Spico/Mirror

<div align="center"> <h1>🪞 Mirror: A Universal Framework for Various Information Extraction Tasks</h1> <img src="figs/mirror-frontpage.png" width="300" alt="Magic mirror"><br> <i>Image generated by DALLE 3</i><br> <!-- <img src="figs/mirror-framework.png" alt="Mirror Framework"> --> <a href="https://arxiv.org/abs/2311.05419" target="blank">[Paper]</a> | <a href="https://huggingface.co/spaces/Spico/Mirror" target="blank">[Demo]</a><br> 📃 Our paper has been accepted to EMNLP23 main conference, <a href="http://arxiv.org/abs/2311.05419" target="_blank">check it out</a>!<br> </div>

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😎: This is the official implementation of 🪞Mirror which supports almost all the Information Extraction tasks.

The name, Mirror, comes from the classical story Snow White and the Seven Dwarfs, where a magic mirror knows everything in the world. We aim to build such a powerful tool for the IE community.

🔥 Supported Tasks

1. 1.Named Entity Recognition
2. 2.Entity Relationship Extraction (Triplet Extraction)
3. 3.Event Extraction
4. 4.Aspect-based Sentiment Analysis
5. 5.Multi-span Extraction (e.g. Discontinuous NER)
6. 6.N-ary Extraction (e.g. Hyper Relation Extraction)
7. 7.Extractive Machine Reading Comprehension (MRC) and Question Answering
8. 8.Classification & Multi-choice MRC

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🌴 Dependencies

Python>=3.10

pip install -r requirements.txt

🚀 QuickStart

Pretrained Model Weights & Datasets

Download the pretrained model weights & datasets from [[OSF]](https://osf.io/kwsm4/?view_only=5b66734d88cf456b93f17b6bac8a44fb) .

No worries, it's an anonymous link just for double blind peer reviewing.

Pretraining

1. 1.Download and unzip the pretraining corpus into resources/Mirror/v1.4_sampled_v3/merged/all_excluded
2. 2.Start to run

CUDA_VISIBLE_DEVICES=0 rex train -m src.task -dc conf/Pretrain_excluded.yaml

Fine-tuning

⚠️ Due to data license constraints, some datasets are unavailable to provide directly (e.g. ACE04, ACE05).

1. 1.Download and unzip the pretraining corpus into resources/Mirror/v1.4_sampled_v3/merged/all_excluded
2. 2.Download and unzip the fine-tuning datasets into resources/Mirror/uie/
3. 3.Start to fine-tuning

# UIE tasks
CUDA_VISIBLE_DEVICES=0 bash scripts/single_task_wPTAllExcluded_wInstruction/run1.sh
CUDA_VISIBLE_DEVICES=1 bash scripts/single_task_wPTAllExcluded_wInstruction/run2.sh
CUDA_VISIBLE_DEVICES=2 bash scripts/single_task_wPTAllExcluded_wInstruction/run3.sh
CUDA_VISIBLE_DEVICES=3 bash scripts/single_task_wPTAllExcluded_wInstruction/run4.sh
# Multi-span and N-ary extraction
CUDA_VISIBLE_DEVICES=4 bash scripts/single_task_wPTAllExcluded_wInstruction/run_new_tasks.sh
# GLUE datasets
CUDA_VISIBLE_DEVICES=5 bash scripts/single_task_wPTAllExcluded_wInstruction/glue.sh

Analysis Experiments

• —Few-shot experiments : scripts/run_fewshot.sh. Collecting results: python mirror_fewshot_outputs/get_avg_results.py
• —Mirror w/ PT w/o Inst. : scripts/single_task_wPTAllExcluded_woInstruction
• —Mirror w/o PT w/ Inst. : scripts/single_task_wo_pretrain
• —Mirror w/o PT w/o Inst. : scripts/single_task_wo_pretrain_wo_instruction

Evaluation

1. 1.Change task_dir and data_pairs you want to evaluate. The default setting is to get results of Mirror_direct on all downstream tasks.
2. 2.CUDA_VISIBLE_DEVICES=0 python -m src.eval

Demo

1. 1.Download and unzip the pretrained task dump into mirror_outputs/Mirror_Pretrain_AllExcluded_2
2. 2.Try our demo:

CUDA_VISIBLE_DEVICES=0 python -m src.app.api_backend

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📋 Citation

@misc{zhu_mirror_2023,
  shorttitle = {Mirror},
  title = {Mirror: A Universal Framework for Various Information Extraction Tasks},
  author = {Zhu, Tong and Ren, Junfei and Yu, Zijian and Wu, Mengsong and Zhang, Guoliang and Qu, Xiaoye and Chen, Wenliang and Wang, Zhefeng and Huai, Baoxing and Zhang, Min},
  url = {http://arxiv.org/abs/2311.05419},
  doi = {10.48550/arXiv.2311.05419},
  urldate = {2023-11-10},
  publisher = {arXiv},
  month = nov,
  year = {2023},
  note = {arXiv:2311.05419 [cs]},
  keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

🛣️ Roadmap

• —[ ] Convert current model into Huggingface version, supporting loading from transformers like other newly released LLMs.
• —[ ] Remove Background area, merge TL, TP into a single T token
• —[ ] Add more task data: keyword extraction, coreference resolution, FrameNet, WikiNER, T-Rex relation extraction dataset, etc.
• —[ ] Pre-train on all the data (including benchmarks) to build a nice out-of-the-box toolkit for universal IE.

💌 Yours sincerely

This project is licensed under Apache-2.0. We hope you enjoy it ~

<hr> <div align="center"> <p>Mirror Team w/ 💖</p> </div>