readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - fin
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: fin4
    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: 0.9209486166007905
      - name: Recall
        type: recall
        value: 0.9282868525896414
      - name: F1
        type: f1
        value: 0.9246031746031745
      - name: Accuracy
        type: accuracy
        value: 0.9913080347678609
  ---
  
  <!-- 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. -->
  
  # fin4
  
  This model is a fine-tuned version of [nlpaueb/sec-bert-num](https://huggingface.co/nlpaueb/sec-bert-num) on the fin dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0549
  - Precision: 0.9209
  - Recall: 0.9283
  - F1: 0.9246
  - Accuracy: 0.9913
  
  ## 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  | 0.1041          | 0.8242    | 0.8406 | 0.8323 | 0.9788   |
  | No log        | 2.0   | 258  | 0.0511          | 0.9173    | 0.9283 | 0.9228 | 0.9902   |
  | No log        | 3.0   | 387  | 0.0430          | 0.9102    | 0.9283 | 0.9191 | 0.9907   |
  | 0.0598        | 4.0   | 516  | 0.0501          | 0.9368    | 0.9442 | 0.9405 | 0.9922   |
  | 0.0598        | 5.0   | 645  | 0.0436          | 0.9325    | 0.9363 | 0.9344 | 0.9924   |
  | 0.0598        | 6.0   | 774  | 0.0489          | 0.9433    | 0.9283 | 0.9357 | 0.9917   |
  | 0.0598        | 7.0   | 903  | 0.0499          | 0.932     | 0.9283 | 0.9301 | 0.9919   |
  | 0.0028        | 8.0   | 1032 | 0.0537          | 0.9209    | 0.9283 | 0.9246 | 0.9913   |
  | 0.0028        | 9.0   | 1161 | 0.0540          | 0.9170    | 0.9243 | 0.9206 | 0.9911   |
  | 0.0028        | 10.0  | 1290 | 0.0549          | 0.9209    | 0.9283 | 0.9246 | 0.9913   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  