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# 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

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "bert_uncased_fake_news", "results": []}]}
transformers tf distilbert text-classification generated_from_keras_callback base_model:distilbert-base-uncased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert_uncased_fake_news

This model is a fine-tuned version of distilbert-base-uncased on the kaggle fake news detection english dataset.
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:



## 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

# bert_uncased_fake_news

This model is a fine-tuned version of distilbert-base-uncased on the kaggle fake news detection english dataset.
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: ## 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
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert_uncased_fake_news

This model is a fine-tuned version of distilbert-base-uncased on the kaggle fake news detection english dataset.
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: ## 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
TAGS
#transformers #tf #distilbert #text-classification #generated_from_keras_callback #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert_uncased_fake_news

This model is a fine-tuned version of distilbert-base-uncased on the kaggle fake news detection english dataset.
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:## 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