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: 0.8571428571428571
      - name: Recall
        type: recall
        value: 0.96
      - name: F1
        type: f1
        value: 0.9056603773584904
      - name: Accuracy
        type: accuracy
        value: 0.9583333333333334
  ---
  
  <!-- 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: 0.0992
  - Precision: 0.8571
  - Recall: 0.96
  - F1: 0.9057
  - Accuracy: 0.9583
  
  ## 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   | 10   | 1.7643          | 0.0       | 0.0    | 0.0    | 0.5694   |
  | No log        | 2.0   | 20   | 1.1420          | 0.0       | 0.0    | 0.0    | 0.5833   |
  | No log        | 3.0   | 30   | 0.7946          | 0.9375    | 0.6    | 0.7317 | 0.8056   |
  | No log        | 4.0   | 40   | 0.5625          | 0.8182    | 0.72   | 0.7660 | 0.8611   |
  | No log        | 5.0   | 50   | 0.4217          | 0.8148    | 0.88   | 0.8462 | 0.9306   |
  | No log        | 6.0   | 60   | 0.3082          | 0.8519    | 0.92   | 0.8846 | 0.9444   |
  | No log        | 7.0   | 70   | 0.2386          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
  | No log        | 8.0   | 80   | 0.1965          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
  | No log        | 9.0   | 90   | 0.1626          | 0.8148    | 0.88   | 0.8462 | 0.9444   |
  | No log        | 10.0  | 100  | 0.1465          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 11.0  | 110  | 0.1314          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 12.0  | 120  | 0.1215          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 13.0  | 130  | 0.1160          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 14.0  | 140  | 0.1104          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 15.0  | 150  | 0.1050          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 16.0  | 160  | 0.1012          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 17.0  | 170  | 0.0997          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 18.0  | 180  | 0.0997          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 19.0  | 190  | 0.0995          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  | No log        | 20.0  | 200  | 0.0992          | 0.8571    | 0.96   | 0.9057 | 0.9583   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.6.1
  - Tokenizers 0.12.1
  