readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - fin
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: fin2
    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: 0.9362745098039216
      - name: Recall
        type: recall
        value: 0.7609561752988048
      - name: F1
        type: f1
        value: 0.8395604395604396
      - name: Accuracy
        type: accuracy
        value: 0.9742916119346969
  ---
  
  <!-- 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. -->
  
  # fin2
  
  This model is a fine-tuned version of [nlpaueb/sec-bert-base](https://huggingface.co/nlpaueb/sec-bert-base) on the fin dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2405
  - Precision: 0.9363
  - Recall: 0.7610
  - F1: 0.8396
  - Accuracy: 0.9743
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 129  | 0.2186          | 0.7980    | 0.6454 | 0.7137 | 0.9653   |
  | No log        | 2.0   | 258  | 0.2109          | 0.9487    | 0.7371 | 0.8296 | 0.9734   |
  | No log        | 3.0   | 387  | 0.2531          | 0.9746    | 0.7649 | 0.8571 | 0.9743   |
  | 0.1166        | 4.0   | 516  | 0.2345          | 0.9403    | 0.7530 | 0.8363 | 0.9741   |
  | 0.1166        | 5.0   | 645  | 0.2405          | 0.9363    | 0.7610 | 0.8396 | 0.9743   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  