readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - favsbot
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-base-cased-NER-favsbot
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: favsbot
        type: favsbot
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.8461538461538461
      - name: Recall
        type: recall
        value: 0.88
      - name: F1
        type: f1
        value: 0.8627450980392156
      - name: Accuracy
        type: accuracy
        value: 0.9444444444444444
  ---
  
  <!-- 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-base-cased-NER-favsbot
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1680
  - Precision: 0.8462
  - Recall: 0.88
  - F1: 0.8627
  - Accuracy: 0.9444
  
  ## 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: 1.5e-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: linear
  - num_epochs: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 7    | 1.8761          | 0.0       | 0.0    | 0.0    | 0.5833   |
  | No log        | 2.0   | 14   | 1.3530          | 0.0       | 0.0    | 0.0    | 0.5972   |
  | No log        | 3.0   | 21   | 1.0400          | 1.0       | 0.12   | 0.2143 | 0.6389   |
  | No log        | 4.0   | 28   | 0.7987          | 0.7895    | 0.6    | 0.6818 | 0.8194   |
  | No log        | 5.0   | 35   | 0.6055          | 0.85      | 0.68   | 0.7556 | 0.875    |
  | No log        | 6.0   | 42   | 0.4749          | 0.8696    | 0.8    | 0.8333 | 0.9167   |
  | No log        | 7.0   | 49   | 0.3838          | 0.84      | 0.84   | 0.8400 | 0.9444   |
  | No log        | 8.0   | 56   | 0.3084          | 0.88      | 0.88   | 0.88   | 0.9583   |
  | No log        | 9.0   | 63   | 0.2643          | 0.88      | 0.88   | 0.88   | 0.9583   |
  | No log        | 10.0  | 70   | 0.2360          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 11.0  | 77   | 0.2168          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 12.0  | 84   | 0.2031          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 13.0  | 91   | 0.1937          | 0.88      | 0.88   | 0.88   | 0.9583   |
  | No log        | 14.0  | 98   | 0.1853          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 15.0  | 105  | 0.1791          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 16.0  | 112  | 0.1757          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 17.0  | 119  | 0.1718          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  | No log        | 18.0  | 126  | 0.1698          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
  | No log        | 19.0  | 133  | 0.1686          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
  | No log        | 20.0  | 140  | 0.1680          | 0.8462    | 0.88   | 0.8627 | 0.9444   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  