Token Classification
Transformers
PyTorch
Literary Chinese
roberta
classical chinese
literary chinese
ancient chinese
pos
dependency-parsing
Instructions to use KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith", device_map="auto") - Notebooks
- Google Colab
- Kaggle
roberta-classical-chinese-base-ud-goeswith
Model Description
This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing (using goeswith for subwords), derived from roberta-classical-chinese-base-char and UD_Classical_Chinese-Kyoto.
How to Use
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/roberta-classical-chinese-base-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("孟子見梁惠王"))
Reference
Koichi Yasuoka: Sequence-Labeling RoBERTa Model for Dependency-Parsing in Classical Chinese and Its Application to Vietnamese and Thai, ICBIR 2023: 8th International Conference on Business and Industrial Research (May 2023), pp.169-173.
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