This model is used to classify weather a text input is talking about food or not. It was trained on a dataset of synthetic data, of food captions, using Mistral Chat.

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
0.4776	1.0	7	0.1124	1.0
0.0502	2.0	14	0.0099	1.0
0.0071	3.0	21	0.0032	1.0
0.0027	4.0	28	0.0016	1.0
0.0015	5.0	35	0.0011	1.0
0.0012	6.0	42	0.0009	1.0
0.0009	7.0	49	0.0008	1.0
0.0008	8.0	56	0.0007	1.0
0.0007	9.0	63	0.0007	1.0
0.0008	10.0	70	0.0007	1.0

Framework versions

Transformers 4.55.2
Pytorch 2.8.0+cu126
Datasets 4.0.0
Tokenizers 0.21.4

AndyHoskins/learn_hf_food_not_food_classifier

learn_hf_food_not_food_classifier

Model description