readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - silicone
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-cased
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: silicone
        type: silicone
        config: swda
        split: test
        args: swda
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.7218128224023581
  ---
  
  <!-- 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. -->
  
  # distilbert-base-cased
  
  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the silicone dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9431
  - Accuracy: 0.7218
  - Micro-precision: 0.7218
  - Micro-recall: 0.7218
  - Micro-f1: 0.7218
  - Macro-precision: 0.3546
  - Macro-recall: 0.2905
  - Macro-f1: 0.2888
  - Weighted-precision: 0.6807
  - Weighted-recall: 0.7218
  - Weighted-f1: 0.6875
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro-precision | Micro-recall | Micro-f1 | Macro-precision | Macro-recall | Macro-f1 | Weighted-precision | Weighted-recall | Weighted-f1 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|:------------------:|:---------------:|:-----------:|
  | 0.9397        | 1.0   | 2980 | 0.9431          | 0.7218   | 0.7218          | 0.7218       | 0.7218   | 0.3546          | 0.2905       | 0.2888   | 0.6807             | 0.7218          | 0.6875      |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2
  