Instructions to use ikekobby/fake-real-news-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ikekobby/fake-real-news-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ikekobby/fake-real-news-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ikekobby/fake-real-news-classifier") model = AutoModelForSequenceClassification.from_pretrained("ikekobby/fake-real-news-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Model based trained on 30% of the kaggle public data on fake and reals news article. The model achieved an auc of 1.0, precision, recall and f1score all at score of 1.0.
- Task;- The predictor classifies news articles into either fake or real news.
- It is a transformer model trained using the
ktrainlibrary on 30% of dataset of size 194MB after preprocessing. - Metrics used are recall,, precision, f1score and roc_auc_score.
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