참조:
다음 명령을 사용하여 TFDS에서 이 데이터세트를 로드합니다.
ds = tfds.load('huggingface:zest')
- 설명 :
ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways.
- 라이선스 : 알려진 라이선스 없음
- 버전 : 0.0.0
- 분할 :
| 나뉘다 | 예 |
|---|---|
'test' | 11980 |
'train' | 10766 |
'validation' | 2280 |
- 특징 :
{
"task_id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"generalization_type": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"derives_from": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"domain": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"all_answers": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}