Edit model card

ko-barTNumText(TNT Model๐Ÿงจ): Try Number To Korean Reading(์ˆซ์ž๋ฅผ ํ•œ๊ธ€๋กœ ๋ฐ”๊พธ๋Š” ๋ชจ๋ธ)

Table of Contents

Model Details

  • Model Description: ๋ญ”๊ฐ€ ์ฐพ์•„๋ด๋„ ๋ชจ๋ธ์ด๋‚˜ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ๋”ฑํžˆ ์—†์–ด์„œ ๋งŒ๋“ค์–ด๋ณธ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค.
    BartForConditionalGeneration Fine-Tuning Model For Number To Korean
    BartForConditionalGeneration์œผ๋กœ ํŒŒ์ธํŠœ๋‹ํ•œ, ์ˆซ์ž๋ฅผ ํ•œ๊ธ€๋กœ ๋ณ€ํ™˜ํ•˜๋Š” Task ์ž…๋‹ˆ๋‹ค.

  • Dataset use Korea aihub
    I can't open my fine-tuning datasets for my private issue
    ๋ฐ์ดํ„ฐ์…‹์€ Korea aihub์—์„œ ๋ฐ›์•„์„œ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ํŒŒ์ธํŠœ๋‹์— ์‚ฌ์šฉ๋œ ๋ชจ๋“  ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์ •์ƒ ๊ณต๊ฐœํ•ด๋“œ๋ฆด ์ˆ˜๋Š” ์—†์Šต๋‹ˆ๋‹ค.

  • Korea aihub data is ONLY permit to Korean!!!!!!!
    aihub์—์„œ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ›์œผ์‹ค ๋ถ„์€ ํ•œ๊ตญ์ธ์ผ ๊ฒƒ์ด๋ฏ€๋กœ, ํ•œ๊ธ€๋กœ๋งŒ ์ž‘์„ฑํ•ฉ๋‹ˆ๋‹ค.
    ์ •ํ™•ํžˆ๋Š” ์Œ์„ฑ์ „์‚ฌ๋ฅผ ์ฒ ์ž์ „์‚ฌ๋กœ ๋ฒˆ์—ญํ•˜๋Š” ํ˜•ํƒœ๋กœ ํ•™์Šต๋œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. (ETRI ์ „์‚ฌ๊ธฐ์ค€)

  • In case, ten million, some people use 10 million or some people use 10000000, so this model is crucial for training datasets
    ์ฒœ๋งŒ์„ 1000๋งŒ ํ˜น์€ 10000000์œผ๋กœ ์“ธ ์ˆ˜๋„ ์žˆ๊ธฐ์—, Training Datasets์— ๋”ฐ๋ผ ๊ฒฐ๊ณผ๋Š” ์ƒ์ดํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

  • ์ˆ˜๊ด€ํ˜•์‚ฌ์™€ ์ˆ˜ ์˜์กด๋ช…์‚ฌ์˜ ๋„์–ด์“ฐ๊ธฐ์— ๋”ฐ๋ผ ๊ฒฐ๊ณผ๊ฐ€ ํ™•์—ฐํžˆ ๋‹ฌ๋ผ์งˆ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. (์‰ฐ์‚ด, ์‰ฐ ์‚ด -> ์‰ฐ์‚ด, 50์‚ด) https://eretz2.tistory.com/34
    ์ผ๋‹จ์€ ๊ธฐ์ค€์„ ์žก๊ณ  ์น˜์šฐ์น˜๊ฒŒ ํ•™์Šต์‹œํ‚ค๊ธฐ์—” ์–ด๋–ป๊ฒŒ ์‚ฌ์šฉ๋ ์ง€ ๋ชฐ๋ผ, ํ•™์Šต ๋ฐ์ดํ„ฐ ๋ถ„ํฌ์— ๋งก๊ธฐ๋„๋ก ํ–ˆ์Šต๋‹ˆ๋‹ค. (์‰ฐ ์‚ด์ด ๋” ๋งŽ์„๊นŒ ์‰ฐ์‚ด์ด ๋” ๋งŽ์„๊นŒ!?)

  • Developed by: Yoo SungHyun(https://github.com/YooSungHyun)

  • Language(s): Korean

  • License: apache-2.0

  • Parent Model: See the kobart-base-v2 for more information about the pre-trained base model.

Uses

Want see more detail follow this URL KoGPT_num_converter
and see bart_inference.py and bart_train.py

Evaluation

Just using evaluate-metric/bleu and evaluate-metric/rouge in huggingface evaluate library
Training wanDB URL

How to Get Started With the Model

from transformers.pipelines import Text2TextGenerationPipeline
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
texts = ["๊ทธ๋Ÿฌ๊ฒŒ ๋ˆ„๊ฐ€ 6์‹œ๊นŒ์ง€ ์ˆ ์„ ๋งˆ์‹œ๋ž˜?"]
tokenizer = AutoTokenizer.from_pretrained("lIlBrother/ko-barTNumText")
model = AutoModelForSeq2SeqLM.from_pretrained("lIlBrother/ko-barTNumText")
seq2seqlm_pipeline = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
kwargs = {
    "min_length": 0,
    "max_length": 1206,
    "num_beams": 100,
    "do_sample": False,
    "num_beam_groups": 1,
}
pred = seq2seqlm_pipeline(texts, **kwargs)
print(pred)
# ๊ทธ๋Ÿฌ๊ฒŒ ๋ˆ„๊ฐ€ ์—ฌ์„ฏ ์‹œ๊นŒ์ง€ ์ˆ ์„ ๋งˆ์‹œ๋ž˜?
Downloads last month
14
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Evaluation results