	Size(용량)	Average Score	NSMC (acc)	Naver NER (F1)	PAWS (acc)	KorNLI (acc)	KorSTS (spearman)	Question Pair (acc)	KorQuaD (Dev) (EM/F1)
KcELECTRA-base	475M	84.84	91.71	86.90	74.80	81.65	82.65	95.78	70.60 / 90.11
KcELECTRA-base-v2022	475M	85.20	91.97	87.35	76.50	82.12	83.67	95.12	69.00 / 90.40
KcBERT-Base	417M	79.65	89.62	84.34	66.95	74.85	75.57	93.93	60.25 / 84.39
KcBERT-Large	1.2G	81.33	90.68	85.53	70.15	76.99	77.49	94.06	62.16 / 86.64
KoBERT	351M	82.21	89.63	86.11	80.65	79.00	79.64	93.93	52.81 / 80.27
XLM-Roberta-Base	1.03G	84.01	89.49	86.26	82.95	79.92	79.09	93.53	64.70 / 88.94
HanBERT	614M	86.24	90.16	87.31	82.40	80.89	83.33	94.19	78.74 / 92.02
KoELECTRA-Base	423M	84.66	90.21	86.87	81.90	80.85	83.21	94.20	61.10 / 89.59
KoELECTRA-Base-v2	423M	86.96	89.70	87.02	83.90	80.61	84.30	94.72	84.34 / 92.58
DistilKoBERT	108M	76.76	88.41	84.13	62.55	70.55	73.21	92.48	54.12 / 77.80
ko-albert-base-v1	51M	80.46	86.83	82.26	69.95	74.17	74.48	94.06	76.08 / 86.82
ko-albert-large-v1	75M	82.39	86.91	83.12	76.10	76.01	77.46	94.33	77.64 / 87.99

*The size of HanBERT is the sum of the BERT model and the tokenizer DB.

*These results were obtained using the default configuration settings. Better performance may be achieved with additional hyperparameter tuning.

How to use

python

from transformers import AutoTokenizer, AutoModel

# Base Model (51M)
tokenizer = AutoTokenizer.from_pretrained("lots-o/ko-albert-base-v1")
model = AutoModel.from_pretrained("lots-o/ko-albert-base-v1")

# Large Model (75M)
tokenizer = AutoTokenizer.from_pretrained("lots-o/ko-albert-large-v1")
model = AutoModel.from_pretrained("lots-o/ko-albert-large-v1")

Acknowledgement

The GCP/TPU environment used for training the ALBERT Model was supported by the TRC program.

Reference

Paper

ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

lots-o/ko-albert-base-v1

Korean ALBERT

Dataset

Evaluation results

How to use

Acknowledgement

Reference

Paper

Github Repos

lots-o/
ko-albert-base-v1