readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - fin
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: fin6
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: fin
        type: fin
        config: default
        split: train
        args: default
      metrics:
      - name: Precision
        type: precision
        value: 0.8237410071942446
      - name: Recall
        type: recall
        value: 0.9123505976095617
      - name: F1
        type: f1
        value: 0.8657844990548205
      - name: Accuracy
        type: accuracy
        value: 0.9836742353161944
  ---
  
  <!-- 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. -->
  
  # fin6
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the fin dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0732
  - Precision: 0.8237
  - Recall: 0.9124
  - F1: 0.8658
  - Accuracy: 0.9837
  
  ## 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 129  | 0.0922          | 0.6559    | 0.8127 | 0.7260 | 0.9739   |
  | No log        | 2.0   | 258  | 0.0471          | 0.8889    | 0.9243 | 0.9062 | 0.9910   |
  | No log        | 3.0   | 387  | 0.0620          | 0.8419    | 0.9124 | 0.8757 | 0.9825   |
  | 0.0622        | 4.0   | 516  | 0.0651          | 0.8156    | 0.9163 | 0.8630 | 0.9805   |
  | 0.0622        | 5.0   | 645  | 0.0508          | 0.8614    | 0.9163 | 0.8880 | 0.9872   |
  | 0.0622        | 6.0   | 774  | 0.0467          | 0.8988    | 0.9203 | 0.9094 | 0.9916   |
  | 0.0622        | 7.0   | 903  | 0.0713          | 0.8099    | 0.9163 | 0.8598 | 0.9822   |
  | 0.0052        | 8.0   | 1032 | 0.0767          | 0.8214    | 0.9163 | 0.8663 | 0.9824   |
  | 0.0052        | 9.0   | 1161 | 0.0739          | 0.8179    | 0.9124 | 0.8625 | 0.9831   |
  | 0.0052        | 10.0  | 1290 | 0.0732          | 0.8237    | 0.9124 | 0.8658 | 0.9837   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  