readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: twitter-roberta-base-WNUT
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.7045454545454546
      - name: Recall
        type: recall
        value: 0.6303827751196173
      - name: F1
        type: f1
        value: 0.6654040404040403
      - name: Accuracy
        type: accuracy
        value: 0.9639611008707811
  ---
  
  <!-- 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-WNUT
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1938
  - Precision: 0.7045
  - Recall: 0.6304
  - F1: 0.6654
  - Accuracy: 0.9640
  
  ## 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: 2e-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        | 0.46  | 25   | 0.3912          | 0.0       | 0.0    | 0.0    | 0.9205   |
  | No log        | 0.93  | 50   | 0.2847          | 0.25      | 0.0024 | 0.0047 | 0.9209   |
  | No log        | 1.39  | 75   | 0.2449          | 0.5451    | 0.3469 | 0.4240 | 0.9426   |
  | No log        | 1.85  | 100  | 0.1946          | 0.6517    | 0.4856 | 0.5565 | 0.9492   |
  | No log        | 2.31  | 125  | 0.1851          | 0.6921    | 0.5646 | 0.6219 | 0.9581   |
  | No log        | 2.78  | 150  | 0.1672          | 0.6867    | 0.5873 | 0.6331 | 0.9594   |
  | No log        | 3.24  | 175  | 0.1675          | 0.6787    | 0.5837 | 0.6277 | 0.9615   |
  | No log        | 3.7   | 200  | 0.1644          | 0.6765    | 0.6328 | 0.6539 | 0.9638   |
  | No log        | 4.17  | 225  | 0.1672          | 0.6997    | 0.6495 | 0.6737 | 0.9640   |
  | No log        | 4.63  | 250  | 0.1652          | 0.6915    | 0.6435 | 0.6667 | 0.9649   |
  | No log        | 5.09  | 275  | 0.1882          | 0.7067    | 0.6053 | 0.6521 | 0.9629   |
  | No log        | 5.56  | 300  | 0.1783          | 0.7128    | 0.6352 | 0.6717 | 0.9645   |
  | No log        | 6.02  | 325  | 0.1813          | 0.7011    | 0.6172 | 0.6565 | 0.9639   |
  | No log        | 6.48  | 350  | 0.1804          | 0.7139    | 0.6447 | 0.6776 | 0.9647   |
  | No log        | 6.94  | 375  | 0.1902          | 0.7218    | 0.6268 | 0.6709 | 0.9641   |
  | No log        | 7.41  | 400  | 0.1883          | 0.7106    | 0.6316 | 0.6688 | 0.9641   |
  | No log        | 7.87  | 425  | 0.1862          | 0.7067    | 0.6340 | 0.6683 | 0.9643   |
  | No log        | 8.33  | 450  | 0.1882          | 0.7053    | 0.6328 | 0.6671 | 0.9639   |
  | No log        | 8.8   | 475  | 0.1919          | 0.7055    | 0.6304 | 0.6658 | 0.9638   |
  | 0.1175        | 9.26  | 500  | 0.1938          | 0.7045    | 0.6304 | 0.6654 | 0.9640   |
  | 0.1175        | 9.72  | 525  | 0.1880          | 0.7025    | 0.6411 | 0.6704 | 0.9646   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  