তথ্যসূত্র:
TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:
ds = tfds.load('huggingface:msr_sqa')
- বর্ণনা :
Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ), which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.
- লাইসেন্স : মাইক্রোসফ্ট রিসার্চ ডেটা লাইসেন্স চুক্তি
- সংস্করণ : 0.0.0
- বিভাজন :
| বিভক্ত | উদাহরণ |
|---|---|
'test' | 3012 |
'train' | 14541 |
- বৈশিষ্ট্য :
{
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"annotator": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"position": {
"dtype": "int32",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"table_file": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"table_header": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"table_data": {
"feature": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer_coordinates": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"answer_text": {
"feature": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}