readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: twitter-roberta-base-dec2021-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: 0.7111716621253406
      - name: Recall
        type: recall
        value: 0.6244019138755981
      - name: F1
        type: f1
        value: 0.664968152866242
      - name: Accuracy
        type: accuracy
        value: 0.9642789042140724
  ---
  
  <!-- 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-WNUT
  
  This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2152
  - Precision: 0.7112
  - Recall: 0.6244
  - F1: 0.6650
  - Accuracy: 0.9643
  
  ## 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        | 0.46  | 25   | 0.2818          | 0.0982    | 0.0383 | 0.0551 | 0.9241   |
  | No log        | 0.93  | 50   | 0.2158          | 0.6181    | 0.4569 | 0.5254 | 0.9480   |
  | No log        | 1.39  | 75   | 0.1930          | 0.6682    | 0.5347 | 0.5940 | 0.9555   |
  | No log        | 1.85  | 100  | 0.1728          | 0.6583    | 0.5646 | 0.6079 | 0.9594   |
  | No log        | 2.31  | 125  | 0.1787          | 0.7050    | 0.5718 | 0.6314 | 0.9619   |
  | No log        | 2.78  | 150  | 0.2051          | 0.6979    | 0.5251 | 0.5993 | 0.9587   |
  | No log        | 3.24  | 175  | 0.1755          | 0.7172    | 0.5945 | 0.6501 | 0.9621   |
  | No log        | 3.7   | 200  | 0.1720          | 0.6943    | 0.6304 | 0.6608 | 0.9645   |
  | No log        | 4.17  | 225  | 0.1873          | 0.7203    | 0.6316 | 0.6730 | 0.9646   |
  | No log        | 4.63  | 250  | 0.1781          | 0.6934    | 0.6196 | 0.6545 | 0.9638   |
  | No log        | 5.09  | 275  | 0.1953          | 0.7040    | 0.6172 | 0.6577 | 0.9631   |
  | No log        | 5.56  | 300  | 0.1953          | 0.7223    | 0.6316 | 0.6739 | 0.9642   |
  | No log        | 6.02  | 325  | 0.1839          | 0.7008    | 0.6471 | 0.6729 | 0.9648   |
  | No log        | 6.48  | 350  | 0.1995          | 0.716     | 0.6423 | 0.6772 | 0.9650   |
  | No log        | 6.94  | 375  | 0.2056          | 0.7251    | 0.6184 | 0.6675 | 0.9640   |
  | No log        | 7.41  | 400  | 0.2044          | 0.7065    | 0.6220 | 0.6616 | 0.9640   |
  | No log        | 7.87  | 425  | 0.2042          | 0.7201    | 0.6400 | 0.6776 | 0.9650   |
  | No log        | 8.33  | 450  | 0.2247          | 0.7280    | 0.6244 | 0.6722 | 0.9638   |
  | No log        | 8.8   | 475  | 0.2060          | 0.7064    | 0.6447 | 0.6742 | 0.9649   |
  | 0.0675        | 9.26  | 500  | 0.2152          | 0.7112    | 0.6244 | 0.6650 | 0.9643   |
  | 0.0675        | 9.72  | 525  | 0.2086          | 0.7070    | 0.6495 | 0.6771 | 0.9650   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  