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

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

harr/distilbert-base-uncased-finetuned-ingredients

distilbert-base-uncased-finetuned-ingredients

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

harr/
distilbert-base-uncased-finetuned-ingredients