readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - Jsevisal/gesture_pred
  pipeline_tag: token-classification
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-gest-pred
    results: 
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: Jsevisal/gesture_pred
        type: Jsevisal/gesture_pred
        config: Jsevisal/gesture_pred
        split: validation
        args: Jsevisal/gesture_pred
      metrics:
      - name: Precision
        type: precision
        value: 0.625532
      - name: Recall
        type: recall
        value: 0.735000
      - name: F1
        type: f1
        value: 0.675862
      - name: Accuracy
        type: accuracy
        value: 0.822497
  ---
  
  <!-- 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-finetuned-gest-pred
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0362
  - Precision: 0.6255
  - Recall: 0.735
  - F1: 0.6759
  - Accuracy: 0.8225
  
  ## 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: 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 147  | 0.7631          | 0.5425    | 0.67   | 0.5996 | 0.8062   |
  | No log        | 2.0   | 294  | 0.7010          | 0.5399    | 0.71   | 0.6134 | 0.8264   |
  | No log        | 3.0   | 441  | 0.7795          | 0.5949    | 0.705  | 0.6453 | 0.8069   |
  | 0.3427        | 4.0   | 588  | 0.8437          | 0.6379    | 0.74   | 0.6852 | 0.8277   |
  | 0.3427        | 5.0   | 735  | 0.8573          | 0.6109    | 0.73   | 0.6651 | 0.8290   |
  | 0.3427        | 6.0   | 882  | 0.9024          | 0.6550    | 0.75   | 0.6993 | 0.8283   |
  | 0.0985        | 7.0   | 1029 | 0.9622          | 0.6920    | 0.775  | 0.7311 | 0.8394   |
  | 0.0985        | 8.0   | 1176 | 1.0299          | 0.6391    | 0.735  | 0.6837 | 0.8160   |
  | 0.0985        | 9.0   | 1323 | 1.0425          | 0.6507    | 0.745  | 0.6946 | 0.8147   |
  | 0.0985        | 10.0  | 1470 | 1.0362          | 0.6255    | 0.735  | 0.6759 | 0.8225   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2