NegaBot-Product-Criticism-Catcher

🤖 NegaBot: SmolLM 360M – Tweet Sentiment Classifier

<p align="center"> <img width="512" src="https://github.com/user-attachments/assets/18669a85-e7fc-47f6-9183-e7ee33648930" alt="NegaBot Model Illustration"> </p>

NegaBot is a fine-tuned SmolLM-360M model that detects negative product reviews on the X platform (formerly Twitter). It is designed to help brands and analysts identify dissatisfaction or criticism in real-time.

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Training Code:

• NoteBook on GitHub
• SmolLM360M-V2.ipynb (V2 – Sarcasm-aware fine-tuning)

📊 Task

Binary Sentiment Classification:

• Input: A tweet about a product.
• Output: Label 1 (Negative) or 0 (Positive)

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🧠 Model Details

• Architecture: Decoder-only Transformer (SmolLM 360M)
• Base Model: HuggingFaceTB/SmolLM-360M
• Tokenizer: AutoTokenizer (from base model)

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🏋️ Training Details

• Dataset: Custom dataset of 7,920 tweets (from Kaggle)
  • Positive tweets: 5,894
  • Negative tweets: 2,026
• Sequence Length: 256
• Batch Size: 16
• Epochs: 3
• Learning Rate: 5e-5
• Optimizer: AdamW
• Loss Function: CrossEntropyLoss
• Hardware: Trained on NVIDIA Tesla P100 GPU
• Training Time: ~20 minutes

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📈 Evaluation Results

• Observation: Removing text cleaning (keeping emojis and special characters) led to a significant boost in performance on noisy tweet data.

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🆕 Updates

• 🔍 Sarcasm Handling:

  • V1 struggled with sarcastic negative tweets being misclassified as positive
  • V2 is fine-tuned using 1,000 sarcastic synthetic tweets + additional real-world raw samples

• 🧪 Improved Generalization:

  • Training on raw, uncleaned tweets improved performance on informal and emoji-heavy content

• 📦 New Notebook:

  • SmolLM360M-V2.ipynb contains the additional training pipeline for sarcasm-aware performance

• ✅ New Accuracy:

  • Final test accuracy improved to 0.94 on sarcasm-inclusive test set

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🧪 How to Use

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("jatinmehra/NegaBot-Product-Criticism-Catcher")
tokenizer = AutoTokenizer.from_pretrained("jatinmehra/NegaBot-Product-Criticism-Catcher")

text = "This product is awful and broke within a week!"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
predicted_class = torch.argmax(outputs.logits, dim=1).item()

print("Negative" if predicted_class == 1 else "Positive")

Labels

• 0 → Positive (The tweet expresses satisfaction or approval of the product)
• 1 → Negative (The tweet expresses dissatisfaction or criticism)

Acknowledgements

• Base Model: SmolLM-360M
• Dataset: Product sentiment tweets from Kaggle
• Libraries: Hugging Face Transformers, Datasets, PyTorch, Pnadas

Author

Jatin Mehra LinkedIn Profile