roberta-bilstm-attention-sentiment

🤖 Sentiment Classifier with Sarcasm Detection

This model combines roberta-base embeddings with a BiLSTM layer and attention mechanism to classify the sentiment of movie reviews, even when the language includes sarcasm, irony, or mixed emotional signals.

🧩 Architecture

• Base Encoder: RoBERTa (frozen)
• Sequence Modeling: Bidirectional LSTM (BiLSTM)
• Attention Layer: Learnable scalar attention weights
• Classifier: Fully connected + Softmax

This model takes the output of RoBERTa, processes it through a BiLSTM, applies attention to focus on important parts of the sentence, and classifies sentiment as Positive (1) or Negative (0).

💡 Why this model?

Standard BERT-based sentiment classifiers often fail on subtle language such as:

• "If I had a dollar for every cliché in this film, I’d be rich." → sarcastic → Negative
• "It’s fine. Just fine. Totally… fine." → sarcastic tone → Negative
• "The visuals were stunning, but the plot was as dead as a doorknob." → contrast → Negative

This model aims to address that by combining contextual language modeling (RoBERTa) with sequence learning (BiLSTM) and attention-based interpretability.

📊 Training Details

• Dataset: IMDB (2-class, 25k reviews)
• Training Subset: 2,000 samples for prototype
• Loss: CrossEntropyLoss
• Optimizer: Adam
• Epochs: 3
• Hardware: Google Colab (T4 GPU)
• Additional Inputs: Custom sarcastic and ambiguous examples

🧪 Example Inputs

🚀 How to Use

from transformers import RobertaTokenizer
from my_model import RobertaBiLSTMAttention  # the custom model
import torch

tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaBiLSTMAttention()
model.load_state_dict(torch.load("pytorch_model.bin"))
model.eval()

text = "It’s fine. Just fine. Totally… fine."
tokens = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
logits = model(tokens["input_ids"], tokens["attention_mask"])
pred = torch.argmax(logits, dim=1).item()
print("Sentiment:", "Positive" if pred == 1 else "Negative")