

# Model Card for kobart-base-v2 

 
# Model Details
 
## Model Description
 
[**BART**](https://arxiv.org/pdf/1910.13461.pdf)(**B**idirectional and **A**uto-**R**egressive **T**ransformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 `autoencoder`의 형태로 학습이 됩니다. 한국어 BART(이하 **KoBART**) 는 논문에서 사용된 `Text Infilling` 노이즈 함수를 사용하여 **40GB** 이상의 한국어 텍스트에 대해서 학습한 한국어 `encoder-decoder` 언어 모델입니다. 이를 통해 도출된 `KoBART-base`를 배포합니다.

- **Developed by:** More information needed
- **Shared by [Optional]:** Heewon(Haven) Jeon
- **Model type:** Feature Extraction 
- **Language(s) (NLP):** Korean
- **License:** MIT
- **Parent Model:** BART
- **Resources for more information:**
  - [GitHub Repo](https://github.com/haven-jeon/KoBART)
   - [Model Demo Space](https://huggingface.co/spaces/gogamza/kobart-summarization)
 	


# Uses
 

## Direct Use
This model can be used for the task of Feature Extraction.
 
## Downstream Use [Optional]
 
More information needed.
 
## Out-of-Scope Use
 
The model should not be used to intentionally create hostile or alienating environments for people. 
 
# Bias, Risks, and Limitations
 
 
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.



## Recommendations
 
 
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

# Training Details
 
## Training Data
 
| Data  | # of Sentences |
|-------|---------------:|
| Korean Wiki |     5M   |  
| Other corpus |  0.27B    | 
 
한국어 위키 백과 이외, 뉴스, 책, [모두의 말뭉치 v1.0(대화, 뉴스, ...)](https://corpus.korean.go.kr/), [청와대 국민청원](https://github.com/akngs/petitions) 등의 다양한 데이터가 모델 학습에 사용되었습니다.
 
`vocab` 사이즈는 30,000 이며 대화에 자주 쓰이는 아래와 같은 이모티콘, 이모지 등을 추가하여 해당 토큰의 인식 능력을 올렸습니다. 
> 😀, 😁, 😆, 😅, 🤣, .. , `:-)`, `:)`, `-)`, `(-:`...
 
## Training Procedure

 
### Tokenizer
 
[`tokenizers`](https://github.com/huggingface/tokenizers) 패키지의 `Character BPE tokenizer`로 학습되었습니다. 
 
 


 
### Speeds, Sizes, Times
| Model       |  # of params |   Type   | # of layers  | # of heads | ffn_dim | hidden_dims | 
|--------------|:----:|:-------:|--------:|--------:|--------:|--------------:|
| `KoBART-base` |  124M  |  Encoder |   6     | 16      | 3072    | 768 | 
|               |        | Decoder |   6     | 16      | 3072    | 768 |

 
# Evaluation
 
 
## Testing Data, Factors & Metrics
 
### Testing Data
 
More information needed 
 
 
### Factors
More information needed
 
### Metrics
 
More information needed
 
 
## Results 
 
NSMC
- acc. : 0.901

The model authors also note in the [GitHub Repo](https://github.com/haven-jeon/KoBART):

|   |  [NSMC](https://github.com/e9t/nsmc)(acc)  | [KorSTS](https://github.com/kakaobrain/KorNLUDatasets)(spearman) | [Question Pair](https://github.com/aisolab/nlp_classification/tree/master/BERT_pairwise_text_classification/qpair)(acc) |
|---|---|---|---|
| **KoBART-base**  | 90.24  | 81.66  | 94.34  |
 
# Model Examination
 
More information needed
 
# Environmental Impact
 
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
- **Hardware Type:** More information needed
- **Hours used:** More information needed
- **Cloud Provider:** More information needed
- **Compute Region:** More information needed
- **Carbon Emitted:** More information needed
 
# Technical Specifications [optional]
 
## Model Architecture and Objective

More information needed 
 
## Compute Infrastructure
 
More information needed 
 
### Hardware
 
 
More information needed
 
### Software
 
More information needed.
 
# Citation

 
**BibTeX:**
 
 
More information needed.

 
 
 
 
# Glossary [optional]
More information needed 
 
# More Information [optional]
More information needed 

 
# Model Card Authors [optional]
 
 Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team


# Model Card Contact
 The model authors note in the [GitHub Repo](https://github.com/haven-jeon/KoBART):
`KoBART` 관련 이슈는 [이곳](https://github.com/SKT-AI/KoBART/issues)에 올려주세요.
 
# How to Get Started with the Model
 
Use the code below to get started with the model.
 
<details>
<summary> Click to expand </summary>

```python
 from transformers import PreTrainedTokenizerFast, BartModel

tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2')
model = BartModel.from_pretrained('gogamza/kobart-base-v2')
 ```
</details>

{"language": "ko", "license": "mit", "tags": ["bart"]}
transformers pytorch safetensors bart feature-extraction ko arxiv:1910.13461 arxiv:1910.09700 license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #feature-extraction #ko #arxiv-1910.13461 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us 

Model Card for kobart-base-v2
=============================


Model Details
=============


Model Description
-----------------


BART(Bidirectional and Auto-Regressive Transformers)는 입력 텍스트 일부에 노이즈를 추가하여 이를 다시 원문으로 복구하는 'autoencoder'의 형태로 학습이 됩니다. 한국어 BART(이하 KoBART) 는 논문에서 사용된 'Text Infilling' 노이즈 함수를 사용하여 40GB 이상의 한국어 텍스트에 대해서 학습한 한국어 'encoder-decoder' 언어 모델입니다. 이를 통해 도출된 'KoBART-base'를 배포합니다.


