---
language:
- ko # Example: fr
license: apache-2.0 # Example: apache-2.0 or any license from https://hf.co/docs/hub/repositories-licenses
library_name: transformers # Optional. Example: keras or any library from https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Libraries.ts
tags:
- text2text-generation # Example: audio
datasets:
- aihub # Example: common_voice. Use dataset id from https://hf.co/datasets
metrics:
- bleu # Example: wer. Use metric id from https://hf.co/metrics
- rouge
# Optional. Add this if you want to encode your eval results in a structured way.
model-index:
- name: ko-TextNumbarT
results:
- task:
type: text2text-generation # Required. Example: automatic-speech-recognition
name: text2text-generation # Optional. Example: Speech Recognition
metrics:
- type: bleu # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.9529006548919251 # Required. Example: 20.90
name: eval_bleu # Optional. Example: Test WER
verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- type: rouge1 # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.9693520563208838 # Required. Example: 20.90
name: eval_rouge1 # Optional. Example: Test WER
verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- type: rouge2 # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.9444220599246154 # Required. Example: 20.90
name: eval_rouge2 # Optional. Example: Test WER
verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- type: rougeL # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.9692485601662657 # Required. Example: 20.90
name: eval_rougeL # Optional. Example: Test WER
verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
- type: rougeLsum # Required. Example: wer. Use metric id from https://hf.co/metrics
value: 0.9692422603343052 # Required. Example: 20.90
name: eval_rougeLsum # Optional. Example: Test WER
verified: true # Optional. If true, indicates that evaluation was generated by Hugging Face (vs. self-reported).
---
# ko-TextNumbarT(TNT Model๐งจ): Try Korean Reading To Number(ํ๊ธ์ ์ซ์๋ก ๋ฐ๊พธ๋ ๋ชจ๋ธ)
## Table of Contents
- [ko-TextNumbarT(TNT Model๐งจ): Try Korean Reading To Number(ํ๊ธ์ ์ซ์๋ก ๋ฐ๊พธ๋ ๋ชจ๋ธ)](#ko-textnumbarttnt-model-try-korean-reading-to-numberํ๊ธ์-์ซ์๋ก-๋ฐ๊พธ๋-๋ชจ๋ธ)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [Uses](#uses)
- [Evaluation](#evaluation)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
## Model Details
- **Model Description:**
๋ญ๊ฐ ์ฐพ์๋ด๋ ๋ชจ๋ธ์ด๋ ์๊ณ ๋ฆฌ์ฆ์ด ๋ฑํ ์์ด์ ๋ง๋ค์ด๋ณธ ๋ชจ๋ธ์
๋๋ค.
BartForConditionalGeneration Fine-Tuning Model For Korean To Number
BartForConditionalGeneration์ผ๋ก ํ์ธํ๋ํ, ํ๊ธ์ ์ซ์๋ก ๋ณํํ๋ Task ์
๋๋ค.
- Dataset use [Korea aihub](https://aihub.or.kr/aihubdata/data/list.do?currMenu=115&topMenu=100&srchDataRealmCode=REALM002&srchDataTy=DATA004)
I can't open my fine-tuning datasets for my private issue
๋ฐ์ดํฐ์
์ Korea aihub์์ ๋ฐ์์ ์ฌ์ฉํ์์ผ๋ฉฐ, ํ์ธํ๋์ ์ฌ์ฉ๋ ๋ชจ๋ ๋ฐ์ดํฐ๋ฅผ ์ฌ์ ์ ๊ณต๊ฐํด๋๋ฆด ์๋ ์์ต๋๋ค.
- Korea aihub data is ONLY permit to Korean!!!!!!!
aihub์์ ๋ฐ์ดํฐ๋ฅผ ๋ฐ์ผ์ค ๋ถ์ ํ๊ตญ์ธ์ผ ๊ฒ์ด๋ฏ๋ก, ํ๊ธ๋ก๋ง ์์ฑํฉ๋๋ค.
์ ํํ๋ ์ฒ ์์ ์ฌ๋ฅผ ์์ฑ์ ์ฌ๋ก ๋ฒ์ญํ๋ ํํ๋ก ํ์ต๋ ๋ชจ๋ธ์
๋๋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
์ฒ๋ง์ 1000๋ง ํน์ 10000000์ผ๋ก ์ธ ์๋ ์๊ธฐ์, Training Datasets์ ๋ฐ๋ผ ๊ฒฐ๊ณผ๋ ์์ดํ ์ ์์ต๋๋ค.
- **Developed by:** Yoo SungHyun(https://github.com/YooSungHyun)
- **Language(s):** Korean
- **License:** apache-2.0
- **Parent Model:** See the [kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) for more information about the pre-trained base model.
## Uses
Want see more detail follow this URL [KoGPT_num_converter](https://github.com/ddobokki/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](https://wandb.ai/bart_tadev/BartForConditionalGeneration/runs/1chrc03q?workspace=user-bart_tadev)
## How to Get Started With the Model
```python
from transformers.pipelines import Text2TextGenerationPipeline
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
texts = ["๊ทธ๋ฌ๊ฒ ๋๊ฐ ์ฌ์ฏ์๊น์ง ์ ์ ๋ง์๋?"]
tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path,
)
model = AutoModelForSeq2SeqLM.from_pretrained(
args.model_name_or_path,
)
seq2seqlm_pipeline = Text2TextGenerationPipeline(model=model, tokenizer=tokenizer)
kwargs = {
"min_length": args.min_length,
"max_length": args.max_length,
"num_beams": args.beam_width,
"do_sample": args.do_sample,
"num_beam_groups": args.num_beam_groups,
}
pred = seq2seqlm_pipeline(texts, **kwargs)
print(pred)
# ๊ทธ๋ฌ๊ฒ ๋๊ฐ 6์๊น์ง ์ ์ ๋ง์๋?
```