Create README.md

README.md

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+# Tutorial for KLUE Relation Extraction.
+Fine-tuning klue/bert-base using KLUE RE dataset.
+- <a href="https://klue-benchmark.com/">KLUE Benchmark Official Webpage</a>
+- <a href="https://github.com/KLUE-benchmark/KLUE">KLUE Official Github</a> 
+- <a href="https://huggingface.co/ainize/klue-bert-base-re">Model on Huggingface</a>
+- Run KLUE RE on free GPU : <a href="https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ainize-team/klue-re-workspace">Ainize Workspace</a>
+
+# Usage
+- <a href="https://github.com/KLUE-benchmark/KLUE/blob/main/klue_benchmark/klue-re-v1/relation_list.json">KLUE Relation List</a>
+<pre><code>
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("ainize/klue-bert-base-re")
+model = AutoModelForSequenceClassification.from_pretrained("ainize/klue-bert-base-re")
+
+# Add "&ltsubj&gt", "&lt/subj&gt" to both ends of the subject object and "&ltobj&gt", "&lt/obj&gt" to both ends of the object object.
+sentence = "&ltsubj&gt손흥민&lt/subj&gt은 &ltobj&gt대한민국&lt/obj&gt에서 태어났다."
+
+encodings = tokenizer(sentence, 
+                      max_length=128, 
+                      truncation=True, 
+                      padding="max_length", 
+                      return_tensors="pt")
+
+outputs = model(**encodings)
+
+logits = outputs['logits']
+
+preds = torch.argmax(logits, dim=1)
+</code></pre>
+
+# About us
+- <a href="https://ainize.ai/teachable-nlp">Teachable NLP</a> - Train NLP models with your own text without writing any code
+- <a href="https://ainize.ai/">Ainize</a> - Deploy ML project using free gpu