Create README.md

README.md

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+# klue-roberta-base-kornli
+
+This model trained with Korean dataset.
+Input premise sentence and hypothesis sentence.
+You can use English, but don't expect accuracy.
+If the context is longer than 1200 characters, the context may be cut in the middle and the result may not come out well.
+
+KLUE-RoBERTa-base-KorNLI DEMO: [Ainize DEMO](https://main-klue-roberta-base-kornli-ehdwns1516.endpoint.ainize.ai/)
+KLUE-RoBERTa-base-KorNLI API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/klue-roberta-base_kornli)
+
+## Overview
+Language model: klue/roberta-base
+Language: Korean
+Training data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
+Eval data: [kakaobrain KorNLI](https://github.com/kakaobrain/KorNLUDatasets/tree/master/KorNLI)
+Code: See [Ainize Workspace](https://a966119d3186.ngrok.io/notebooks/DJ/KLUE-NLI/klue-roberta-base-kornli.ipynb)
+
+## Usage
+## In Transformers
+
+```
+from transformers import AutoTokenizer, pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/klue-roberta-base-kornli")
+
+classifier = pipeline(
+    "text-classification",
+    model="ehdwns1516/klue-roberta-base-kornli",
+    return_all_scores=True,
+)
+
+premise = "your premise"
+hypothesis = "your hypothesis"
+
+result = dict()
+result[0] = classifier(premise + tokenizer.sep_token + hypothesis)[0]
+```