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subjqa

References:

books

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/books')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 345
'train' 1314
'validation' 256
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

electronics

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/electronics')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 358
'train' 1295
'validation' 255
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

grocery

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/grocery')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 591
'train' 1124
'validation' 218
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

movies

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/movies')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 291
'train' 1369
'validation' 261
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

restaurants

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/restaurants')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 266
'train' 1400
'validation' 267
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}

tripadvisor

Use the following command to load this dataset in TFDS:

ds = tfds.load('huggingface:subjqa/tripadvisor')
  • Description:
SubjQA is a question answering dataset that focuses on subjective questions and answers.
The dataset consists of roughly 10,000 questions over reviews from 6 different domains: books, movies, grocery,
electronics, TripAdvisor (i.e. hotels), and restaurants.
  • License: No known license
  • Version: 1.1.0
  • Splits:
Split Examples
'test' 512
'train' 1165
'validation' 230
  • Features:
{
    "domain": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "nn_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_mod": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "query_asp": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "q_reviews_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question_subj_level": {
        "dtype": "int64",
        "id": null,
        "_type": "Value"
    },
    "ques_subj_score": {
        "dtype": "float32",
        "id": null,
        "_type": "Value"
    },
    "is_ques_subjective": {
        "dtype": "bool",
        "id": null,
        "_type": "Value"
    },
    "review_id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "id": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "title": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "context": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "question": {
        "dtype": "string",
        "id": null,
        "_type": "Value"
    },
    "answers": {
        "feature": {
            "text": {
                "dtype": "string",
                "id": null,
                "_type": "Value"
            },
            "answer_start": {
                "dtype": "int32",
                "id": null,
                "_type": "Value"
            },
            "answer_subj_level": {
                "dtype": "int64",
                "id": null,
                "_type": "Value"
            },
            "ans_subj_score": {
                "dtype": "float32",
                "id": null,
                "_type": "Value"
            },
            "is_ans_subjective": {
                "dtype": "bool",
                "id": null,
                "_type": "Value"
            }
        },
        "length": -1,
        "id": null,
        "_type": "Sequence"
    }
}