---
license: apache-2.0
base_model: SpamAcc/ingredient_prune
tags:
- generated_from_trainer
model-index:
- name: ingredient_prune
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ingredient_prune

This model is a fine-tuned version of [SpamAcc/ingredient_prune](https://huggingface.co/SpamAcc/ingredient_prune) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0432

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.312         | 1.82  | 100  | 0.0295          |
| 0.0533        | 3.64  | 200  | 0.0149          |
| 0.0247        | 5.45  | 300  | 0.0136          |
| 0.0149        | 7.27  | 400  | 0.0124          |
| 0.0114        | 9.09  | 500  | 0.0127          |
| 0.0086        | 10.91 | 600  | 0.0127          |
| 0.0075        | 12.73 | 700  | 0.0145          |
| 0.0061        | 14.55 | 800  | 0.0151          |
| 0.0058        | 16.36 | 900  | 0.0161          |
| 0.0044        | 18.18 | 1000 | 0.0169          |
| 0.0039        | 20.0  | 1100 | 0.0199          |
| 0.0044        | 21.82 | 1200 | 0.0181          |
| 0.0035        | 23.64 | 1300 | 0.0230          |
| 0.0039        | 25.45 | 1400 | 0.0226          |
| 0.0028        | 27.27 | 1500 | 0.0234          |
| 0.0026        | 29.09 | 1600 | 0.0272          |
| 0.0023        | 30.91 | 1700 | 0.0261          |
| 0.0028        | 32.73 | 1800 | 0.0254          |
| 0.0018        | 34.55 | 1900 | 0.0268          |
| 0.0022        | 36.36 | 2000 | 0.0303          |
| 0.002         | 38.18 | 2100 | 0.0286          |
| 0.0018        | 40.0  | 2200 | 0.0299          |
| 0.0024        | 41.82 | 2300 | 0.0322          |
| 0.0019        | 43.64 | 2400 | 0.0328          |
| 0.0015        | 45.45 | 2500 | 0.0310          |
| 0.002         | 47.27 | 2600 | 0.0352          |
| 0.0015        | 49.09 | 2700 | 0.0361          |
| 0.0013        | 50.91 | 2800 | 0.0358          |
| 0.0011        | 52.73 | 2900 | 0.0368          |
| 0.0017        | 54.55 | 3000 | 0.0387          |
| 0.0012        | 56.36 | 3100 | 0.0384          |
| 0.0011        | 58.18 | 3200 | 0.0402          |
| 0.0016        | 60.0  | 3300 | 0.0394          |
| 0.0012        | 61.82 | 3400 | 0.0403          |
| 0.0013        | 63.64 | 3500 | 0.0392          |
| 0.0011        | 65.45 | 3600 | 0.0413          |
| 0.0015        | 67.27 | 3700 | 0.0400          |
| 0.0021        | 69.09 | 3800 | 0.0412          |
| 0.0009        | 70.91 | 3900 | 0.0410          |
| 0.0013        | 72.73 | 4000 | 0.0419          |
| 0.0009        | 74.55 | 4100 | 0.0415          |
| 0.0011        | 76.36 | 4200 | 0.0418          |
| 0.0008        | 78.18 | 4300 | 0.0422          |
| 0.0013        | 80.0  | 4400 | 0.0434          |
| 0.0011        | 81.82 | 4500 | 0.0436          |
| 0.0011        | 83.64 | 4600 | 0.0434          |
| 0.0008        | 85.45 | 4700 | 0.0434          |
| 0.0009        | 87.27 | 4800 | 0.0436          |
| 0.0006        | 89.09 | 4900 | 0.0442          |
| 0.0009        | 90.91 | 5000 | 0.0436          |
| 0.001         | 92.73 | 5100 | 0.0434          |
| 0.0008        | 94.55 | 5200 | 0.0433          |
| 0.0013        | 96.36 | 5300 | 0.0434          |
| 0.001         | 98.18 | 5400 | 0.0433          |
| 0.0008        | 100.0 | 5500 | 0.0432          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2