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

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

# bert_uncased_fake_news

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the kaggle fake news detection english [dataset](https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english).
It achieves the following results on the evaluation set:
- Train Loss: 0.0015
- Train Accuracy: 0.9997
- Validation Loss: 0.0048
- Validation Accuracy: 0.9983
- Test F1 Score (macro): 0.9989

## How to use

You can use this model directly with a pipeline for text classification:

```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="rasyosef/bert_uncased_fake_news")
>>> classifier(["Wow! Talk about clueless! Austen Fletcher approaches anti-Trump protesters and gets clueless answers on why they re against Trump:Thought you might enjoy this  @PrisonPlanet @allidoisowen @JackPosobiec pic.twitter.com/kdYm2WlfdB  austen fletcher (@fleccas) July 17, 2017"])

[{'label': 'Fake News', 'score': 0.9999557733535767}]
```

## Model description

More information needed

## Intended uses & limitations

More information needed

### 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 2814, 'end_learning_rate': 0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32

### Training results



### Framework versions

- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0