model documentation

README.md

@@ -1,8 +1,182 @@
 ---
 language: ko
+license: cc-by-nc-sa-4.0
 tags:
 - gpt2
-license: cc-by-nc-sa-4.0
 ---
 
-For more details: https://github.com/SKT-AI/KoGPT2
+
+# Model Card for kogpt2-base-v2
+ 
+# Model Details
+ 
+## Model Description
+ 
+[GPT-2](https://openai.com/blog/better-language-models/)는 주어진 텍스트의 다음 단어를 잘 예측할 수 있도록 학습된 언어모델이며 문장 생성에 최적화 되어 있습니다. `KoGPT2`는 부족한 한국어 성능을 극복하기 위해 40GB 이상의 텍스트로 학습된 한국어 디코더(`decoder`) 언어모델입니다.
+ 
+- **Developed by:** SK Telecom
+- **Shared by [Optional]:** SK Telecom
+- **Model type:** Text Generation
+- **Language(s) (NLP):** Korean
+- **License:** cc-by-nc-sa-4.0 
+- **Parent Model:** GPT-2
+- **Resources for more information:**
+  - [GitHub Repo](https://github.com/SKT-AI/KoGPT2/tree/master)
+   - [Model Demo Space](https://huggingface.co/spaces/gogamza/kogpt2-base-v2)
+ 	
+
+
+# Uses
+ 
+
+## Direct Use
+This model can be used for the task of Text Generation
+ 
+## 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
+ The model authors also note in the [GitHub Repo](https://github.com/SKT-AI/KoGPT2/tree/master):
+ 
+[`tokenizers`](https://github.com/huggingface/tokenizers) 패키지의 `Character BPE tokenizer`로 학습되었습니다.
+ 
+사전 크기는 51,200 이며 대화에 자주 쓰이는 아래와 같은 이모티콘, 이모지 등을 추가하여 해당 토큰의 인식 능력을 올렸습니다.
+ 
+> 😀, 😁, 😆, 😅, 🤣, .. , `:-)`, `:)`, `-)`, `(-:`...
+ 
+[한국어 위키 백과](https://ko.wikipedia.org/) 이외, 뉴스, [모두의 말뭉치 v1.0](https://corpus.korean.go.kr/), [청와대 국민청원](https://github.com/akngs/petitions) 등의 다양한 데이터가 모델 학습에 사용되었습니다.
+ 
+ 
+## Training Procedure
+
+ 
+### Preprocessing
+ 
+More information needed 
+
+ 
+### Speeds, Sizes, Times
+| Model       |  # of params |   Type   | # of layers  | # of heads | ffn_dim | hidden_dims |
+|--------------|:----:|:-------:|--------:|--------:|--------:|--------------:|
+| `kogpt2-base-v2` |  125M  |  Decoder |   12     | 12      | 3072    | 768 |
+
+ 
+# Evaluation
+ 
+ 
+## Testing Data, Factors & Metrics
+ 
+### Testing Data
+ 
+More information needed 
+ 
+ 
+### Factors
+More information needed
+ 
+### Metrics
+ 
+More information needed
+ 
+ 
+## Results 
+ 
+
+### Classification or Regression
+|   |  [NSMC](https://github.com/e9t/nsmc)(acc)  | [KorSTS](https://github.com/kakaobrain/KorNLUDatasets)(spearman) |
+|---|---|---|
+| **KoGPT2 2.0**  | 89.1  | 77.8  |
+
+ 
+# 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]
+ 
+SK Telecom in collaboration with Ezi Ozoani and the Hugging Face team
+
+
+# Model Card Contact
+ The model authors also note in the [GitHub Repo](https://github.com/SKT-AI/KoGPT2/tree/master)
+> `KoGPT2` 관련 이슈는 [이곳](https://github.com/SKT-AI/KoGPT2/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 AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("skt/kogpt2-base-v2")
+
+model = AutoModelForCausalLM.from_pretrained("skt/kogpt2-base-v2")
+ 
+ ```
+</details>
+