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README.md

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-roberta-bilstm-attention-sentiment model
+# 🤖 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
+
+| Input | Prediction |
+|-------|------------|
+| "If I had a dollar for every cliché..." | 🔴 Negative |
+| "It’s fine. Just fine. Totally fine."   | 🔴 Negative |
+| "The acting was great, but the plot..." | 🔴 Negative |
+| "Brilliant visuals and solid pacing."   | 🟢 Positive |
+
+## 🚀 How to Use
+
+```python
+from transformers import RobertaTokenizer
+from my_model import RobertaBiLSTMAttention  # Your 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")