KorSQuAD-pl

Korean(한국어)

KorSQuAD-pl provides code that enables transfer learning experiments on KorQuAD and SQuAD, which are Korean and English Question Answering task datasets.

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KorSQuAD-pl has the following features.

• KorSQuAD-pl use pre-trained language models distributed in Huggingface Transformers.
• KorSQuAD-pl is implemented through PyTorch Lightning's code style.

Dependencies

• torch>=1.9.0
• pytorch-lihgtning==1.3.8
• transformers>=4.8.0
• kobert-transformers==0.5.1
• sentencepiece>=0.1.96
• scikit-learn
• numpy

Usage

1. Download Dataset

The datasets supported by KorSQuAD-pl are as follows.

Dataset	Link
KorQuAD 1.0	LINK
KorQuAD 2.0 (Preparing)	LINK
SQuAD 1.1	LINK
SQuAD 2.0	LINK

• In the case of KorQuAD dataset, if you run the following command, the dataset is automatically downloaded and saved in the ./data path. (Currently, code for downloading KorQuAD 2.0 dataset is not yet complete. I will update it as soon as possible.)

  python download_korquad.py --download_dir ./data
• In the case of SQuAD dataset, if you run the following command, the dataset is automatically downloaded and saved in the ./data path.

  python download_squad.py --download_dir ./data

2. Training and Evaluation

Transfer learning is performed and evaluating through the following command.

• --model_type : type of model, e.g., bert
• --model_name_or_path : name or path of the model, e.g., bert-base-uncased
• --data_name : dataset name to use training and evaluating, e.g., korquad_v1.0, korquad_v2.0, squad_v1.1, squad_v2.0
• --do_train : training run
• --do_eval : evaluating run
• --gpu_ids : GPU ids to be used when performing transfer learning, e.g., 0 mean using GPU 0, 0,3 mean using GPU 0 and 3
• --max_seq_length : the maximum total input sequence length after WordPiece tokenization
• --num_train_epochs : number of epochs in training
• --batch_size : batch size at training
• --learning_rate : optimizer for learning rate.
• --adam_epsilon : huggingface AdamW optimizer's epsilon value

python3 run_qa.py --model_type bert \
                  --model_name_or_path bert-base-uncased \
                  --data_name squad_v2.0 \
                  --do_train \
                  --do_eval \
                  --gpu_ids 0 \
                  --max_seq_length 384 \
                  --num_train_epochs 2 \
                  --batch_size 16 \
                  --learning_rate 3e-5 \
                  --adam_epsilon 1e-8

3. Distributed Training and Evaluation

If you want to perform distributed training, use the following command.

python3 run_qa.py --model_type bert \
                  --model_name_or_path bert-large-uncased-whole-word-masking \
                  --data_name squad_v2.0 \
                  --do_train \
                  --gpu_ids 0,1,2,3 \
                  --max_seq_length 384 \
                  --num_train_epochs 2 \
                  --batch_size 4 \
                  --learning_rate 3e-5 \
                  --adam_epsilon 1e-8

When performing distributed training, crash take place when evaluating. So, you need to command to change single GPU as follows. In other words, if you use a multi GPU, training and evaluating cannot be performed at the same time.

python3 run_qa.py --model_type bert \
                  --model_name_or_path bert-large-uncased-whole-word-masking \
                  --data_name squad_v2.0 \
                  --do_eval \
                  --gpu_ids 0 \
                  --max_seq_length 384 \
                  --num_train_epochs 2 \
                  --batch_size 4 \
                  --learning_rate 3e-5 \
                  --adam_epsilon 1e-8

4. Tensorboard with.PyTorch Lightning

All checkpoint and tensorboard log files are stored in the ./model folder.

Therefore, you can use tensorboard by specifying --logdir as follows.

tensorboard --logdir ./model/squad_v2.0/bert-base-uncased/

Experiment Settings

Hyper parameters and GPU settings for experiments are as follows:

• For models of small and base size, experiments are performed using a single GPU.
• For models of large size, experiments are performed through distributed training. In other words, this cases are performed at Multi GPU environment.(Specifically, 4 GPUs of 16GB were used.)

