| # 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] | |
| ``` | |