shakespeare-authenticator-pytorch

🎭 Shakespeare Authenticator - PyTorch Implementation

Model Description

This is a PyTorch manual implementation of the Shakespeare Authenticator model, distinguishing between authentic Shakespearean text and modern writing. This model was built from scratch using raw PyTorch (without Hugging Face Trainer) for educational purposes.

Model Performance

Comparison with Original Implementation

Training Details

• Architecture: BERT-base + Custom Classification Head
• Training Approach: Manual PyTorch training loop
• Learning Rates: BERT (2e-5), Classifier (1e-4)
• Epochs: 3
• Batch Size: 128
• Best Epoch: 3
• Best Validation Accuracy: 0.9849

Model Architecture

class ShakespeareClassifier(nn.Module):
    def __init__(self, bert_model, num_classes=2, dropout_rate=0.1):
        super().__init__()
        self.bert = bert_model
        self.dropout = nn.Dropout(dropout_rate)
        self.classifier = nn.Linear(768, num_classes)

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs.pooler_output
        x = self.dropout(pooled_output)
        logits = self.classifier(x)
        return logits