readme: # bert-base for KLUE Relation Extraction task.
  Fine-tuned 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://github.com/ainize-team/klue-re-workspace">KLUE RE Github</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>
  
  <br>
  
  # Usage
  <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>
  
  <br>
  
  # 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