Token Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use chunwoolee0/klue_ner_roberta_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chunwoolee0/klue_ner_roberta_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chunwoolee0/klue_ner_roberta_model")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chunwoolee0/klue_ner_roberta_model") model = AutoModelForTokenClassification.from_pretrained("chunwoolee0/klue_ner_roberta_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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README.md
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Model description
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Pretrained RoBERTa Model on Korean Language. See [Github](https://github.com/KLUE-benchmark/KLUE) and [Paper](https://arxiv.org/abs/2105.09680) for more details.
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## Intended uses & limitations
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## How to use
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_NOTE:_ Use `BertTokenizer` instead of RobertaTokenizer. (`AutoTokenizer` will load `BertTokenizer`)
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```python
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from transformers import AutoModel, AutoTokenizer
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model = AutoModel.from_pretrained("klue/roberta-base")
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tokenizer = AutoTokenizer.from_pretrained("klue/roberta-base")
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```
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## Training and evaluation data
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