TUNiB-Electra

We release several new versions of the ELECTRA model, which we name TUNiB-Electra. There are two motivations. First, all the existing pre-trained Korean encoder models are monolingual, that is, they have knowledge about Korean only. Our bilingual models are based on the balanced corpora of Korean and English. Second, we want new off-the-shelf models trained on much more texts. To this end, we collected a large amount of Korean text from various sources such as blog posts, comments, news, web novels, etc., which sum up to 100 GB in total.

How to use

You can use this model directly with transformers library:

from transformers import AutoModel, AutoTokenizer

# Small Model (Korean-English bilingual model)
tokenizer = AutoTokenizer.from_pretrained('tunib/electra-ko-en-small')
model = AutoModel.from_pretrained('tunib/electra-ko-en-small')

Tokenizer example

>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained('tunib/electra-ko-en-small')
>>> tokenizer.tokenize("tunib is a natural language processing tech startup.")
['tun', '##ib', 'is', 'a', 'natural', 'language', 'processing', 'tech', 'startup', '.']
>>> tokenizer.tokenize("튜닙은 자연어처리 테크 스타트업입니다.")
['튜', '##닙', '##은', '자연', '##어', '##처리', '테크', '스타트업', '##입니다', '.']

Results on Korean downstream tasks

	# Params	Avg.	NSMC (acc)	Naver NER (F1)	PAWS (acc)	KorNLI (acc)	KorSTS (spearman)	Question Pair (acc)	KorQuaD (Dev) (EM/F1)	Korean-Hate-Speech (Dev) (F1)
TUNiB-Electra-ko-small	14M	81.29	89.56	84.98	72.85	77.08	78.76	94.98	61.17 / 87.64	64.50
TUNiB-Electra-ko-en-small	18M	81.44	89.28	85.15	75.75	77.06	77.61	93.79	80.55 / 89.77	63.13
KoELECTRA-small-v3	14M	82.58	89.36	85.40	77.45	78.60	80.79	94.85	82.11 / 91.13	63.07

Results on English downstream tasks

	# Params	Avg.	CoLA (MCC)	SST (Acc)	MRPC (Acc)	STS (Spearman)	QQP (Acc)	MNLI (Acc)	QNLI (Acc)	RTE (Acc)
TUNiB-Electra-ko-en-small	18M	80.44	56.76	88.76	88.73	86.12	88.66	79.03	87.26	68.23
ELECTRA-small	13M	79.71	55.6	91.1	84.9	84.6	88.0	81.6	88.3	63.6
BERT-small	13M	74.06	27.8	89.7	83.4	78.8	87.0	77.6	86.4	61.8