Create README.md

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is a merged version from a trained LoRa Adapter which was fine-tuned above the foundation model [KT-AI/midm-bitext-S-7B-inst-v1](KT-AI/midm-bitext-S-7B-inst-v1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** [Jangmin Oh](https://huggingface.co/jangmin)
- **Model type:** KT-AI/midm-bitext-S-7B-inst-v1
- **Language(s) (NLP):** ko
- **License:** CC-BY-NC 4.0 from KT-AI. Please refer [KT-AI/midm-bitext-S-7B-inst-v1](KT-AI/midm-bitext-S-7B-inst-v1).
- **Finetuned from model:** [KT-AI/midm-bitext-S-7B-inst-v1](KT-AI/midm-bitext-S-7B-inst-v1)

# Purpose of the model

It is designed with the expectation of understanding Korean food ordering sentences, and analyzing food menus, option names, and quantities.

# Uses

TODO




# Dataset

I reused the dataset from [jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K](jangmin/qlora-llama2-7b-chat-hf-food-order-understanding-30K). However, I am sorry that the dataset can not be disclosed. In fact, the dataset was constructed using GPT-API with `gpt-4`. A prompt template was desginged to generate examples of sentence pairs of a food order and its understanding. Total 30k examples were generated. Note that it cost about $400 to generate 30K examples through 3,000 API calls.

Some generated examples are as follows:

```json
{
'input': '다음은 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n분석 결과를 완성해주기 바란다.\n\n### 명령: 제육볶음 한그릇하고요, 비빔밥 한그릇 추가해주세요. ### 응답:\n',
'output': '- 분석 결과 0: 음식명:제육볶음,수량:한그릇\n- 분석 결과 1: 음식명:비빔밥,수량:한그릇'
},
{
'input': '다음은 매장에서 고객이 음식을 주문하는 주문 문장이다. 이를 분석하여 음식명, 옵션명, 수량을 추출하여 고객의 의도를 이해하고자 한다.\n분석 결과를 완성해주기 바란다.\n\n### 명령: 사천탕수육 곱배기 주문하고요, 샤워크림치킨도 하나 추가해주세요. ### 응답:\n',
'output': '- 분석 결과 0: 음식명:사천탕수육,옵션:곱배기\n- 분석 결과 1: 음식명:샤워크림치킨,수량:하나'
}
```

README.md

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+---
+language:
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+pipeline_tag: text-generation
+---