---
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: 0.9871794871794872
    - name: Recall
      type: recall
      value: 0.992633517495396
    - name: F1
      type: f1
      value: 0.98989898989899
    - name: Accuracy
      type: accuracy
      value: 0.9953393533352752
---

<!-- 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: 0.0308
- Precision: 0.9872
- Recall: 0.9926
- F1: 0.9899
- Accuracy: 0.9953

## 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   | 80   | 0.1360          | 0.7993    | 0.8582 | 0.8277 | 0.9645   |
| No log        | 2.0   | 160  | 0.0396          | 0.9762    | 0.9834 | 0.9798 | 0.9924   |
| No log        | 3.0   | 240  | 0.0410          | 0.9745    | 0.9834 | 0.9789 | 0.9916   |
| No log        | 4.0   | 320  | 0.0382          | 0.9817    | 0.9890 | 0.9853 | 0.9933   |
| No log        | 5.0   | 400  | 0.0326          | 0.9818    | 0.9908 | 0.9863 | 0.9945   |
| No log        | 6.0   | 480  | 0.0366          | 0.9781    | 0.9890 | 0.9835 | 0.9936   |
| 0.0953        | 7.0   | 560  | 0.0351          | 0.9781    | 0.9853 | 0.9817 | 0.9936   |
| 0.0953        | 8.0   | 640  | 0.0314          | 0.9854    | 0.9926 | 0.9890 | 0.9950   |
| 0.0953        | 9.0   | 720  | 0.0299          | 0.9872    | 0.9926 | 0.9899 | 0.9953   |
| 0.0953        | 10.0  | 800  | 0.0308          | 0.9872    | 0.9926 | 0.9899 | 0.9953   |


### Framework versions

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