readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: microsoft-deberta-v3-large_ner_wikiann
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: en
        split: train
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8557286258220838
      - name: Recall
        type: recall
        value: 0.8738159196946134
      - name: F1
        type: f1
        value: 0.8646776957783918
      - name: Accuracy
        type: accuracy
        value: 0.9406352438660972
  ---
  
  <!-- 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_wikiann
  
  This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3108
  - Precision: 0.8557
  - Recall: 0.8738
  - F1: 0.8647
  - Accuracy: 0.9406
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3005        | 1.0   | 1250 | 0.2462          | 0.8205    | 0.8400 | 0.8301 | 0.9294   |
  | 0.1931        | 2.0   | 2500 | 0.2247          | 0.8448    | 0.8630 | 0.8538 | 0.9386   |
  | 0.1203        | 3.0   | 3750 | 0.2341          | 0.8468    | 0.8693 | 0.8579 | 0.9403   |
  | 0.0635        | 4.0   | 5000 | 0.2948          | 0.8596    | 0.8745 | 0.8670 | 0.9411   |
  | 0.0451        | 5.0   | 6250 | 0.3108          | 0.8557    | 0.8738 | 0.8647 | 0.9406   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  