Create README.md

README.md

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+# 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 [amazon_review dataset](https://huggingface.co/datasets/amazon_reviews_multi).
+
+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]
+```