参考：
es
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:head_qa/es')
- 说明：
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
de Sanidad, Consumo y Bienestar Social.
The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
- 许可：MIT License
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
2742 |
'train' |
2657 |
'validation' |
1366 |
- 特征：
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"year": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"qid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"qtext": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"ra": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"image": {
"id": null,
"_type": "Image"
},
"answers": [
{
"aid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"atext": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}
en
使用以下命令在 TFDS 中加载此数据集：
ds = tfds.load('huggingface:head_qa/en')
- 说明：
HEAD-QA is a multi-choice HEAlthcare Dataset. The questions come from exams to access a specialized position in the
Spanish healthcare system, and are challenging even for highly specialized humans. They are designed by the Ministerio
de Sanidad, Consumo y Bienestar Social.
The dataset contains questions about the following topics: medicine, nursing, psychology, chemistry, pharmacology and biology.
- 许可：MIT License
- 版本：1.0.0
- 拆分：
| 拆分 | 样本 |
|---|---|
'test' |
2742 |
'train' |
2657 |
'validation' |
1366 |
- 特征：
{
"name": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"year": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"category": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"qid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"qtext": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"ra": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"image": {
"id": null,
"_type": "Image"
},
"answers": [
{
"aid": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"atext": {
"dtype": "string",
"id": null,
"_type": "Value"
}
}
]
}