তথ্যসূত্র:
রান্না
TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:
ds = tfds.load('huggingface:doqa/cooking')
- বর্ণনা :
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
- লাইসেন্স : কোনো পরিচিত লাইসেন্স নেই
- সংস্করণ : 2.1.0
- বিভাজন :
| বিভক্ত | উদাহরণ |
|---|---|
'test' | 1797 |
'train' | 4612 |
'validation' | 911 |
- বৈশিষ্ট্য :
{
"title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"background": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"followup": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"yesno": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"orig_answer": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
সিনেমা
TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:
ds = tfds.load('huggingface:doqa/movies')
- বর্ণনা :
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
- লাইসেন্স : কোনো পরিচিত লাইসেন্স নেই
- সংস্করণ : 2.1.0
- বিভাজন :
| বিভক্ত | উদাহরণ |
|---|---|
'test' | 1884 |
- বৈশিষ্ট্য :
{
"title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"background": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"followup": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"yesno": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"orig_answer": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}
ভ্রমণ
TFDS এ এই ডেটাসেট লোড করতে নিম্নলিখিত কমান্ডটি ব্যবহার করুন:
ds = tfds.load('huggingface:doqa/travel')
- বর্ণনা :
DoQA is a dataset for accessing Domain Specific FAQs via conversational QA that contains 2,437 information-seeking question/answer dialogues
(10,917 questions in total) on three different domains: cooking, travel and movies. Note that we include in the generic concept of FAQs also
Community Question Answering sites, as well as corporate information in intranets which is maintained in textual form similar to FAQs, often
referred to as internal “knowledge bases”.
These dialogues are created by crowd workers that play the following two roles: the user who asks questions about a given topic posted in Stack
Exchange (https://stackexchange.com/), and the domain expert who replies to the questions by selecting a short span of text from the long textual
reply in the original post. The expert can rephrase the selected span, in order to make it look more natural. The dataset covers unanswerable
questions and some relevant dialogue acts.
DoQA enables the development and evaluation of conversational QA systems that help users access the knowledge buried in domain specific FAQs.
- লাইসেন্স : কোনো পরিচিত লাইসেন্স নেই
- সংস্করণ : 2.1.0
- বিভাজন :
| বিভক্ত | উদাহরণ |
|---|---|
'test' | 1713 |
- বৈশিষ্ট্য :
{
"title": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"background": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"context": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"question": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"id": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answers": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
},
"followup": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"yesno": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"orig_answer": {
"feature": {
"text": {
"dtype": "string",
"id": null,
"_type": "Value"
},
"answer_start": {
"dtype": "int32",
"id": null,
"_type": "Value"
}
},
"length": -1,
"id": null,
"_type": "Sequence"
}
}