readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: mini-bert-refinetuned-gest-pred
    results: []
  ---
  
  <!-- 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. -->
  
  # mini-bert-refinetuned-gest-pred
  
  This model is a fine-tuned version of [Jsevisal/bert-finetuned-gest-pred](https://huggingface.co/Jsevisal/bert-finetuned-gest-pred) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.792952
  - Precision: 0.792952
  - Recall: 0.869565
  - F1: 0.829493
  - Accuracy: 0.940977
  
  ## 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   | 8    | 0.2389          | 0.7965    | 0.8696 | 0.8314 | 0.9423   |
  | 0.5887        | 2.0   | 16   | 0.2352          | 0.7939    | 0.8744 | 0.8322 | 0.9349   |
  | 0.3524        | 3.0   | 24   | 0.2275          | 0.8089    | 0.8792 | 0.8426 | 0.9457   |
  | 0.2855        | 4.0   | 32   | 0.2268          | 0.7965    | 0.8696 | 0.8314 | 0.9437   |
  | 0.2177        | 5.0   | 40   | 0.2215          | 0.7860    | 0.8696 | 0.8257 | 0.9417   |
  | 0.2177        | 6.0   | 48   | 0.2167          | 0.7650    | 0.8647 | 0.8118 | 0.9383   |
  | 0.1664        | 7.0   | 56   | 0.2083          | 0.7930    | 0.8696 | 0.8295 | 0.9410   |
  | 0.1446        | 8.0   | 64   | 0.2091          | 0.8318    | 0.8841 | 0.8571 | 0.9437   |
  | 0.1319        | 9.0   | 72   | 0.2115          | 0.8243    | 0.8841 | 0.8531 | 0.9423   |
  | 0.1188        | 10.0  | 80   | 0.2115          | 0.8243    | 0.8841 | 0.8531 | 0.9423   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  