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# 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

{"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": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "ingredients_yes_no", "type": "ingredients_yes_no", "args": "IngredientsYesNo"}, "metrics": [{"type": "precision", "value": 0.9898648648648649, "name": "Precision"}, {"type": "recall", "value": 0.9932203389830508, "name": "Recall"}, {"type": "f1", "value": 0.9915397631133671, "name": "F1"}, {"type": "accuracy", "value": 0.9978308026030369, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard distilbert token-classification generated_from_trainer dataset:ingredients_yes_no license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

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: 0.0105
* 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



### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3



### 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 ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3
passage: TAGS
#transformers #pytorch #tensorboard #distilbert #token-classification #generated_from_trainer #dataset-ingredients_yes_no #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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### Framework versions


* Transformers 4.10.2
* Pytorch 1.9.0+cu102
* Datasets 1.11.0
* Tokenizers 0.10.3