Instructions to use Arjun24420/BERT-FakeOrReal-BinaryClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Arjun24420/BERT-FakeOrReal-BinaryClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Arjun24420/BERT-FakeOrReal-BinaryClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Arjun24420/BERT-FakeOrReal-BinaryClassification") model = AutoModelForSequenceClassification.from_pretrained("Arjun24420/BERT-FakeOrReal-BinaryClassification") - Notebooks
- Google Colab
- Kaggle
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## Model Details
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This model is a
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It takes the title of the news article and classifies it into Reliable or Unreliable news.
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## Model Details
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This model is a Binary classification model fine-tuned on the FakeOrRealNews Dataset using the BERT (bert-base-uncased) architecture. The primary task is to classify news articles into different categories, making it suitable for fake news detection. BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based model known for its effectiveness in natural language processing tasks.
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It takes the title of the news article and classifies it into Reliable or Unreliable news.
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