.0336          | 0.9447    | 0.9537 | 0.9492 | 0.9914   |
  | 0.079         | 2.27  | 500  | 0.0345          | 0.9420    | 0.9566 | 0.9492 | 0.9914   |
  | 0.079         | 2.39  | 525  | 0.0364          | 0.9436    | 0.9522 | 0.9479 | 0.9913   |
  | 0.079         | 2.5   | 550  | 0.0340          | 0.9479    | 0.9514 | 0.9496 | 0.9916   |
  | 0.079         | 2.61  | 575  | 0.0339          | 0.9481    | 0.9559 | 0.9520 | 0.9917   |
  | 0.079         | 2.73  | 600  | 0.0396          | 0.9326    | 0.9504 | 0.9414 | 0.9902   |
  | 0.079         | 2.84  | 625  | 0.0348          | 0.9461    | 0.9544 | 0.9502 | 0.9915   |
  | 0.079         | 2.95  | 650  | 0.0359          | 0.9419    | 0.9527 | 0.9473 | 0.9908   |
  | 0.079         | 3.07  | 675  | 0.0347          | 0.9434    | 0.9573 | 0.9503 | 0.9916   |
  | 0.079         | 3.18  | 700  | 0.0351          | 0.9464    | 0.9566 | 0.9515 | 0.9918   |
  | 0.079         | 3.3   | 725  | 0.0370          | 0.9446    | 0.9536 | 0.9491 | 0.9911   |
  | 0.079         | 3.41  | 750  | 0.0358          | 0.9462    | 0.9583 | 0.9522 | 0.9917   |
  | 0.079         | 3.52  | 775  | 0.0353          | 0.9483    | 0.9564 | 0.9523 | 0.9920   |
  | 0.079         | 3.64  | 800  | 0.0351          | 0.9469    | 0.9564 | 0.9516 | 0.9916   |
  | 0.079         | 3.75  | 825  | 0.0361          | 0.9479    | 0.9579 | 0.9529 | 0.9919   |
  | 0.079         | 3.86  | 850  | 0.0370          | 0.9498    | 0.9581 | 0.9539 | 0.9918   |
  | 0.079         | 3.98  | 875  | 0.0374      