Hyperparameter	Value
\(n_{parameters}\)	1,331,810,304
\(n_{layers}\)	24
\(d_{model}\)	2,048
\(d_{ff}\)	8,192
\(n_{heads}\)	16
\(d_{head}\)	128
\(n_{ctx}\)	2,048
\(n_{vocab}\)	30,003 / 30,080
Positional Encoding	Rotary Position Embedding (RoPE)
RoPE Dimensions	64

The model consists of 24 transformer layers with a model dimension of 2048, and a feedforward dimension of 8192. The model dimension is split into 16 heads, each with a dimension of 128. Rotary Position Embedding (RoPE) is applied to 64 dimensions of each head. The model is trained with a tokenization vocabulary of 30003.

Training data

Polyglot-Ko-1.3B was trained on 863 GB of Korean language data (1.2TB before processing), a large-scale dataset curated by TUNiB. The data collection process has abided by South Korean laws. This dataset was collected for the purpose of training Polyglot-Ko models, so it will not be released for public use.

Source	Size (GB)	Link
Korean blog posts	682.3	-
Korean news dataset	87.0	-
Modu corpus	26.4	corpus.korean.go.kr
Korean patent dataset	19.0	-
Korean Q & A dataset	18.1	-
KcBert dataset	12.7	github.com/Beomi/KcBERT
Korean fiction dataset	6.1	-
Korean online comments	4.2	-
Korean wikipedia	1.4	ko.wikipedia.org
Clova call	< 1.0	github.com/clovaai/ClovaCall
Naver sentiment movie corpus	< 1.0	github.com/e9t/nsmc
Korean hate speech dataset	< 1.0	-
Open subtitles	< 1.0	opus.nlpl.eu/OpenSubtitles.php
AIHub various tasks datasets	< 1.0	aihub.or.kr
Standard Korean language dictionary	< 1.0	stdict.korean.go.kr/main/main.do

Furthermore, in order to avoid the model memorizing and generating personally identifiable information (PII) in the training data, we masked out the following sensitive information in the pre-processing stage:

<|acc|> : bank account number
<|rrn|> : resident registration number
<|tell|> : phone number

Training procedure

Polyglot-Ko-1.3B was trained on 213 billion tokens over 102,000 steps on 256 A100 GPUs with the GPT-NeoX framework. It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token.

How to use

This model can be easily loaded using the AutoModelForCausalLM class:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/polyglot-ko-1.3b")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/polyglot-ko-1.3b")

Evaluation results

We evaluate Polyglot-Ko-1.3B on KOBEST dataset, a benchmark with 5 downstream tasks, against comparable models such as skt/ko-gpt-trinity-1.2B-v0.5, kakaobrain/kogpt and facebook/xglm-7.5B, using the prompts provided in the paper.

The following tables show the results when the number of few-shot examples differ. You can reproduce these results using the polyglot branch of lm-evaluation-harness and the following scripts. For a fair comparison, all models were run under the same conditions and using the same prompts. In the tables, n refers to the number of few-shot examples.

In case of WiC dataset, all models show random performance.

console

python main.py \
   --model gpt2 \
   --model_args pretrained='EleutherAI/polyglot-ko-1.3b' \
   --tasks kobest_copa,kobest_hellaswag,kobest_boolq,kobest_sentineg,kobest_wic \
   --num_fewshot $YOUR_NUM_FEWSHOT \
   --batch_size $YOUR_BATCH_SIZE \
   --device $YOUR_DEVICE \
   --output_path $/path/to/output/

COPA (F1)

Model	params	0-shot	5-shot	10-shot	50-shot
skt/ko-gpt-trinity-1.2B-v0.5	1.2B	0.6696	0.6477	0.6419	0.6514
kakaobrain/kogpt	6.0B	0.7345	0.7287	0.7277	0.7479
facebook/xglm-7.5B	7.5B	0.6723	0.6731	0.6769	0.7119
EleutherAI/polyglot-ko-1.3b (this)	1.3B	0.7196	0.7193	0.7204	0.7206
EleutherAI/polyglot-ko-3.8b	3.8B	0.7595	0.7608	0.7638	0.7788
EleutherAI/polyglot-ko-5.8b	5.8B	0.7745	0.7676	0.7775	0.7887
EleutherAI/polyglot-ko-12.8b	12.8B	0.7937	0.8108	0.8037	0.8369

HellaSwag (F1)

