readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: twitter-roberta-base-CoNLL
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.953111963957951
      - name: Recall
        type: recall
        value: 0.9612924941097274
      - name: F1
        type: f1
        value: 0.9571847507331379
      - name: Accuracy
        type: accuracy
        value: 0.9925820645613489
  ---
  
  <!-- 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-CoNLL
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0423
  - Precision: 0.9531
  - Recall: 0.9613
  - F1: 0.9572
  - Accuracy: 0.9926
  
  ## 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: 6e-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   | 0.2063          | 0.6517    | 0.6659 | 0.6587 | 0.9386   |
  | No log        | 0.23  | 50   | 0.0810          | 0.8373    | 0.8766 | 0.8565 | 0.9771   |
  | No log        | 0.34  | 75   | 0.0651          | 0.8937    | 0.9058 | 0.8997 | 0.9827   |
  | No log        | 0.45  | 100  | 0.0537          | 0.9014    | 0.9135 | 0.9074 | 0.9849   |
  | No log        | 0.57  | 125  | 0.0464          | 0.9097    | 0.9244 | 0.9170 | 0.9867   |
  | No log        | 0.68  | 150  | 0.0423          | 0.9243    | 0.9350 | 0.9296 | 0.9885   |
  | No log        | 0.8   | 175  | 0.0381          | 0.9250    | 0.9438 | 0.9343 | 0.9900   |
  | No log        | 0.91  | 200  | 0.0388          | 0.9264    | 0.9446 | 0.9354 | 0.9896   |
  | No log        | 1.02  | 225  | 0.0394          | 0.9328    | 0.9441 | 0.9384 | 0.9898   |
  | No log        | 1.14  | 250  | 0.0423          | 0.9348    | 0.9458 | 0.9403 | 0.9896   |
  | No log        | 1.25  | 275  | 0.0432          | 0.9304    | 0.9406 | 0.9355 | 0.9892   |
  | No log        | 1.36  | 300  | 0.0382          | 0.9393    | 0.9473 | 0.9433 | 0.9901   |
  | No log        | 1.48  | 325  | 0.0381          | 0.9326    | 0.9504 | 0.9414 | 0.9901   |
  | No log        | 1.59  | 350  | 0.0387          | 0.9337    | 0.9524 | 0.9429 | 0.9902   |
  | No log        | 1.7   | 375  | 0.0365          | 0.9404    | 0.9475 | 0.9439 | 0.9901   |
  | No log        | 1.82  | 400  | 0.0382          | 0.9431    | 0.9517 | 0.9474 | 0.9905   |
  | No log        | 1.93  | 425  | 0.0373          | 0.9399    | 0.9524 | 0.9461 | 0.9903   |
  | No log        | 2.05  | 450  | 0.0367          | 0.9440    | 0.9556 | 0.9497 | 0.9910   |
  | No log        | 2.16  | 475  | 0.0396          | 0.9400    | 0.9551 | 0.9475 | 0.9907   |
  | 0.0771        | 2.27  | 500  | 0.0353          | 0.9442    | 0.9574 | 0.9508 | 0.9912   |
  | 0.0771        | 2.39  | 525  | 0.0394          | 0.9401    | 0.9507 | 0.9454 | 0.9906   |
  | 0.0771        | 2.5   | 550  | 0.0370          | 0.9447    | 0.9522 | 0.9485 | 0.9910   |
  | 0.0771        | 2.61  | 575  | 0.0352          | 0.9404    | 0.9541 | 0.9472 | 0.9908   |
  | 0.0771        | 2.73  | 600  | 0.0386          | 0.9345    | 0.9554 | 0.9448 | 0.9908   |
  | 0.0771        | 2.84  | 625  | 0.0366          | 0.9428    | 0.9576 | 0.9502 | 0.9916   |
  | 0.0771        | 2.95  | 650  | 0.0353          | 0.9427    | 0.9546 | 0.9486 | 0.9913   |
  | 0.0771        | 3.07  | 675  | 0.0359          | 0.9412    | 0.9544 | 0.9478 | 0.9911   |
  | 0.0771        | 3.18  | 700  | 0.0356          | 0.9476    | 0.9593 | 0.9534 | 0.9920   |
  | 0.0771        | 3.3   | 725  | 0.0345          | 0.9484    | 0.9586 | 0.9535 | 0.9918   |
  | 0.0771        | 3.41  | 750  | 0.0345          | 0.9427    | 0.9557 | 0.9492 | 0.9916   |
  | 0.0771        | 3.52  | 775  | 0.0364          | 0.9389    | 0.9569 | 0.9478 | 0.9914   |
  | 0.0771        | 3.64  | 800  | 0.0360          | 0.9430    | 0.9584 | 0.9507 | 0.9915   |
  | 0.0771        | 3.75  | 825  | 0.0387          | 0.9458    | 0.9552 | 0.9505 | 0.9915   |
  | 0.0771        | 3.86  | 850  | 0.0347          | 0.9468    | 0.9576 | 0.9521 | 0.9917   |
  | 0.0771        | 3.98  | 875  | 0.0357          | 0.9445    | 0.9574 | 0.9509