https://github.com/zjunlp/knowprompt
[WWW 2022] KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
https://github.com/zjunlp/knowprompt
dataset dialogre few-shot-learning knowledge-informed-prompt-learning knowprompt prompt prompt-learning prompt-tuning pytorch re relation-extraction semeval tacred text-classification
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[WWW 2022] KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
- Host: GitHub
- URL: https://github.com/zjunlp/knowprompt
- Owner: zjunlp
- License: mit
- Created: 2021-10-21T06:40:36.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2023-06-20T02:47:56.000Z (over 2 years ago)
- Last Synced: 2025-05-09T22:34:33.107Z (5 months ago)
- Topics: dataset, dialogre, few-shot-learning, knowledge-informed-prompt-learning, knowprompt, prompt, prompt-learning, prompt-tuning, pytorch, re, relation-extraction, semeval, tacred, text-classification
- Language: Python
- Homepage:
- Size: 27.9 MB
- Stars: 201
- Watchers: 6
- Forks: 34
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# KnowPrompt
Code and datasets for the WWW2022 paper [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://arxiv.org/pdf/2104.07650.pdf).
- ❗NOTE: We provide a paper-list at [PromptKG](https://github.com/zjunlp/PromptKG) and open-source [KnowLM](https://github.com/zjunlp/KnowLM), a knowledgeable large language model framework with pre-training and instruction fine-tuning code (supports multi-machine multi-GPU setup).
# What's New
## Apr,5 2023
* Our follow-up paper on schema-aware prompting KGC [Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction](https://arxiv.org/abs/2210.10709) has been accepted by SIGIR2023. The project address is visible at [RAP](https://github.com/zjunlp/RAP).
## Dec,7 2022
* We release a checkpoint for `SemEval` dataset at [huggingface model hub](https://huggingface.co/zjunlp/KnowPrompt)
## Sept,21 2022
* Our follow-up paper on retrieval-augmented prompting NLP [Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning](https://arxiv.org/abs/2205.14704) has been accepted by NeurIPS2022. The project address is visible at [RetroPrompt](https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt).
## March,30 2022
* Our follow-up paper on retrieval-enhanced prompting RE [Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning](https://arxiv.org/abs/2205.02355) has been accepted by SIGIR2022. The project address is visible at [RetrievalRE](https://github.com/zjunlp/PromptKG/tree/main/research/RetrievalRE).
## Jan,14 2022
* Our paper [KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction](https://arxiv.org/abs/2104.07650) has been accepted by WWW2022.
# Requirements
It is recommended to use a virtual environment to run KnowPrompt.
```bash
conda create -n knowprompt python=3.8
conda activate knowprompt
```
To install requirements:
```
pip install -r requirements.txt
```
Datasets
==========
We provide all the datasets and prompts used in our experiments.
+ [[SEMEVAL]](dataset/semeval)
+ [[DialogRE]](dataset/dialogue)
+ [[TACRED-Revisit]](dataset/tacrev)
+ [[Re-TACRED]](dataset/retacred)
+ [[TACRED]](dataset/tacred)
The expected structure of files is:
```
knowprompt
|-- dataset
| |-- semeval
| | |-- train.txt
| | |-- dev.txt
| | |-- test.txt
| | |-- temp.txt
| | |-- rel2id.json
| |-- dialogue
| | |-- train.json
| | |-- dev.json
| | |-- test.json
| | |-- rel2id.json
| |-- tacred
| | |-- train.txt
| | |-- dev.txt
| | |-- test.txt
| | |-- temp.txt
| | |-- rel2id.json
| |-- tacrev
| | |-- train.txt
| | |-- dev.txt
| | |-- test.txt
| | |-- temp.txt
| | |-- rel2id.json
| |-- retacred
| | |-- train.txt
| | |-- dev.txt
| | |-- test.txt
| | |-- temp.txt
| | |-- rel2id.json
|-- scripts
| |-- semeval.sh
| |-- dialogue.sh
| |-- ...
```
Run the experiments
==========
## Initialize the answer words
Use the comand below to get the answer words to use in the training.
```shell
python get_label_word.py --model_name_or_path bert-large-uncased --dataset_name semeval
```
The `{answer_words}.pt`will be saved in the dataset, you need to assign the `model_name_or_path` and `dataset_name` in the `get_label_word.py`.
## Split dataset
Download the data first, and put it to `dataset` folder. Run the comand below, and get the few shot dataset.
```shell
python generate_k_shot.py --data_dir ./dataset --k 8 --dataset semeval
cd dataset
cd semeval
cp rel2id.json val.txt test.txt ./k-shot/8-1
```
You need to modify the `k` and `dataset` to assign k-shot and dataset. Here we default seed as 1,2,3,4,5 to split each k-shot, you can revise it in the `generate_k_shot.py`
## Let's run
Our script code can automatically run the experiments in 8-shot, 16-shot, 32-shot and
standard supervised settings with both the procedures of train, eval and test. We just choose the random seed to be 1 as an example in our code. Actually you can perform multiple experments with different seeds.
#### Example for SEMEVAL
Train the KonwPrompt model on SEMEVAL with the following command:
```bash
>> bash scripts/semeval.sh # for roberta-large
```
As the scripts for `TACRED-Revist`, `Re-TACRED`, `Wiki80` included in our paper are also provided, you just need to run it like above example.
#### Example for DialogRE
As the data format of DialogRE is very different from other dataset, Class of processor is also different.
Train the KonwPrompt model on DialogRE with the following command:
```bash
>> bash scripts/dialogue.sh # for roberta-base
```
## More emperical results
We report emperical results on more datasets in the EMNLP 2022 (Findings) paper "[Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study](https://arxiv.org/pdf/2210.10678.pdf)" [[code](https://github.com/zjunlp/LREBench)].
# Acknowledgement
Part of our code is borrowed from [code](https://github.com/thunlp/PTR) of [PTR: Prompt Tuning with Rules for Text Classification](https://arxiv.org/abs/2105.11259), many thanks.
# Citation
If you use the code, please cite the following paper:
```bibtex
@inproceedings{DBLP:conf/www/ChenZXDYTHSC22,
author = {Xiang Chen and
Ningyu Zhang and
Xin Xie and
Shumin Deng and
Yunzhi Yao and
Chuanqi Tan and
Fei Huang and
Luo Si and
Huajun Chen},
editor = {Fr{\'{e}}d{\'{e}}rique Laforest and
Rapha{\"{e}}l Troncy and
Elena Simperl and
Deepak Agarwal and
Aristides Gionis and
Ivan Herman and
Lionel M{\'{e}}dini},
title = {KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization
for Relation Extraction},
booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France,
April 25 - 29, 2022},
pages = {2778--2788},
publisher = {{ACM}},
year = {2022},
url = {https://doi.org/10.1145/3485447.3511998},
doi = {10.1145/3485447.3511998},
timestamp = {Tue, 26 Apr 2022 16:02:09 +0200},
biburl = {https://dblp.org/rec/conf/www/ChenZXDYTHSC22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```