klue-bert-base-re

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>

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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