readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - plod-filtered
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: albert-base-v2-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: plod-filtered
        type: plod-filtered
        config: PLODfiltered
        split: validation
        args: PLODfiltered
      metrics:
      - name: Precision
        type: precision
        value: 0.988973631766888
      - name: Recall
        type: recall
        value: 0.988142815877984
      - name: F1
        type: f1
        value: 0.9885580492613874
      - name: Accuracy
        type: accuracy
        value: 0.9883807619910069
  ---
  
  <!-- 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-base-v2-finetuned-ner
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the plod-filtered dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0319
  - Precision: 0.9890
  - Recall: 0.9881
  - F1: 0.9886
  - Accuracy: 0.9884
  
  ## 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: 1e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0649        | 1.0   | 3018 | 0.0471          | 0.9838    | 0.9814 | 0.9826 | 0.9818   |
  | 0.0442        | 2.0   | 6036 | 0.0319          | 0.9890    | 0.9881 | 0.9886 | 0.9884   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  