README.md

metadata

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: 0.9847619047619047
          - name: Recall
            type: recall
            value: 0.988527724665392
          - name: F1
            type: f1
            value: 0.9866412213740458
          - name: Accuracy
            type: accuracy
            value: 0.9974453590689754

distilbert-base-uncased-finetuned-ingredients

This model is a fine-tuned version of distilbert-base-uncased on the ingredients_yes_no dataset. It achieves the following results on the evaluation set:

• Loss: 0.0138
• Precision: 0.9848
• Recall: 0.9885
• F1: 0.9866
• Accuracy: 0.9974

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	83	0.1004	0.8978	0.9235	0.9105	0.9807
No log	2.0	166	0.0237	0.9714	0.9751	0.9733	0.9940
No log	3.0	249	0.0204	0.9715	0.9771	0.9743	0.9929
No log	4.0	332	0.0138	0.9773	0.9866	0.9819	0.9969
No log	5.0	415	0.0137	0.9829	0.9866	0.9847	0.9969
No log	6.0	498	0.0134	0.9847	0.9866	0.9857	0.9972
0.0923	7.0	581	0.0160	0.9866	0.9885	0.9876	0.9972
0.0923	8.0	664	0.0147	0.9848	0.9885	0.9866	0.9974
0.0923	9.0	747	0.0139	0.9848	0.9885	0.9866	0.9974
0.0923	10.0	830	0.0138	0.9848	0.9885	0.9866	0.9974

Framework versions

• Transformers 4.10.0
• Pytorch 1.9.0+cu102
• Datasets 1.11.0
• Tokenizers 0.10.3