readme: ---
  language: ko
  license: mit
  tags:
  - bart
  ---
  
  
  # 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>
  