Instructions to use rezaya/fatima_nlp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use rezaya/fatima_nlp with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://rezaya/fatima_nlp") - Notebooks
- Google Colab
- Kaggle
Model description
NLP model for fake news classification using Roberta pre-trained model
Intended uses & limitations
Fake news classification
Training and evaluation data
Trained on 80% of English fake news classification dataset: https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| Hyperparameters | Value |
|---|---|
| name | Adam |
| clipnorm | 0.8 |
| learning_rate | 2.9999999242136255e-05 |
| decay | 0.025 |
| beta_1 | 0.8999999761581421 |
| beta_2 | 0.9990000128746033 |
| epsilon | 9e-09 |
| amsgrad | False |
| training_precision | float32 |
Model Plot
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