massive

This is a text classification dataset. It is intended for machine learning research and experimentation.

This dataset is obtained via formatting another publicly available data to be compatible with our AutoIntent Library.

Usage

It is intended to be used with our AutoIntent Library:

from autointent import Dataset

massive = Dataset.from_hub("AutoIntent/massive")

Source

This dataset is taken from mteb/amazon_massive_intent and formatted with our AutoIntent Library:

from datasets import Dataset as HFDataset
from datasets import load_dataset

from autointent import Dataset
from autointent.schemas import Intent, Sample


def extract_intents_info(split: HFDataset) -> tuple[list[Intent], dict[str, int]]:
    """Extract metadata."""
    intent_names = sorted(split.unique("label"))
    intent_names.remove("cooking_query")
    intent_names.remove("audio_volume_other")
    n_classes = len(intent_names)
    name_to_id = dict(zip(intent_names, range(n_classes), strict=False))
    intents_data = [Intent(id=i, name=intent_names[i]) for i in range(n_classes)]
    return intents_data, name_to_id


def convert_massive(split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
    """Extract utterances and labels."""
    return [Sample(utterance=s["text"], label=name_to_id[s["label"]]) for s in split if s["label"] in name_to_id]


if __name__ == "__main__":
    massive = load_dataset("mteb/amazon_massive_intent", "en")
    intents, name_to_id = extract_intents_info(massive["train"])
    train_samples = convert_massive(massive["train"], name_to_id)
    test_samples = convert_massive(massive["test"], name_to_id)
    validation_samples = convert_massive(massive["validation"], name_to_id)
    dataset = Dataset.from_dict(
        {"intents": intents, "train": train_samples, "test": test_samples, "validation": validation_samples}
    )