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MultiNERD-NER

NER model repo for MultiNERD dataset from HF 🤗

Resources

Following resources were utitlized to develop this project:
Kaggle - GPU T4x2
Google Colab - GPU T4x1
Dataset: https://huggingface.co/datasets/Babelscape/multinerd
Pretrained model: https://huggingface.co/roberta-base

The fine-tuned models and dataset have been uploaded and distributed for public good.
Find them on Kaggle: https://www.kaggle.com/datasets/jayantyadav/multinerd-ner-models/

The findings and limitations have been recorded in MultiNERD_NER__RISE.pdf.
The english subset of the dataset has been shared under the folder dataset.

Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

The project currenlty runs on jupyter notebook and requires CUDA enabled GPU.

Installation

  1. Clone the repo 
    git clone https://github.com/jayant-yadav/RISE-NER.git
  2. Create virtual environment and activate it 
    python -m venv './NER' --upgrade-deps
  3. cd into the local repository and install python packages 
    pip install -r ./requirement.txt

Usage

Either spin up your jupyter notebook server or VSCode in order to run .ipynb scripts.

finetuning.ipynb:

  • Contains code for finetuning RoBERTa-base models.
  • Run code line by line. Comments are added for better understanding.
  • Toggle is_systemB variable to switch from system A to system B.
  • Take note of model file name after it is saved. The file path will be used for evalution later.

evalution.ipynb:

  • Contains code for evaluting fine-tuned models on Test dataset.
  • Run code line by line. Comments are added for better understanding.
  • Toggle is_systemB variable to switch from system A to system B.
  • Use the appropriate model file path to run the correct model checkpoints.

In case of issues while installing dependencies from requirements.txt :

  • Uncomment !pip install code in respective .ipynb to reinstall the required dependencies of that notebook.