readme: # ehdwns1516/gpt3-kor-based_gpt2_review_SR1
  
  * This model has been trained Korean dataset as a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
  * Input text what you want to generate review.
  * If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
  
  review generator DEMO: [Ainize DEMO](https://main-review-generator-ehdwns1516.endpoint.ainize.ai/)
  
  review generator API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/review_generator)
  
  ## Model links for each 1 to 5 star
  * [ehdwns1516/gpt3-kor-based_gpt2_review_SR1](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR1)
  * [ehdwns1516/gpt3-kor-based_gpt2_review_SR2](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR2)
  * [ehdwns1516/gpt3-kor-based_gpt2_review_SR3](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR3)
  * [ehdwns1516/gpt3-kor-based_gpt2_review_SR4](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR4)
  * [ehdwns1516/gpt3-kor-based_gpt2_review_SR5](https://huggingface.co/ehdwns1516/gpt3-kor-based_gpt2_review_SR5)
  
  ## Overview
  
  Language model: [gpt3-kor-small_based_on_gpt2](https://huggingface.co/kykim/gpt3-kor-small_based_on_gpt2)
  
  Language: Korean
  
  Training data: review_body dataset with a star of 1 in the [naver shopping reivew dataset](https://github.com/bab2min/corpus/tree/master/sentiment).
  
  Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/gpt2_review_fine-tunning_note)
  
  ## Usage
  ## In Transformers
  
  ```
  from transformers import AutoTokenizer, AutoModelWithLMHead
    
  tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1")
  
  model = AutoModelWithLMHead.from_pretrained("ehdwns1516/gpt3-kor-based_gpt2_review_SR1")
  
  generator = pipeline(
      "text-generation",
      model="ehdwns1516/gpt3-kor-based_gpt2_review_SR1",
      tokenizer=tokenizer
  )
  
  context = "your context"
  
  result = dict()
  result[0] = generator(context)[0]
  ```
  