---
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: 0.9944576405384006
    - name: Recall
      type: recall
      value: 0.9960348929421095
    - name: F1
      type: f1
      value: 0.9952456418383517
    - name: Accuracy
      type: accuracy
      value: 0.9984752521698335
---

<!-- 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: 0.0112
- Precision: 0.9945
- Recall: 0.9960
- F1: 0.9952
- Accuracy: 0.9985

## 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   | 198  | 0.0264          | 0.9771    | 0.9794 | 0.9782 | 0.9937   |
| No log        | 2.0   | 396  | 0.0149          | 0.9851    | 0.9937 | 0.9893 | 0.9965   |
| 0.1007        | 3.0   | 594  | 0.0116          | 0.9937    | 0.9952 | 0.9945 | 0.9979   |
| 0.1007        | 4.0   | 792  | 0.0099          | 0.9960    | 0.9968 | 0.9964 | 0.9986   |
| 0.1007        | 5.0   | 990  | 0.0096          | 0.9945    | 0.9968 | 0.9956 | 0.9986   |
| 0.0057        | 6.0   | 1188 | 0.0102          | 0.9937    | 0.9960 | 0.9949 | 0.9986   |
| 0.0057        | 7.0   | 1386 | 0.0105          | 0.9937    | 0.9960 | 0.9949 | 0.9986   |
| 0.0014        | 8.0   | 1584 | 0.0108          | 0.9945    | 0.9960 | 0.9952 | 0.9985   |
| 0.0014        | 9.0   | 1782 | 0.0111          | 0.9945    | 0.9960 | 0.9952 | 0.9985   |
| 0.0014        | 10.0  | 1980 | 0.0112          | 0.9945    | 0.9960 | 0.9952 | 0.9985   |


### Framework versions

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