🧠 SentimentClassifier-BERT-RegulatoryCompliance

A BERT-based sentiment analysis model fine-tuned on regulatory feedback and compliance-related text. This model classifies input text into Positive, Neutral, or Negative, making it well-suited for analyzing complaints, formal feedback, and regulatory communication.

✨ Model Highlights

📌 Based on bert-base-uncased
🔍 Fine-tuned on a custom dataset of labeled regulatory feedback
⚡ Supports prediction of 3 classes: Positive, Neutral, Negative
🧠 Built using Hugging Face Transformers and PyTorch

🧠 Intended Uses

✅ Regulatory and compliance feedback classification
✅ Complaint monitoring and triaging
✅ Customer sentiment analysis for compliance departments

🚫 Limitations

❌ Not optimized for multi-language input (English only)
📏 Input longer than 128 tokens will be truncated
🤔 Model may misinterpret informal or slang language
⚠️ Not intended to replace expert human judgment in legal matters

🏋️‍♂️ Training Details

Attribute	Value
Base Model	`bert-base-uncased`
Dataset	Custom `.txt` file with feedbacks
Labels	Negative (0), Neutral (1), Positive (2)
Max Token Length	128
Epochs	3
Batch Size	16
Optimizer	AdamW
Loss Function	CrossEntropyLoss
Framework	PyTorch + Transformers
Hardware	CUDA-enabled GPU

📊 Evaluation Metrics

Metric	Score
Accuracy	0.84
Precision	0.85
Recall	0.84
F1 Score	0.85

🔎 Label Mapping

Label ID	Sentiment
0	Negative
1	Neutral
2	Positive

🚀 Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import torch.nn.functional as F

model_name = "your-username/sentiment-bert-regulatory-compliance"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()

def predict(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    with torch.no_grad():
        outputs = model(**inputs)
        probs = F.softmax(outputs.logits, dim=1)
        pred = torch.argmax(probs, dim=1).item()
        label_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
        return f"Sentiment: {label_map[pred]} (Confidence: {probs[0][pred]:.2f})"

# Example
print(predict("The issue was resolved promptly and professionally."))

📁 Repository Structure

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├── model/               # Fine-tuned model files (pytorch_model.bin, config.json)
├── tokenizer/           # Tokenizer config and vocab
├── training_script.py   # Training code
├── feedbacks.txt        # Source dataset
├── README.md            # Model card

🤝 Contributing

Contributions are welcome! Feel free to open an issue or pull request to improve the model or its documentation.