README.md

---
title: T9 Oracle Entity Extractor
emoji: 🔬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: mit
hardware: t4-small
---

# T9 Oracle Entity Extractor

**Zero-shot NER for Medical Device Technical Documentation**

This Space provides entity extraction using GLiNER Large (1.7GB) for technical documentation in the medical device domain.

## Features

- **70 Entity Labels** across 10 tiers
- **Zero-shot learning** - no training required
- **Medical device focus** - optimized for endoscope equipment, parts, specifications
- **Hardware:** NVIDIA T4 GPU for fast inference

## Entity Types

### Tier 1: Critical Identifiers
- part_number, component_name, manufacturer, model_number

### Tier 2: Specifications
- pressure, temperature, voltage, current, material, dimensions, flow_rate, power

### Tier 3: Standards & Compliance
- standard_reference (ISO, ASTM, EN, IEC, ANSI), certification, compliance

### Tier 4-10: Additional Labels
- Thread standards, geometry, documentation, operational parameters, manufacturing IDs, medical device specific, visual elements, quality & maintenance

## API Usage

```python
from gradio_client import Client

client = Client("YOUR_USERNAME/t9-oracle-gliner-entity-extractor")

text = "Part Number: A70002-2, Material: SS316L, Pressure: 60 psi"
result = client.predict(text, api_name="/extract")
print(result)
```

## Configuration

- **Model:** urchade/gliner_large-v2.1
- **GPU:** NVIDIA T4 (16GB VRAM)
- **Cost:** $0.60/hour (Persistent)
- **Max input:** 10,000 characters per request

## Project

Part of the T9 Oracle Knowledge Base Extraction System for Auto Sink medical device documentation.