* Developed by: More information needed
* Shared by [Optional]: Heewon(Haven) Jeon
* Model type: Feature Extraction
* Language(s) (NLP): Korean
* License: MIT
* Parent Model: BART
* Resources for more information:
	+ GitHub Repo
	+ Model Demo Space


Uses
====


Direct Use
----------


This model can be used for the task of Feature Extraction.


Downstream Use [Optional]
-------------------------


More information needed.


Out-of-Scope Use
----------------


The model should not be used to intentionally create hostile or alienating environments for people.


Bias, Risks, and Limitations
============================


Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.


Recommendations
---------------


Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.


Training Details
================


Training Data
-------------



한국어 위키 백과 이외, 뉴스, 책, 모두의 말뭉치 v1.0(대화, 뉴스, ...), 청와대 국민청원 등의 다양한 데이터가 모델 학습에 사용되었습니다.


'vocab' 사이즈는 30,000 이며 대화에 자주 쓰이는 아래와 같은 이모티콘, 이모지 등을 추가하여 해당 토큰의 인식 능력을 올렸습니다.



> 
> , , , , , .. , ':-)', ':)', '-)', '(-:'...
> 
> 
> 


Training Procedure
------------------


### Tokenizer


'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다.


### Speeds, Sizes, Times



Evaluation
==========


Testing Data, Factors & Metrics
-------------------------------


### Testing Data


More information needed


### Factors


More information needed


### Metrics


More information needed


Results
-------


NSMC


* acc. : 0.901


The model authors also note in the GitHub Repo:



Model Examination
=================


More information needed


Environmental Impact
====================


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type: More information needed
* Hours used: More information needed
* Cloud Provider: More information needed
* Compute Region: More information needed
* Carbon Emitted: More information needed


Technical Specifications [optional]
===================================


Model Architecture and Objective
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed


### Hardware


More information needed


### Software


More information needed.


BibTeX:


More information needed.


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team


Model Card Contact
==================


The model authors note in the GitHub Repo:
'KoBART' 관련 이슈는 이곳에 올려주세요.


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand 


### Tokenizer


'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다. ### Speeds, Sizes, Times



Evaluation
==========


Testing Data, Factors & Metrics
------------------------------- ### Testing Data


More information needed ### Factors


More information needed ### Metrics


More information needed


Results
-------


NSMC


* acc. : 0.901


The model authors also note in the GitHub Repo:



Model Examination
=================


More information needed


Environmental Impact
====================


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type: More information needed
* Hours used: More information needed
* Cloud Provider: More information needed
* Compute Region: More information needed
* Carbon Emitted: More information needed


Technical Specifications [optional]
===================================


Model Architecture and Objective
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed ### Hardware


More information needed ### Software


More information needed.


BibTeX:


More information needed.


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team


Model Card Contact
==================


The model authors note in the GitHub Repo:
'KoBART' 관련 이슈는 이곳에 올려주세요.


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand
TAGS
#transformers #pytorch #safetensors #bart #feature-extraction #ko #arxiv-1910.13461 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us 
 ### Tokenizer


'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다. ### Speeds, Sizes, Times



Evaluation
==========


Testing Data, Factors & Metrics
------------------------------- ### Testing Data


More information needed ### Factors


More information needed ### Metrics


More information needed


Results
-------


NSMC


* acc. : 0.901


The model authors also note in the GitHub Repo:



Model Examination
=================


More information needed


Environmental Impact
====================


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type: More information needed
* Hours used: More information needed
* Cloud Provider: More information needed
* Compute Region: More information needed
* Carbon Emitted: More information needed


Technical Specifications [optional]
===================================


Model Architecture and Objective
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed ### Hardware


More information needed ### Software


More information needed.


BibTeX:


More information needed.


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team


Model Card Contact
==================


The model authors note in the GitHub Repo:
'KoBART' 관련 이슈는 이곳에 올려주세요.


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand
passage: TAGS
#transformers #pytorch #safetensors #bart #feature-extraction #ko #arxiv-1910.13461 #arxiv-1910.09700 #license-mit #endpoints_compatible #region-us 
### Tokenizer


'tokenizers' 패키지의 'Character BPE tokenizer'로 학습되었습니다.### Speeds, Sizes, Times



Evaluation
==========


Testing Data, Factors & Metrics
-------------------------------### Testing Data


More information needed### Factors


More information needed### Metrics


More information needed


Results
-------


NSMC


* acc. : 0.901


The model authors also note in the GitHub Repo:



Model Examination
=================


More information needed


Environmental Impact
====================


Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).


* Hardware Type: More information needed
* Hours used: More information needed
* Cloud Provider: More information needed
* Compute Region: More information needed
* Carbon Emitted: More information needed


Technical Specifications [optional]
===================================


Model Architecture and Objective
--------------------------------


More information needed


Compute Infrastructure
----------------------


More information needed### Hardware


More information needed### Software


More information needed.


BibTeX:


More information needed.


Glossary [optional]
===================


More information needed


More Information [optional]
===========================


More information needed


Model Card Authors [optional]
=============================


Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team


Model Card Contact
==================


The model authors note in the GitHub Repo:
'KoBART' 관련 이슈는 이곳에 올려주세요.


How to Get Started with the Model
=================================


Use the code below to get started with the model.



 Click to expand