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: 0.0005
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_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3937	1.0	7	0.0620	1.0
0.0266	2.0	14	0.0067	1.0
0.0041	3.0	21	0.0021	1.0
0.0018	4.0	28	0.0011	1.0
0.0011	5.0	35	0.0008	1.0
0.0008	6.0	42	0.0006	1.0
0.0007	7.0	49	0.0006	1.0
0.0006	8.0	56	0.0005	1.0
0.0006	9.0	63	0.0005	1.0
0.0006	10.0	70	0.0005	1.0

Framework versions

Transformers 4.57.6
Pytorch 2.11.0+cpu
Datasets 5.0.1
Tokenizers 0.22.2

onnx-community/learn_hf_food_not_food_text_classifier_distilbert_base_uncased-ONNX

learn_hf_food_not_food_text_classifier_distilbert_base_uncased (ONNX)

Usage with Transformers.js

learn_hf_food_not_food_text_classifier_distilbert_base_uncased

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

onnx-community/
learn_hf_food_not_food_text_classifier_distilbert_base_uncased-ONNX