paladin-improved

PALADIM Sentiment Analysis (Improved)

A balanced, production-ready sentiment analysis model using PALADIM architecture

🎯 Model Performance

• Overall Accuracy: 78.68%
• Positive Sentiment: 74.61% accuracy
• Negative Sentiment: 82.87% accuracy
• Training Data: 22,500 balanced samples from IMDb
• Balanced Training: Equal positive/negative samples (no bias!)

📊 Test Results

All predictions correct with high confidence:

🚀 Quick Start

from peft import PeftModel
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load model
base_model = AutoModelForSequenceClassification.from_pretrained(
    "prajjwal1/bert-tiny",
    num_labels=2
)
model = PeftModel.from_pretrained(base_model, "nickagge/paladim-sentiment-improved")
tokenizer = AutoTokenizer.from_pretrained("nickagge/paladim-sentiment-improved")

# Predict
text = "This movie was fantastic!"
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model(**inputs)
prediction = torch.argmax(outputs.logits, dim=-1).item()

sentiment = "POSITIVE" if prediction == 1 else "NEGATIVE"
confidence = torch.softmax(outputs.logits, dim=-1).max().item()

print(f"{sentiment} ({confidence*100:.1f}%)")

Model Details

PALADIM (Pre Adaptive Learning Architecture of Dual-Process Hebbian-MoE Schema) is a continual learning system that combines:

• Stable Core: Pre-trained BERT-tiny (4.4M parameters) - frozen
• Plastic Memory: LoRA adapters (12,546 trainable = 0.29%)
• MoE Layer: Mixture of Experts routing
• Consolidation: EWC + Knowledge Distillation
• Meta-Controller: Adaptive learning triggers
• Replay Buffer: Anti-forgetting mechanism

Model Description

This model is fine-tuned for binary sentiment classification (positive/negative) with balanced training to avoid prediction bias. It achieves 78.68% accuracy with high confidence predictions on both sentiment classes.

• Developed by: nickagge
• Model type: BERT-tiny with LoRA adapters
• Language(s): English
• License: MIT
• Finetuned from model: prajjwal1/bert-tiny

Training Details

Training Data

• Dataset: IMDb movie reviews
• Training samples: 22,500 (11,250 positive + 11,250 negative)
• Validation samples: 2,500 (balanced)
• Max sequence length: 128 tokens

Training Procedure

Training Hyperparameters

• Training regime: fp32 (CPU training)
• Epochs: 3
• Batch size: 16
• Learning rate: 5e-4
• Optimizer: AdamW
• LoRA rank (r): 8
• LoRA alpha: 16
• LoRA dropout: 0.1
• Target modules: ["query", "value", "key"]

Training Progress

Evaluation

Testing Data & Metrics

• Test set: 2,500 balanced samples from IMDb
• Metrics: Accuracy (overall and per-class)
• Positive class accuracy: 74.61%
• Negative class accuracy: 82.87%

Results

✅ Balanced predictions - No systematic bias
✅ High confidence - 86-96% on test sentences
✅ Consistent performance - Both classes above 74%

Uses

Direct Use

• Sentiment analysis for movie reviews, product reviews, customer feedback
• Social media sentiment monitoring
• Content moderation and filtering
• Market research and opinion mining

Limitations

• Trained specifically on movie reviews (may need domain adaptation for other contexts)
• Binary classification only (positive/negative, no neutral class)
• English language only
• Max sequence length: 128 tokens

Citation

@misc{paladim-sentiment-improved,
  title={PALADIM Sentiment Analysis Model},
  author={nickagge},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/nickagge/paladim-sentiment-improved}}
}

Related Models

• Original PALADIM Model
• BERT-tiny Base

Framework versions

• PEFT 0.18.0