MWO-NER Model Documentation

Introduction

MWO-NER is a Named Entity Recognition (NER) model based on the Flair library, designed specifically for identifying entities within Maintenance Work Order (MWO) documents. The model classifies text into three distinct categories: Item, Activity, and Observation.

Architecture

The model utilizes the Flair library, which is built on top of PyTorch, to perform token classification tasks. Flair is known for its ability to handle sequence tagging, making it well-suited for the NER tasks required by MWO documents.

Training

This model was trained using a dataset specific to MWO entities, ensuring that it can accurately classify the defined categories. The dataset used is referred to as mwo_ner and contains examples relevant to the maintenance industry.

Guide: Running Locally

To run the MWO-NER model locally, follow these steps:

1. Prerequisites: Ensure you have Python and PyTorch installed.
2. Install Flair: Use the command pip install flair to install the Flair library.
3. Load the Model: Download the MWO-NER model files from the Hugging Face repository.
4. Run Inference: Use Flair's easy-to-use API to load the model and run predictions on your text.
5. GPU Acceleration: To improve performance, consider using cloud GPU services like AWS EC2, Google Cloud Platform, or Azure.

License

The model and its components are subject to the licensing agreements as specified in the Hugging Face repository. Please review the terms to ensure compliance with usage and distribution rights.