add: metaretriever

metaretriever/.gitignore

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+# UIE
+/data
+/pretrain_data
+/hf_models
+/pd_models
+/runs
+/models
+*.lock
+
+# mac
+.DS_Store
+
+# env
+/.vscode
+/.idea
+**/__pycache__
+*.pyc
+.pytest_cache

metaretriever/README.md

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+# Universal Information Extraction with Meta-Pretrained Self-Retrieval
+
+This code is for ACL 2023 Findings paper "Universal Information Extraction with Meta-Pretrained Self-Retrieval".
+
+## Overview
+
+![](img/MetaRetriever.png)
+
+Universal Information Extraction (Universal IE) aims to solve different extraction tasks in a uniform text-to-structure generation manner. Such a generation procedure tends to struggle when there exist complex information structures to be extracted. Retrieving knowledge from external knowledge bases may help models to overcome this problem but it is impossible to construct a knowledge base suitable for various IE tasks. Inspired by the fact that large amount of knowledge are stored in the pretrained language models (PLM) and can be retrieved explicitly, in this paper, we propose MetaRetriever to retrieve task-specific knowledge from PLMs to enhance universal IE. As different IE tasks need different knowledge, we further propose a Meta-Pretraining Algorithm which allows MetaRetriever to quicktly achieve maximum task-specific retrieval performance when fine-tuning on downstream IE tasks. Experimental results show that MetaRetriever achieves the new state-of-the-art on 4 IE tasks, 12 datasets under fully-supervised, low-resource and few-shot scenarios.
+
+## Requirements
+
+General
+
+- Python (verified on 3.8)
+- CUDA (verified on 10.2)
+
+Python Packages
+``` bash
+conda create -n metaretriever python=3.8
+conda install -y pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
+pip install -r requirements.txt
+```
+
+**NOTE**: Different versions of packages (such as `pytorch`, `transformers`, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.
+
+## Usage
+
+### Data Preprocess
+
+``` bash
+cd ./dataset_processing/ours
+bash download_and_preprocess_data_clean.sh > clean_log.txt
+```
+
+### Model Preparation
+
+Please refer to [UIE](https://github.com/universal-ie/UIE) to download UIE model checkpoint and put it under the `models` dir.
+
+### Meta-Pretraining
+
+``` bash
+
+bash run_seq2seq_pretrain.bash -v -d 0,1,2,3,4,5,6,7 -b 64 -k 1 --lr 1e-4 --warmup_ratio 0.06 -i relation/ours_clean --spot_noise 0.0 --asoc_noise 0.0 -f spotasoc --map_config config/offset_map/closest_offset_en.yaml -m ./models/uie-base-en --random_prompt --epoch 4 --trainer_type meta_pretrain_v2 --use_prompt_tuning_model False --output_dir output/meta-pretrained-model
+```
+
+### Meta-Finetuning
+
+1. Full Supervision Scenario
+``` bash
+. config/exp_conf/large_model_conf.ini  && trainer_type=meta_finetune_v2 model_name=meta-pretrained-model dataset_name=relation/conll04 selected_gpus=0,1,2,3,4,5,6,7 BATCH_SIZE=4 use_prompt_tuning_model=False run_time=1 bash scripts_exp/run_exp.bash
+```
+
+2. Few-Shot Scenario
+``` bash
+. config/exp_conf/base_model_conf_sa_shot.ini && trainer_type=meta_finetune_v2 model_name=meta-pretrained-model dataset_name=relation/conll04 selected_gpus=0,1,2,3,4,5,6,7 BATCH_SIZE=16 use_prompt_tuning_model=False bash scripts_exp/run_exp_shot.bash
+```
+
+3. Low-Resource Scenario
+``` bash
+. config/exp_conf/base_model_conf_sa_ratio.ini && trainer_type=meta_finetune_v2 model_name=meta-pretrained-model dataset_name=relation/conll04 selected_gpus=0,1,2,3,4,5,6,7 BATCH_SIZE=16 use_prompt_tuning_model=False bash scripts_exp/run_exp_ratio.bash
+```
+
+## Citation
+
+If this repository helps you, please cite this paper:
+```
+@inproceedings{cong-etal-2023-universal,
+    title = "Universal Information Extraction with Meta-Pretrained Self-Retrieval",
+    author = "Cong, Xin  and
+      Yu, Bowen  and
+      Fang, Mengcheng  and
+      Liu, Tingwen  and
+      Yu, Haiyang  and
+      Hu, Zhongkai  and
+      Huang, Fei  and
+      Li, Yongbin  and
+      Wang, Bin",
+    editor = "Rogers, Anna  and
+      Boyd-Graber, Jordan  and
+      Okazaki, Naoaki",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
+    month = jul,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.findings-acl.251",
+    doi = "10.18653/v1/2023.findings-acl.251",
+}
+```

