---
tags:
- "japanese"
- "question-answering"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "question-answering"
widget:
- text: "国語"
  context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "教科書"
  context: "全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
- text: "の"
  context: "全学年にわたって小学校の国語[MASK]教科書に挿し絵が用いられている"
---

# deberta-base-japanese-aozora-ud-head

## Model Description

This is a DeBERTa(V2) model pretrained on 青空文庫 for dependency-parsing (head-detection on long-unit-words) as question-answering, derived from [deberta-base-japanese-aozora](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora) and [UD_Japanese-GSDLUW](https://huggingface.co/UniversalDependencies/UD_Japanese-GSDLUW). Use [MASK] inside `context` to avoid ambiguity when specifing a multiple-used word as `question`.

## How to Use

```py
import torch
from transformers import AutoTokenizer,AutoModelForQuestionAnswering
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
model=AutoModelForQuestionAnswering.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora-ud-head")
question="国語"
context="全学年にわたって小学校の国語の教科書に挿し絵が用いられている"
inputs=tokenizer(question,context,return_tensors="pt",return_offsets_mapping=True)
offsets=inputs.pop("offset_mapping").tolist()[0]
outputs=model(**inputs)
start,end=torch.argmax(outputs.start_logits),torch.argmax(outputs.end_logits)
print(context[offsets[start][0]:offsets[end][-1]])
```