Instructions to use joonhan/roberta-roa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joonhan/roberta-roa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="joonhan/roberta-roa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("joonhan/roberta-roa") model = AutoModelForTokenClassification.from_pretrained("joonhan/roberta-roa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from joonhan/roberta-roa: direct link, hf CLI and curl.
- Browser
- Download file 405 Bytes
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https://huggingface.co/joonhan/roberta-roa/resolve/main/README.md
- Command line
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hf download hf://joonhan/roberta-roa/README.md
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curl -L -o README.md https://huggingface.co/joonhan/roberta-roa/resolve/main/README.md
405 Bytes
- Fine-tunning "KLUE/roberta-large" model For CER(Company Entity Recognition) With Custom Dataset
- Custom Datasets are composed of news data
label_list = ['O',"B-PER","I-PER","B-ORG","I-ORG","B-COM","I-COM","B-LOC","I-LOC","B-DAT","I-DAT","B-TIM","I-TIM","B-QNT","I-QNT"]
refer_list = ['0','1','2','3','4','5','6','7','8','9','10','11','12','13','14']
- EX: "B-PER" : 1 , "B-COM" : 5