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 autoencoder의 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-base를 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:
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
| Data | # of Sentences |
|---|---|
| Korean Wiki | 5M |
| Other corpus | 0.27B |
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 tokenizer로 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:
| NSMC(acc) | KorSTS(spearman) | Question Pair(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 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.
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:
KoBART 관련 이슈는 이곳에 올려주세요.
How to Get Started with the Model
Use the code below to get started with the model.
<details> <summary> Click to expand </summary>
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2')
model = BartModel.from_pretrained('gogamza/kobart-base-v2')
</details>