metaretriever/config/data_conf/base_model_conf_absa.ini

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+
+export k8s_gpu_cards=1
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=5
+export max_source_length=384
+
+export BATCH_SIZE="16"
+export LR_RATE="1e-4 3e-4 5e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'

metaretriever/config/data_conf/base_model_conf_nyt.ini

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+export job_name=FT_Multi
+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export BATCH_SIZE="16"
+export LR_RATE="3e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags=""
+export job_remark="3e-4,0.1"
+export eval_match_mode="set"

metaretriever/config/data_conf/base_scierc_conf.ini

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+
+export k8s_gpu_cards=1
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=5
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="16"
+export LR_RATE="5e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="5e-4,0.1"
+export start_eval_step=3000

metaretriever/config/data_conf/large_ace04ent_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1 0.2"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="1e-4,0.1,0.2"

metaretriever/config/data_conf/large_ace05ent_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1 0.2"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="1e-4,0.1,0.2"

metaretriever/config/data_conf/large_ace05evt_conf.ini

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+export job_name=FT_spotasocname
+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=2000
+export epoch=50
+export run_time=3
+export max_source_length=256
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'
+export start_eval_step=15000
+export job_remark="1e-4,0.1"

metaretriever/config/data_conf/large_ace05rel_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4 3e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.2"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="1e-4,3e-4,0.2"

metaretriever/config/data_conf/large_casie_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=256
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="3e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.2"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="3e-4,0.2"

metaretriever/config/data_conf/large_conll03_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=256
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/first_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="1e-4,0.1"

metaretriever/config/data_conf/large_conll03_conf_b8.ini

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+
+export k8s_gpu_cards=1
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=256
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="5e-5"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/first_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="5e-5,0.1"

metaretriever/config/data_conf/large_conll04_conf.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="3e-4"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.2 0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="3e-4,0.1,0.2"

metaretriever/config/data_conf/large_model_conf_absa.ini

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+
+export k8s_gpu_cards=1
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export BATCH_SIZE="8"
+export LR_RATE="1e-4 3e-5 5e-5"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.1"
+export map_config='config/offset_map/closest_offset_en.yaml'

metaretriever/config/data_conf/large_model_conf_nyt.ini

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+
+export k8s_gpu_cards=4
+export gpu_node=${k8s_gpu_cards}
+
+export eval_steps=0
+export epoch=50
+export run_time=3
+export max_source_length=384
+
+export job_tags="${dataset_name},${model_name}_rp"
+export job_remark="d${dataset_name},m${model_name}"
+
+export BATCH_SIZE="8"
+export LR_RATE="5e-5"
+export WARMUP_PROP="0.06"
+export LABEL_SMOOTHING="0"
+export NEGATIVE="-1"
+export NOISE="0.2"
+export map_config='config/offset_map/closest_offset_en.yaml'
+
+export job_tags="${dataset_name},${model_name}"
+export job_remark="5e-5,0.2"
+export eval_match_mode="set"