---
title: Structured Intelligence & Grounded Meaning Analyzer
emoji: 👀
colorFrom: yellow
colorTo: gray
sdk: gradio
app_file: app.py
pinned: false
---

# Structured Intelligence & Grounded Meaning Analyzer

Try the interactive SIGMA reasoning tool via
[Hugging Face Space Link](https://huggingface.co/spaces/morinousagi/nlp-intelligence-analyzer) | [UI snapshot](app_ui.png)

This project was developed using prompt engineering techniques with **ChatGPT GPT-5.5**.
```
Manual code review - notes:

roberta-large-mnli uses 0 for contradiction and 2 for entailment, while 
GLUE MNLI uses 0 for entailment and 2 for contradiction.
Generated code (for evaluate_mnli.py) applied 0 for contradiction and 2 for entailment. 

>>> Upon intervention, AI regenerated the code to apply label re-mapping to handle the differences.
```
---

## Model Stack

### Fact Extraction 
- `spaCy en_core_web_sm`
- Explicit fact extraction, extracts structured factual triples:
`Subject → Action → Object`


### Inference Validation 
- `roberta-large-mnli` (Multi-Genre Natural Language Inference)
- Transformer model to evaluate candidate hypotheses against the original passage, classifying each hypothesis as:
   - ENTAILMENT
   - NEUTRAL
   - CONTRADICTION

- Confidence scores are derived from the model's softmax probability distribution over the three inference classes.

### Summarization
- `facebook/bart-large-cnn`
- Generates concise analytic summaries


### Technical Notes
- Uses conservative syntactic extraction to avoid speculative relation generation
- Separates confirmed facts from analytical assessments
- Built entirely with pretrained transformer models
- Optimized for CPU deployment on Hugging Face Spaces

---

## Architecture
```
Input Text
   ↓
[NER + Relation Extraction]
   ↓
Explicit Fact List
   ↓
[NLI Model - Entailment Testing]
   ↓
Validated Implicit Inferences
   ↓
[Summarization Model]
   ↓
Intelligence Brief Output
```
### Architecture of Inference Layer
```
Structured Facts
       ↓
Embedded Clause Extraction
       ↓
Standalone Hypothesis Generation
       ↓
MNLI Entailment Test
       ↓
Filter (confidence threshold)
       ↓
Implicit Inference Output
```

---

## Validation

Validated pretrained reasoning model against standardized benchmark with tracked metrics and reproducible experiment logging.

Evaluated `roberta-large-mnli` on a `GLUE MNLI` validation split and track results with **MLflow**. 

Label re-mapping is required to handle: 
- Model roberta-large-mnli: 0 = contradiction | 1 = neutral | 2 = entailment
- Dataset GLUE MNLI: 0 = entailment | 1 = neutral | 2 = contradiction

### Results
![mlflow](mlflow_eval_run.png)

<img src="confusion_matrix.png" width="50%">

---

## Project Structure
```
/
├── src/                     # Core NLP logic
│   ├── __init__.py
│   ├── fact_extractor.py
│   ├── inference_engine.py
│   ├── summarizer.py
│   ├── pipeline.py
│
├── app.py                   # UI / deployment entrypoint
├── requirements.txt
└── README.md
```