1. KorQuAD

Hyper Parameter	Value
null_score_diff_threshold	0.0
max_seq_length	512
doc_stride	128
max_query_length	64
n_best_size	20
max_answer_length	30
batch_size	16(small size, base size), 4(large size)
num_train_epochs	3
weight_decay	0.01
adam_epsilon	1e-6(KoELECTRA), 1e-8(others)
learning_rate	5e-5

2. SQuAD

Hyper Parameter	Value
null_score_diff_threshold	0.0
max_seq_length	384
doc_stride	128
max_query_length	64
n_best_size	20
max_answer_length	30
batch_size	16(small size, base size), 4(large size)
num_train_epochs	3
weight_decay	0.01
adam_epsilon	1e-6(ALBERT, RoBERTa, ELECTRA), 1e-8(others)
learning_rate	3e-5

Result of Experiments

1. KorQuAD 1.0

Model Type	model_name_or_path	Model Size	Exact Match (%)	F1 Score (%)
BERT	bert-base-multilingual-cased	Base	66.92	87.18
KoBERT	monologg/kobert	Base	47.73	75.12
DistilBERT	distilbert-base-multilingual-cased	Small	62.91	83.28
DistilKoBERT	monologg/distilkobert	Small	54.78	78.85
KoELECTRA	monologg/koelectra-small-v2-discriminator	Small	81.45	90.09
	monologg/koelectra-base-v2-discriminator	Base	83.94	92.20
	monologg/koelectra-small-v3-discriminator	Small	81.13	90.70
	monologg/koelectra-base-v3-discriminator	Base	83.92	92.92

2. KorQuAD 2.0 (Preparing)

Model Type	model_name_or_path	Model Size	Exact Match (%)	F1 Score (%)
BERT	bert-base-multilingual-cased	Base
KoBERT	monologg/kobert	Base
DistilBERT	distilbert-base-multilingual-cased	Small
DistilKoBERT	monologg/distilkobert	Small
KoELECTRA	monologg/koelectra-small-v2-discriminator	Small
	monologg/koelectra-base-v2-discriminator	Base
	monologg/koelectra-small-v3-discriminator	Small
	monologg/koelectra-base-v3-discriminator	Base

3. SQuAD 1.1

Model Type	model_name_or_path	Model Size	Exact Match (%)	F1 Score (%)
BERT	bert-base-cased	Base	80.38	87.99
	bert-base-uncased	Base	80.03	87.52
	bert-large-uncased-whole-word-masking	Large	85.51	91.88
DistilBERT	distilbert-base-cased	Small	75.94	84.30
	distilbert-base-uncased	Small	76.72	84.78
ALBERT	albert-base-v1	Base	79.46	87.70
	albert-base-v2	Base	79.25	87.34
RoBERTa	roberta-base	Base	83.04	90.48
	roberta-large	Large	85.18	92.25
ELECTRA	google/electra-small-discriminator	Small	77.11	85.41
	google/electra-base-discriminator	Base	84.70	91.30
	google/electra-large-discriminator	Large	87.14	93.41

4. SQuAD 2.0

Model Type	model_name_or_path	Model Size	Exact Match (%)	F1 Score (%)
BERT	bert-base-cased	Base	70.52	73.79
	bert-base-uncased	Base	72.02	75.35
	bert-large-uncased-whole-word-masking	Large	78.97	82.14
DistilBERT	distilbert-base-cased	Small	63.89	66.97
	distilbert-base-uncased	Small	65.40	68.03
ALBERT	albert-base-v1	Base	74.75	77.77
	albert-base-v2	Base	76.48	79.92
RoBERTa	roberta-base	Base	78.91	82.20
	roberta-large	Large	80.83	84.29
ELECTRA	google/electra-small-discriminator	Small	70.55	73.64
	google/electra-base-discriminator	Base	78.70	82.17

TODO list

• add KorQuAD 2.0

References

• KoBERT
• Huggingface Transformers
• KorQuAD
• KorQuAD by graykode
• KorQuAD by lyeoni
• KoBert shows low performance on KorQuad
• KoBERT-KorQuAD by monologg
• SQuAD
• NVIDIA NeMo
• KoELECTRA by monologg

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If you have any additional questions, please register an issue in this repository or contact [email protected].