| --- |
| 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 |
| |
