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.
- Git clone.
- Make a new virtual environment.
- Upgrade pip by
pip install -U pip
- Upgrade pip by
- 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 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
The amount of memory needed varies depending upon the number of languages on which you wish to run your triple extraction.
- All languages (Hindi/Tamil/Telugu/Urdu)
- GPU present
- ~6GB on CPU/RAM
- ~6GB on GPU
- GPU absent
- ~7GB on CPU/RAM
- GPU present
- Only one language
- GPU present
- ~3GB on CPU/RAM
- ~4GB on GPU
- GPU absent
- ~3GB on CPU/RAM
- GPU present
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.
Will be updated