readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - ingredients_yes_no
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-ingredients
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: ingredients_yes_no
        type: ingredients_yes_no
        args: IngredientsYesNo
      metrics:
      - name: Precision
        type: precision
        value: 0.9898648648648649
      - name: Recall
        type: recall
        value: 0.9932203389830508
      - name: F1
        type: f1
        value: 0.9915397631133671
      - name: Accuracy
        type: accuracy
        value: 0.9978308026030369
  ---
  
  <!-- 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-uncased-finetuned-ingredients
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ingredients_yes_no dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0105
  - Precision: 0.9899
  - Recall: 0.9932
  - F1: 0.9915
  - Accuracy: 0.9978
  
  ## 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   | 47   | 0.2783          | 0.4       | 0.5492 | 0.4629 | 0.8910   |
  | No log        | 2.0   | 94   | 0.1089          | 0.8145    | 0.8780 | 0.8450 | 0.9718   |
  | No log        | 3.0   | 141  | 0.0273          | 0.9865    | 0.9932 | 0.9899 | 0.9973   |
  | No log        | 4.0   | 188  | 0.0168          | 0.9865    | 0.9932 | 0.9899 | 0.9973   |
  | No log        | 5.0   | 235  | 0.0156          | 0.9865    | 0.9898 | 0.9882 | 0.9957   |
  | No log        | 6.0   | 282  | 0.0129          | 0.9865    | 0.9932 | 0.9899 | 0.9973   |
  | No log        | 7.0   | 329  | 0.0121          | 0.9899    | 0.9932 | 0.9915 | 0.9978   |
  | No log        | 8.0   | 376  | 0.0115          | 0.9899    | 0.9932 | 0.9915 | 0.9978   |
  | No log        | 9.0   | 423  | 0.0108          | 0.9899    | 0.9932 | 0.9915 | 0.9978   |
  | No log        | 10.0  | 470  | 0.0105          | 0.9899    | 0.9932 | 0.9915 | 0.9978   |
  
  
  ### Framework versions
  
  - Transformers 4.10.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  