gliner multitask v1.0

Introduction

GLiNER-Multitask is a versatile information extraction model designed for a variety of natural language processing tasks such as Named Entity Recognition (NER), relation extraction, summarization, sentiment extraction, and more. It leverages a bidirectional transformer encoder architecture similar to BERT and supports custom prompts for task-specific applications.

Architecture

GLiNER-Multitask employs a bidirectional transformer encoder architecture, ensuring high generalization and computational efficiency. Despite its compact size, the model achieves state-of-the-art performance on zero-shot NER benchmarks and handles diverse tasks, providing a powerful tool for natural language processing applications.

Training

The model is trained on synthetic multi-task datasets, achieving robust performance across various benchmarks. It offers capabilities in key tasks like NER, relation extraction, summarization, sentiment extraction, and question-answering, among others.

Guide: Running Locally

1. Install the GLiNER Python Library:

   pip install gliner
   

2. Load the Model:

   from gliner import GLiNER
   model = GLiNER.from_pretrained("knowledgator/gliner-multitask-v1.0")
   

3. Perform Tasks: Use the model for tasks such as NER or relation extraction by inputting text and specifying labels.

4. Cloud GPUs: For faster processing, consider using cloud services with GPU support like AWS, Google Cloud, or Azure.

License

GLiNER-Multitask is available under the Apache 2.0 license, allowing for free use and distribution with minimal restrictions.