---
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: 0.9916855631141346
    - name: Recall
      type: recall
      value: 0.9977186311787072
    - name: F1
      type: f1
      value: 0.9946929492039424
    - name: Accuracy
      type: accuracy
      value: 0.998049340218015
---

<!-- 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: 0.0117
- Precision: 0.9917
- Recall: 0.9977
- F1: 0.9947
- Accuracy: 0.9980

## 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   | 199  | 0.0387          | 0.9431    | 0.9703 | 0.9565 | 0.9900   |
| No log        | 2.0   | 398  | 0.0195          | 0.9805    | 0.9916 | 0.9860 | 0.9960   |
| 0.1062        | 3.0   | 597  | 0.0187          | 0.9842    | 0.9939 | 0.9890 | 0.9963   |
| 0.1062        | 4.0   | 796  | 0.0120          | 0.9887    | 0.9954 | 0.9920 | 0.9976   |
| 0.1062        | 5.0   | 995  | 0.0131          | 0.9887    | 0.9962 | 0.9924 | 0.9975   |
| 0.0067        | 6.0   | 1194 | 0.0106          | 0.9917    | 0.9970 | 0.9943 | 0.9980   |
| 0.0067        | 7.0   | 1393 | 0.0116          | 0.9909    | 0.9977 | 0.9943 | 0.9979   |
| 0.002         | 8.0   | 1592 | 0.0118          | 0.9909    | 0.9977 | 0.9943 | 0.9979   |
| 0.002         | 9.0   | 1791 | 0.0118          | 0.9917    | 0.9977 | 0.9947 | 0.9980   |
| 0.002         | 10.0  | 1990 | 0.0117          | 0.9917    | 0.9977 | 0.9947 | 0.9980   |


### Framework versions

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