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 "<subj>", "</subj>" to both ends of the subject object and "<obj>", "</obj>" to both ends of the object object.
sentence = "<subj>손흥민</subj>은 <obj>대한민국</obj>에서 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
