---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: news_classification_model
  results: []
---


# news_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:
- Train Loss: 0.0865
- Train Accuracy: 0.9712
- Validation Loss: 1.5070
- Validation Accuracy: 0.7252
- Epoch: 1

## Model description

Output Labels of this Model are:

- 'HEALTH & MEDICINE': 0,
- 'ENTERTAINMENT': 1,
- 'FINANCE': 2,
- 'SCIENCE & TECHNOLOGY': 3,
- 'WORLD NEWS': 4,
- 'EDUCATION': 5,
- 'SOCIETY': 6,
- 'POLITICS': 7,
- 'ENVIRONMENT': 8,
- 'BUSINESS': 9,
- 'MEDIA': 10,
- 'FOOD': 11,
- 'CRIME': 12,
- 'LIFESTYLE': 13,
- 'SPORTS': 14

## Intended uses & limitations

This models classifies News headlines into different categories mentioned above.

## Training and evaluation data

Model was trained and evaluated using the dataset available here: https://www.kaggle.com/datasets/rmisra/news-category-dataset

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'Adam',
- 'weight_decay': None,
- 'clipnorm': None,
- 'global_clipnorm': None,
- 'clipvalue': None,
- 'use_ema': False,
- 'ema_momentum': 0.99,
- 'ema_overwrite_frequency': None,
- 'jit_compile': True,
- 'is_legacy_optimizer': False,
- 'learning_rate': 5e-05,
- 'beta_1': 0.9,
- 'beta_2': 0.999,
- 'epsilon': 1e-07,
- 'amsgrad': False}
  
- training_precision: float32

### Training results

Upon the second round of trainning, the results were as follows:

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.1022     | 0.9666         | 1.4192          | 0.7276              | 0     |
| 0.0865     | 0.9712         | 1.5070          | 0.7252              | 1     |


### Framework versions

- Transformers 4.33.1
- TensorFlow 2.13.0
- Tokenizers 0.13.3