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ERNIE (sub-project of OpenSKL)

ERNIE is a sub-project of OpenSKL, providing an open-sourced toolkit (Enhanced language RepresentatioN with Informative Entities) for augmenting pre-trained language models with knowledge graph representations.

Overview

ERNIE contains the source code and dataset for "ERNIE: Enhanced Language Representation with Informative Entities", and is an effective and efficient toolkit for augmenting pre-trained language models with knowledge graph representations.

Models

We provide our knowledge-enhanced pre-trained language model ERNIE in this toolkit. We also provide the detailed commands to fine-tune ERNIE for different downstream tasks.

Evaluation

We validate the effectiveness of ERNIE on entity typing and relation classification tasks through fine-tuning.

Settings

We use the following datasets: FIGER and OpenEntity for entity typing, FewRel and TACRED for relation classification. We will fine-tune the models (BERT and ERNIE) first, and then evaluate their accuracies and F1 scores.

Results

Here we report the main results on the above datasets. From this table, we observe that ERNIE effectively improves the performance of BERT on these knowledge-driven tasks.

FIGEROpenEntityFewRelTACRED
Acc.F1F1F1
BERT52.0473.5684.8966.00
ERNIE57.1975.5688.3267.97

Usage

Requirements:

Prepare Pre-train Data

Run the following command to create training instances.

  # Download Wikidump
  wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2
  # Download anchor2id
  wget -c https://cloud.tsinghua.edu.cn/f/6318808dded94818b3a1/?dl=1 -O anchor2id.txt
  # WikiExtractor
  python3 pretrain_data/WikiExtractor.py enwiki-latest-pages-articles.xml.bz2 -o pretrain_data/output -l --min_text_length 100 --filter_disambig_pages -it abbr,b,big --processes 4
  # Modify anchors with 4 processes
  python3 pretrain_data/extract.py 4
  # Preprocess with 4 processes
  python3 pretrain_data/create_ids.py 4
  # create instances
  python3 pretrain_data/create_insts.py 4
  # merge
  python3 code/merge.py

If you want to get anchor2id by yourself, run the following code(this will take about half a day) after python3 pretrain_data/extract.py 4

  # extract anchors
  python3 pretrain_data/utils.py get_anchors
  # query Mediawiki api using anchor link to get wikibase item id. For more details, see https://en.wikipedia.org/w/api.php?action=help.
  python3 pretrain_data/create_anchors.py 256 
  # aggregate anchors 
  python3 pretrain_data/utils.py agg_anchors

Run the following command to pretrain:

  python3 code/run_pretrain.py --do_train --data_dir pretrain_data/merge --bert_model bert_base --output_dir pretrain_out/ --task_name pretrain --fp16 --max_seq_length 256

We use 8 NVIDIA-2080Ti to pre-train our model and there are 32 instances in each GPU. It takes nearly one day to finish the training (1 epoch is enough).

Pre-trained Model

Download pre-trained knowledge embedding from Google Drive/Tsinghua Cloud and extract it.

tar -xvzf kg_embed.tar.gz

Download pre-trained ERNIE from Google Drive/Tsinghua Cloud and extract it.

tar -xvzf ernie_base.tar.gz

Note that the extraction may be not completed in Windows.

Fine-tuning

As most datasets except FewRel don't have entity annotations, we use TAGME to extract the entity mentions in the sentences and link them to their corresponding entitoes in KGs. We provide the annotated datasets Google Drive/Tsinghua Cloud.

tar -xvzf data.tar.gz

In the root directory of the project, run the following codes to fine-tune ERNIE on different datasets.

FewRel:

python3 code/run_fewrel.py   --do_train   --do_lower_case   --data_dir data/fewrel/   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 32   --learning_rate 2e-5   --num_train_epochs 10   --output_dir output_fewrel   --fp16   --loss_scale 128
# evaluate
python3 code/eval_fewrel.py   --do_eval   --do_lower_case   --data_dir data/fewrel/   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 32   --learning_rate 2e-5   --num_train_epochs 10   --output_dir output_fewrel   --fp16   --loss_scale 128

TACRED:

python3 code/run_tacred.py   --do_train   --do_lower_case   --data_dir data/tacred   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 32   --learning_rate 2e-5   --num_train_epochs 4.0   --output_dir output_tacred   --fp16   --loss_scale 128 --threshold 0.4
# evaluate
python3 code/eval_tacred.py   --do_eval   --do_lower_case   --data_dir data/tacred   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 32   --learning_rate 2e-5   --num_train_epochs 4.0   --output_dir output_tacred   --fp16   --loss_scale 128 --threshold 0.4

FIGER:

python3 code/run_typing.py    --do_train   --do_lower_case   --data_dir data/FIGER   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 2048   --learning_rate 2e-5   --num_train_epochs 3.0   --output_dir output_figer  --gradient_accumulation_steps 32 --threshold 0.3 --fp16 --loss_scale 128 --warmup_proportion 0.2
# evaluate
python3 code/eval_figer.py    --do_eval   --do_lower_case   --data_dir data/FIGER   --ernie_model ernie_base   --max_seq_length 256   --train_batch_size 2048   --learning_rate 2e-5   --num_train_epochs 3.0   --output_dir output_figer  --gradient_accumulation_steps 32 --threshold 0.3 --fp16 --loss_scale 128 --warmup_proportion 0.2

OpenEntity:

python3 code/run_typing.py    --do_train   --do_lower_case   --data_dir data/OpenEntity   --ernie_model ernie_base   --max_seq_length 128   --train_batch_size 16   --learning_rate 2e-5   --num_train_epochs 10.0   --output_dir output_open --threshold 0.3 --fp16 --loss_scale 128
# evaluate
python3 code/eval_typing.py   --do_eval   --do_lower_case   --data_dir data/OpenEntity   --ernie_model ernie_base   --max_seq_length 128   --train_batch_size 16   --learning_rate 2e-5   --num_train_epochs 10.0   --output_dir output_open --threshold 0.3 --fp16 --loss_scale 128

Some code is modified from the pytorch-pretrained-BERT. You can find the explanation of most parameters in pytorch-pretrained-BERT.

As the annotations given by TAGME have confidence score, we use --threshlod to set the lowest confidence score and choose the annotations whose scores are higher than --threshold. In this experiment, the value is usually 0.3 or 0.4.

The script for the evaluation of relation classification just gives the accuracy score. For the macro/micro metrics, you should use code/score.py which is from tacred repo.

python3 code/score.py gold_file pred_file

You can find gold_file and pred_file on each checkpoint in the output folder (--output_dir).

New Tasks

If you want to use ERNIE in new tasks, you should follow these steps:

Here is a quick-start example (code/example.py) using ERNIE for Masked Language Model. We show how to annotate the given sentence with TAGME and build the input data for ERNIE. Note that it will take some time (around 5 mins) to load the model.

# If you haven't installed tagme
pip install tagme
# Run example
python3 code/example.py

Citation

If you use the code, please cite this paper:

@inproceedings{zhang2019ernie,
  title={{ERNIE}: Enhanced Language Representation with Informative Entities},
  author={Zhang, Zhengyan and Han, Xu and Liu, Zhiyuan and Jiang, Xin and Sun, Maosong and Liu, Qun},
  booktitle={Proceedings of ACL 2019},
  year={2019}
}

About OpenSKL

OpenSKL project aims to harness the power of both structured knowledge and natural languages via representation learning. All sub-projects of OpenSKL, under the categories of Algorithm, Resource and Application, are as follows.