readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  model-index:
  - name: my-distilBERT-finetune-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: train
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9376994122586062
      - name: Recall
        type: recall
        value: 0.9397509256142713
      - name: F1
        type: f1
        value: 0.9387240480793477
  ---
  
  <!-- 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. -->
  
  # my-distilBERT-finetune-ner
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0563
  - Precision: 0.9377
  - Recall: 0.9398
  - F1: 0.9387
  
  ## 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: 32
  - eval_batch_size: 64
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
  | No log        | 1.0   | 439  | 0.0547          | 0.9251    | 0.9291 | 0.9271 |
  | 0.1451        | 2.0   | 878  | 0.0531          | 0.9315    | 0.9386 | 0.9350 |
  | 0.0326        | 3.0   | 1317 | 0.0563          | 0.9377    | 0.9398 | 0.9387 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  