learn_hf_food_not_food_text_classification-distilbert-base-uncased

This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0002
Accuracy: 1.0

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: 0.0001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4734	1.0	7	0.1078	1.0
0.056	2.0	14	0.0247	0.98
0.005	3.0	21	0.0545	0.98
0.0016	4.0	28	0.0029	1.0
0.0008	5.0	35	0.0006	1.0
0.0006	6.0	42	0.0004	1.0
0.0004	7.0	49	0.0003	1.0
0.0004	8.0	56	0.0003	1.0
0.0003	9.0	63	0.0002	1.0
0.0003	10.0	70	0.0002	1.0
0.0003	11.0	77	0.0002	1.0
0.0002	12.0	84	0.0002	1.0
0.0002	13.0	91	0.0002	1.0
0.0002	14.0	98	0.0002	1.0
0.0002	15.0	105	0.0002	1.0
0.0002	16.0	112	0.0002	1.0
0.0002	17.0	119	0.0002	1.0
0.0002	18.0	126	0.0002	1.0
0.0002	19.0	133	0.0002	1.0
0.0002	20.0	140	0.0002	1.0

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

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Naemah1998/learn_hf_food_not_food_text_classification-distilbert-base-uncased

learn_hf_food_not_food_text_classification-distilbert-base-uncased

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

learn_hf_food_not_food_text_classification-distilbert-base-uncased

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions