finetuned_model

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

• Loss: 0.0005
• Accuracy: 1.0
• F1: 1.0
• Precision: 1.0
• Recall: 1.0

Model description

This model evaluates text data of recipes. This model was developed with distilbert-base-uncased.

Intended uses & limitations

This model aims to identify if a recipe is considered healthy or unhealthy. It is not intended for any other purposes.

Training and evaluation data

This model was trained and evaluated on the original and augmented datasets of written recipes. These were sourced from mohitk24/text_dataset

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• 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: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.0012	1.0	80	0.0005	1.0	1.0	1.0	1.0
0.0003	2.0	160	0.0002	1.0	1.0	1.0	1.0
0.0002	3.0	240	0.0001	1.0	1.0	1.0	1.0
0.0001	4.0	320	0.0001	1.0	1.0	1.0	1.0
0.0001	5.0	400	0.0001	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0