---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: food_type_classification_model
  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. -->

# food_type_classification_model

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0335
- Accuracy: 0.9946

## Model description

This model can categorize a given food product title into Plant-based ("PLANT_BASED") or Animal-based("ANIMAL_BASED").

## Intended uses & limitations

"whey" is an "ANIMAL_BASED" product derived from Cow's milk. Therefore it must be categorized as as an ANIMAL_BASED food product.

Example usage:
```
from transformers import pipeline

text = "whey"
classifier = pipeline("text-classification", model="nish-j/food_type_classification_model")
classifier(text)

>>> [{'label': 'ANIMAL_BASED', 'score': 0.9941352605819702}]
```
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 325  | 0.0324          | 0.9938   |
| 0.0347        | 2.0   | 650  | 0.0335          | 0.9946   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0