NER model repo for MultiNERD dataset from HF 🤗

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.

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

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

• Install Jupyter notebook as such (or visit https://jupyter.org/install)

  pip install notebook

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

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.