Specialized Models

The Four Sided Triangle framework incorporates several specialized models, each designed to handle specific aspects of the information processing pipeline. This document details each model’s purpose, configuration, and usage.

Model Overview

Our system uses a combination of pre-trained and custom-trained models, each optimized for specific tasks in the pipeline:

1. SciBert: Scientific text understanding and slot filling
2. BART-MNLI: Natural language inference and verification
3. Custom Models: Domain-specific processing
4. Verification Models: Output validation and quality assurance

SciBert Model

Purpose

SciBert is used for scientific text understanding and slot filling. It processes scientific text and extracts structured information.

Configuration

model:
  name: SciBert
  version: "1.0"
  config:
    threshold: 0.85
    max_length: 512
    batch_size: 32

Usage

from models import SciBert

model = SciBert(config)
results = model.process_text("Your scientific text here")

Performance Characteristics

• Processing speed: ~100 tokens/second
• Memory usage: 2-4GB
• GPU utilization: Medium
• Accuracy: 92% on benchmark dataset

BART-MNLI Model

Purpose

BART-MNLI handles natural language inference tasks, verifying relationships between text segments.

Configuration

model:
  name: BART-MNLI
  version: "1.0"
  config:
    confidence_threshold: 0.75
    max_sequence_length: 1024

Usage

from models import BART_MNLI

model = BART_MNLI(config)
verification = model.verify_inference(premise, hypothesis)

Performance Characteristics

• Processing speed: ~150 tokens/second
• Memory usage: 3-5GB
• GPU utilization: High
• Accuracy: 89% on NLI tasks

Custom Models

Domain-Specific Processors

These models are tailored for specific domains or tasks:

1. Text Classifier

   model:
     name: TextClassifier
     version: "1.0"
     config:
       classes: ["class1", "class2"]
       threshold: 0.8
   

2. Entity Extractor

   model:
     name: EntityExtractor
     version: "1.0"
     config:
       entity_types: ["ORG", "PERSON"]
       confidence_threshold: 0.7
   

Usage

from models import TextClassifier, EntityExtractor

classifier = TextClassifier(config)
extractor = EntityExtractor(config)

classification = classifier.classify(text)
entities = extractor.extract(text)

Verification Models

Purpose

These models ensure output quality and validate processing results.

Components

1. Consistency Checker

   model:
     name: ConsistencyChecker
     version: "1.0"
     config:
       validation_rules: ["rule1", "rule2"]
   

2. Quality Validator

   model:
     name: QualityValidator
     version: "1.0"
     config:
       quality_threshold: 0.9
   

Usage

from models import ConsistencyChecker, QualityValidator

checker = ConsistencyChecker(config)
validator = QualityValidator(config)

consistency = checker.check(results)
quality = validator.validate(results)

Model Pipeline Integration

Configuration

pipeline:
  stages:
    - name: scientific_understanding
      model: SciBert
      config:
        threshold: 0.85
    - name: verification
      model: BART-MNLI
      config:
        confidence_threshold: 0.75
    - name: domain_processing
      model: TextClassifier
      config:
        threshold: 0.8
    - name: validation
      model: QualityValidator
      config:
        quality_threshold: 0.9

Execution Flow

1. Input text processing
2. Scientific understanding
3. Verification
4. Domain-specific processing
5. Quality validation

Model Management

Loading and Unloading

from model_manager import ModelManager

manager = ModelManager()
manager.load_model("SciBert")
manager.unload_model("SciBert")

Caching

from model_manager import ModelCache

cache = ModelCache()
cache.set_model("SciBert", model_instance)
model = cache.get_model("SciBert")

Performance Optimization

Memory Management

• Model sharing between processes
• Lazy loading
• Cache management
• Resource cleanup

Batch Processing

• Dynamic batch sizing
• Queue management
• Load balancing

Error Handling

Common Issues

1. Out of memory
2. Model loading failures
3. Input validation errors
4. Processing timeouts

Recovery Strategies

1. Automatic retries
2. Fallback models
3. Error logging
4. Alert generation

Monitoring

Metrics

• Processing time
• Memory usage
• GPU utilization
• Error rates
• Accuracy scores

Logging

• Model operations
• Performance statistics
• Error tracking
• Usage patterns

Development Guidelines

Adding New Models

1. Implement model interface
2. Add configuration
3. Update pipeline
4. Add tests
5. Document changes

Testing

• Unit tests
• Integration tests
• Performance benchmarks
• Validation sets