readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-test2
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        config: wnut_17
        split: train
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.5278121137206427
      - name: Recall
        type: recall
        value: 0.3957367933271548
      - name: F1
        type: f1
        value: 0.4523305084745763
      - name: Accuracy
        type: accuracy
        value: 0.9461758796118165
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-test2
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3055
  - Precision: 0.5278
  - Recall: 0.3957
  - F1: 0.4523
  - Accuracy: 0.9462
  
  ## 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: 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 213  | 0.2889          | 0.5439    | 0.3503 | 0.4262 | 0.9453   |
  | No log        | 2.0   | 426  | 0.2938          | 0.5236    | 0.3800 | 0.4404 | 0.9457   |
  | 0.0544        | 3.0   | 639  | 0.3055          | 0.5278    | 0.3957 | 0.4523 | 0.9462   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu102
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  