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IndIE

This is the code for the paper titled "IndIE: A Multilingual Open Information Extraction Tool For Indic Languages" accepted in the Findings of IJCNLP-AACL 2023.

You can check out the live deployment of the IndIE here.

Installation

• Git clone.
• Make a new virtual environment.
  • Upgrade pip by pip install -U pip
• Install the necessary libraries by using the following command: pip install -r requirements.txt
  • If you face any difficulty in installing any library then we recommend to install it without version number. Hence, pip will install from the latest version.
  • However, we do not recommend this for stanza library because a different version of stanza library will yield in different dependency parse trees [source].

Download Models

Download the relevant files from here.

Then place the files in the directories such that your directory structure looks like this:

.
├── chunking
│   ├── chunking_model.py
│   ├── crf_chunker.py
│   ├── data
│   │   └── my_tagset_BI.bin
│   └── state_dicts
│       └── model
│           ├── 26_epoch_4.pth.tar
│           └── sklearn_crf_model_v2_pos_mapped_2.pkl

Memory needed

The amount of memory needed varies depending upon the number of languages on which you wish to run your triple extraction.

1. All languages (Hindi/Tamil/Telugu/Urdu)
   • GPU present
     • ~6GB on CPU/RAM
     • ~6GB on GPU
   • GPU absent
     • ~7GB on CPU/RAM
2. Only one language
   • GPU present
     • ~3GB on CPU/RAM
     • ~4GB on GPU
   • GPU absent
     • ~3GB on CPU/RAM

Extracting triples

Specify the language and list of strings in the main.py file.

On GPU, make sure you have same device order on nvidia-smi and PCI bus. Command: export CUDA_DEVICE_ORDER=PCI_BUS_ID

Run

CUDA_VISIBLE_DEVICES=0 python main.py

where 0 is your GPU ID. The code also runs in absence of GPU but takes a little longer. In order to run the code only on CPU, simply omit the GPU ID.

Citation

Will be updated