Model	params	0-shot	5-shot	10-shot	50-shot
skt/ko-gpt-trinity-1.2B-v0.5	1.2B	0.5243	0.5272	0.5166	0.5352
kakaobrain/kogpt	6.0B	0.5590	0.5833	0.5828	0.5907
facebook/xglm-7.5B	7.5B	0.5665	0.5689	0.5565	0.5622
EleutherAI/polyglot-ko-1.3b (this)	1.3B	0.5247	0.5260	0.5278	0.5427
EleutherAI/polyglot-ko-3.8b	3.8B	0.5707	0.5830	0.5670	0.5787
EleutherAI/polyglot-ko-5.8b	5.8B	0.5976	0.5998	0.5979	0.6208
EleutherAI/polyglot-ko-12.8b	12.8B	0.5954	0.6306	0.6098	0.6118

BoolQ (F1)

Model	params	0-shot	5-shot	10-shot	50-shot
skt/ko-gpt-trinity-1.2B-v0.5	1.2B	0.3356	0.4014	0.3640	0.3560
kakaobrain/kogpt	6.0B	0.4514	0.5981	0.5499	0.5202
facebook/xglm-7.5B	7.5B	0.4464	0.3324	0.3324	0.3324
EleutherAI/polyglot-ko-1.3b (this)	1.3B	0.3552	0.4751	0.4109	0.4038
EleutherAI/polyglot-ko-3.8b	3.8B	0.4320	0.5263	0.4930	0.4038
EleutherAI/polyglot-ko-5.8b	5.8B	0.4356	0.5698	0.5187	0.5236
EleutherAI/polyglot-ko-12.8b	12.8B	0.4818	0.6041	0.6289	0.6448

SentiNeg (F1)

Model	params	0-shot	5-shot	10-shot	50-shot
skt/ko-gpt-trinity-1.2B-v0.5	1.2B	0.6065	0.6878	0.7280	0.8413
kakaobrain/kogpt	6.0B	0.3747	0.8942	0.9294	0.9698
facebook/xglm-7.5B	7.5B	0.3578	0.4471	0.3964	0.5271
EleutherAI/polyglot-ko-1.3b (this)	1.3B	0.6790	0.6257	0.5514	0.7851
EleutherAI/polyglot-ko-3.8b	3.8B	0.4858	0.7950	0.7320	0.7851
EleutherAI/polyglot-ko-5.8b	5.8B	0.3394	0.8841	0.8808	0.9521
EleutherAI/polyglot-ko-12.8b	12.8B	0.9117	0.9015	0.9345	0.9723

WiC (F1)

Model	params	0-shot	5-shot	10-shot	50-shot
skt/ko-gpt-trinity-1.2B-v0.5	1.2B	0.3290	0.4313	0.4001	0.3621
kakaobrain/kogpt	6.0B	0.3526	0.4775	0.4358	0.4061
facebook/xglm-7.5B	7.5B	0.3280	0.4903	0.4945	0.3656
EleutherAI/polyglot-ko-1.3b (this)	1.3B	0.3297	0.4850	0.465	0.3290
EleutherAI/polyglot-ko-3.8b	3.8B	0.3390	0.4944	0.4203	0.3835
EleutherAI/polyglot-ko-5.8b	5.8B	0.3913	0.4688	0.4189	0.3910
EleutherAI/polyglot-ko-12.8b	12.8B	0.3985	0.3683	0.3307	0.3273

Limitations and Biases

Polyglot-Ko has been trained to optimize next token prediction. Language models such as this are often used for a wide variety of tasks and it is important to be aware of possible unexpected outcomes. For instance, Polyglot-Ko will not always return the most factual or accurate response but the most statistically likely one. In addition, Polyglot may produce socially unacceptable or offensive content. We recommend having a human curator or other filtering mechanism to censor sensitive content.

Citation and Related Information

BibTeX entry

If you find our work useful, please consider citing:

bibtex

@misc{ko2023technical,
      title={A Technical Report for Polyglot-Ko: Open-Source Large-Scale Korean Language Models}, 
      author={Hyunwoong Ko and Kichang Yang and Minho Ryu and Taekyoon Choi and Seungmu Yang and jiwung Hyun and Sungho Park},
      year={2023},
      eprint={2306.02254},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Licensing

All our models are licensed under the terms of the Apache License 2.0.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

Acknowledgement

This project was made possible thanks to the computing resources from Stability.ai, and thanks to TUNiB for providing a large-scale Korean dataset for this work.