dependency-parsing

README.md

@@ -7,6 +7,7 @@ tags:
 - "ancient chinese"
 - "token-classification"
 - "pos"
+- "dependency-parsing"
 datasets:
 - "universal_dependencies"
 license: "apache-2.0"
@@ -19,7 +20,7 @@ widget:
 
 ## Model Description
 
-This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
+This is a RoBERTa model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [roberta-classical-chinese-large-char](https://huggingface.co/KoichiYasuoka/roberta-classical-chinese-large-char). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
 
 ## How to Use
 
@@ -28,9 +29,12 @@ import torch
 from transformers import AutoTokenizer,AutoModelForTokenClassification
 tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")
 model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-classical-chinese-large-upos")
-s="子曰學而時習之不亦説乎有朋自遠方來不亦樂乎人不知而不慍不亦君子乎"
-p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
-print(list(zip(s,p)))
+```
+or
+
+```py
+import esupar
+nlp=esupar.load("KoichiYasuoka/roberta-classical-chinese-large-upos")
 ```
 
 ## Reference