readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wnut_17
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: microsoft-deberta-v3-large_ner_wnut_17
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wnut_17
        type: wnut_17
        args: wnut_17
      metrics:
      - name: Precision
        type: precision
        value: 0.7670623145400594
      - name: Recall
        type: recall
        value: 0.618421052631579
      - name: F1
        type: f1
        value: 0.6847682119205298
      - name: Accuracy
        type: accuracy
        value: 0.9666942096230853
  ---
  
  <!-- 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. -->
  
  # microsoft-deberta-v3-large_ner_wnut_17
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the wnut_17 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2199
  - Precision: 0.7671
  - Recall: 0.6184
  - F1: 0.6848
  - Accuracy: 0.9667
  
  ## 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: cosine
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 213  | 0.1751          | 0.6884    | 0.5682 | 0.6225 | 0.9601   |
  | No log        | 2.0   | 426  | 0.1702          | 0.7351    | 0.6208 | 0.6732 | 0.9655   |
  | 0.1003        | 3.0   | 639  | 0.1954          | 0.7360    | 0.6136 | 0.6693 | 0.9656   |
  | 0.1003        | 4.0   | 852  | 0.2113          | 0.7595    | 0.6232 | 0.6846 | 0.9669   |
  | 0.015         | 5.0   | 1065 | 0.2199          | 0.7671    | 0.6184 | 0.6848 | 0.9667   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  