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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](https://huggingface.co/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
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