This contains script that reimplements KAPING framework (Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, Baek et al. 2023 URL)
The process for KAPING will work as follows:
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at least python3.8 (tested on python3.8, python3.10, python 3.11)
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After cloning the repository, install the requirements
$ pip install -r requirements.txt.
├── README.md
├── arguments.py
├── kaping
│ ├── entity_extractor.py
│ ├── entity_injection.py
│ ├── entity_verbalization.py
│ └── model.py
├── qa
│ ├── qa_evaluate.py
│ ├── qa_inference.py
│ └── qa_preprocessing.py
├── requirements.txt
└── run.py - With default, this script will run a QA-inference model using Bert-large-uncased with KAPING (retrieved top-10-triples) to create prompt on CPU
$ python run.py --input <mintaka_dataset.json> - To check all arguments
$ python run.py -h
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Pipeline are designed to treat Question and Context together as a Prompt and fed into the pipeline to request model to generate answer, not find the answer from the given inputs. Thus the inference task used are text2text-generation or text-generation depending on which model is compatible.
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Available to test is
gpt2,t5-small,t5-base,t5-large, but if you have sufficient space resource, you can try largert5. Also, if you want to try other models, please check back on https://huggingface.co/.