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---
language: en
license: mit
library_name: pytorch
tags:
- shakespeare
- text-classification
- bert
- pytorch
- nlp
datasets:
- lanretto/shakespeare-vs-modern-dialogue
---
# 🎭 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
| Metric | Value |
|--------|-------|
| **Accuracy** | 0.9835 |
| **F1-Score** | 0.9685 |
| **Test Samples** | 40,626 |
| **Avg Confidence** | 0.9938 |
### Comparison with Original Implementation
| Model | Accuracy | F1-Score |
|-------|----------|----------|
| Original (HF Trainer) | 0.9820 | 0.9658 |
| **PyTorch Manual** | **0.9835** | **0.9685** |
## 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
```python
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
```
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