readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: albert-large-v2_ner_conll2003
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9396018069265518
      - name: Recall
        type: recall
        value: 0.9451363177381353
      - name: F1
        type: f1
        value: 0.9423609363201612
      - name: Accuracy
        type: accuracy
        value: 0.9874810170943499
  ---
  
  <!-- 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. -->
  
  # albert-large-v2_ner_conll2003
  
  This model is a fine-tuned version of [albert-large-v2](https://huggingface.co/albert-large-v2) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0584
  - Precision: 0.9396
  - Recall: 0.9451
  - F1: 0.9424
  - Accuracy: 0.9875
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2034        | 1.0   | 878  | 0.0653          | 0.9114    | 0.9278 | 0.9195 | 0.9837   |
  | 0.0561        | 2.0   | 1756 | 0.0602          | 0.9316    | 0.9280 | 0.9298 | 0.9845   |
  | 0.0303        | 3.0   | 2634 | 0.0536          | 0.9380    | 0.9424 | 0.9402 | 0.9872   |
  | 0.0177        | 4.0   | 3512 | 0.0535          | 0.9393    | 0.9456 | 0.9425 | 0.9877   |
  | 0.011         | 5.0   | 4390 | 0.0584          | 0.9396    | 0.9451 | 0.9424 | 0.9875   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  