| --- |
| language: |
| - en |
| --- |
| ### Model Description |
| This model is a multiclass classification model trained on the Liar 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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| The model classifies the input text into one of 6 target classes. |
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| Bias: The model may inherit biases present in the training data, and it's important to be aware of potential biases in the predictions. |
|
|
| ### Code Implementation |
| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| |
| # Load model directly |
| tokenizer = AutoTokenizer.from_pretrained( |
| "Arjun24420/BERT-FakeNews-Classification") |
| model = AutoModelForSequenceClassification.from_pretrained( |
| "Arjun24420/BERT-FakeNews-Classification") |
| |
| # Define class labels mapping |
| class_mapping = { |
| 0: 'half-true', |
| 1: 'mostly-true', |
| 2: 'false', |
| 3: 'true', |
| 4: 'barely-true', |
| 5: 'pants-fire' |
| } |
| |
| |
| def predict(text): |
| # Tokenize the input text and move tensors to the GPU if available |
| inputs = tokenizer(text, padding=True, truncation=True, |
| max_length=512, return_tensors="pt") |
| |
| # Get model output (logits) |
| outputs = model(**inputs) |
| |
| probs = outputs.logits.softmax(1) |
| # Get the probabilities for each class |
| class_probabilities = {class_mapping[i]: probs[0, i].item() |
| for i in range(probs.shape[1])} |
| |
| return class_probabilities |
| |
| |
| ``` |
|
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|
