gliner

🔎 NER (Named Entity Recognition) Application with Gliner 🛠️

An example of an API which extracts named entities from a provided user query

BrainUp is an AI-driven self-improvement app created for Hack the Hill 2024. It analyzes saved Instagram reels and posts to recommend productive activities and classes based on user interests. Built with ReactJS for the frontend and a Flask API for the backend, BrainUp leverages AI models like HDBSCAN, MPnet-base v2, and Mistral for data analysis,

Leverage ModernGLiNER's capabilities using LitServe.

GLiner-TransbronchialBiopsy is a specialized NER system for extracting medical entities from transbronchial biopsy reports, focused on transplant rejection.

NER on PDF file using Rust

A simple tool to anonymize LLM prompts.

Named Entity Recognition with OpenVINO

🛡️ Text Anonymizer using NuNER Zero-shot An academic project that auto-detects & masks sensitive entities (names, orgs, locations) in .txt/.docx files using Zero-shot NER. Features CLI & REST API. Replaces data with realistic fakes (Faker) or [REDACTED]. Built with Python, Flask, GLiNER.

Langchain compatible GraphRAG implementation

AI-powered platform for OSINT intelligence analysis. Features archive discovery with hypothesis-driven investigation, GLiNER entity extraction, Mapbox geospatial visualization, network analysis, and document processing. Built with FastAPI, Next.js, Weaviate, and DSPy.

Text preprocessing and PII anonymisation for NLP/ML. ONNX NER ensemble, language detection, stopword removal. Built for statistical ML and language models.

Python bindings to Inference engine for GLiNER models written in Rust

Comparative study of parameter-efficient fine-tuning (PEFT) strategies for biomedical NER on top of GLiNER — including soft prompt tuning, embedding injection, and a custom in-place embedding extension that matches full fine-tuning performance at 13% of trainable parameters.

A fine-tuned GLiNER2 model specialized in Named Entity Recognition for Mexican legal documents in Spanish. Suitable for extracting or redacting PII data.

Scan Local Directory For PII

Long-context experiments with GLiNER for documents that exceed 512-token windows.

Training utilities and recipes for GLiNER named-entity recognition models.

Performance benchmarks for GLiNER named-entity recognition models across ONNX runtimes.