plexus-accelerator-ner-v3

🌌 plexus-accelerator-ner-v3

plexus-accelerator-ner-v3 is a high-performance, multilingual Named Entity Recognition (NER) engine. It serves as the critical Triage and Accelerator Layer for the Plexus V4.2 Adaptive Intelligence Engine, a system designed to assist users with executive dysfunction and ADHD through proactive AI coaching.

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🏗 System Role: The "Accelerator" Layer

In the Plexus V4.2 architecture, this model sits at Layer 1 (The Accelerator). Its primary goal is to resolve user intent with minimal computational overhead.

• Semantic Routing: Extracts entities (Project Names, Deadlines, Categories) to create anonymized intent templates.
• LLM Bypass: Allows the system to execute "Automatic Paths" (>98% confidence) without invoking expensive LLMs like GPT-4o, reducing latency by ~85%.
• Privacy-First: Operates as a "Data Sanitizer," ensuring that personally identifiable information (PII) is tokenized before being passed to cloud-based reasoning layers.

📊 Technical Specifications

Model Card

Training Hyperparameters

The model was fine-tuned using a high-precision, curated dataset of executive function commands and organizational logic.

• Optimizer: AdamW with Weight Decay (0.01)
• Learning Rate: $2 \times 10^{-5}$
• Scheduler: Linear warmup with decay
• Epochs: 5
• Batch Size: 16 (on NVIDIA T4 GPU)

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📈 Performance Metrics (Test Set)

The model demonstrates state-of-the-art accuracy for domain-specific entity extraction in the productivity space.

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🛠 Usage & Implementation

Direct Inference

from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

# Initialize the Plexus Accelerator
tokenizer = AutoTokenizer.from_pretrained("nour833/plexus-accelerator-ner-v3")
model = AutoModelForTokenClassification.from_pretrained("nour833/plexus-accelerator-ner-v3")

nlp = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")

# Sample "Guardian Angel" Scenario
prompt = "Remind me to finalize the investment deck before the 4 PM meeting on Friday."
results = nlp(prompt)

for entity in results:
    print(f"Entity: {entity['word']} | Label: {entity['entity_group']} | Score: {entity['score']:.4f}")

Entity Schema

The model is fine-tuned to recognize the following specialized labels:

• B-PROJECT: Start of a project or goal name.
• B-TIME: Temporal constraints and deadlines.
• B-CATEGORY: Task classification (Work, Social, Health).
• B-URGENCY: Priority markers in natural language.

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🛡 Ethical Considerations & Privacy

This model is built on the principle of Local-First AI. By performing NER locally, Plexus minimizes the surface area for data leaks. This is particularly critical for users tracking sensitive medical or professional data.

🔗 Project Context

This model is part of the Plexus Project, an initiative to democratize executive function support.

• Developer: Nour Kooli
• Project Hub: GitHub Repository