Model reference · open weights
kobart is an open-weight embedding model from gogamza. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | gogamza |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Parameters (lead) | 124M |
| Context | 1026 tokens |
| Runs with | transformers |
| Released | 2022-03-02 |
| Popularity | 3k downloads / month |
| Licence | Open weights |
About
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.
This model can be used for the task of Feature Extraction.
More information needed.
The model should not be used to intentionally create hostile or alienating environments for people.
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.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
| 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.
😀, 😁, 😆, 😅, 🤣, .. ,
:-),:),-),(-:...
tokenizers 패키지의 Character BPE tokenizer로 학습되었습니다.
| 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 |
More information needed
More information needed
More information needed
NSMC
The model authors also note in the GitHub Repo:
| NSMC(acc) | KorSTS(spearman) | Question Pair(acc) | |
|---|---|---|---|
| KoBART-base | 90.24 | 81.66 | 94.34 |
More information needed
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
More information needed
More information needed
More information needed
More information needed.
BibTeX:
More information needed.
More information needed
More information needed
Heewon(Haven) Jeon in collaboration with Ezi Ozoani and the Hugging Face team
The model authors note in the GitHub Repo:
KoBART 관련 이슈는 이곳에 올려주세요.
Use the code below to get started with the model.
from transformers import PreTrainedTokenizerFast, BartModel
tokenizer = PreTrainedTokenizerFast.from_pretrained('gogamza/kobart-base-v2')
model = BartModel.from_pretrained('gogamza/kobart-base-v2')
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys kobart for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (kobart below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"kobart","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.