Token Classification
Transformers
PyTorch
Literary Chinese
qwen2
classical chinese
literary chinese
ancient chinese
pos
dependency-parsing
text-generation-inference
Instructions to use KoichiYasuoka/Xunzi-Qwen2-7B-upos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/Xunzi-Qwen2-7B-upos with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/Xunzi-Qwen2-7B-upos")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/Xunzi-Qwen2-7B-upos") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/Xunzi-Qwen2-7B-upos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Xunzi-Qwen2-7B-upos
Model Description
This is a Qwen2 model pre-trained on Classical Chinese texts for POS-tagging, derived from Xunzi-Qwen2-7B. Every word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.
How to Use
from transformers import pipeline
nlp=pipeline("upos","KoichiYasuoka/Xunzi-Qwen2-7B-upos",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("不入虎穴不得虎子"))
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