nickagge/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