readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - caner
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner-v2.2
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: caner
        type: caner
        config: default
        split: train[90%:91%]
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.8822751322751323
      - name: Recall
        type: recall
        value: 0.8496815286624204
      - name: F1
        type: f1
        value: 0.8656716417910448
      - name: Accuracy
        type: accuracy
        value: 0.942741116751269
  ---
  
  <!-- 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. -->
  
  # bert-finetuned-ner-v2.2
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the caner dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3595
  - Precision: 0.8823
  - Recall: 0.8497
  - F1: 0.8657
  - Accuracy: 0.9427
  
  ## 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: 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: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2726        | 1.0   | 3228 | 0.4504          | 0.7390    | 0.7287 | 0.7338 | 0.9107   |
  | 0.2057        | 2.0   | 6456 | 0.3679          | 0.8633    | 0.8446 | 0.8538 | 0.9385   |
  | 0.1481        | 3.0   | 9684 | 0.3595          | 0.8823    | 0.8497 | 0.8657 | 0.9427   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  