metadata
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuned_model
results: []
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