README.md

metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 857605
        num_examples: 15200
      - name: validation
        num_bytes: 160686
        num_examples: 3100
      - name: test
        num_bytes: 287654
        num_examples: 5500
    download_size: 542584
    dataset_size: 1305945
  - config_name: intents
    features:
      - name: id
        dtype: int64
      - name: name
        dtype: string
      - name: tags
        sequence: 'null'
      - name: regexp_full_match
        sequence: 'null'
      - name: regexp_partial_match
        sequence: 'null'
      - name: description
        dtype: 'null'
    splits:
      - name: intents
        num_bytes: 5368
        num_examples: 150
    download_size: 5519
    dataset_size: 5368
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
  - config_name: intents
    data_files:
      - split: intents
        path: intents/intents-*

clinc150

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
banking77 = Dataset.from_hub("AutoIntent/clinc150")

Source

This dataset is taken from cmaldona/All-Generalization-OOD-CLINC150 and formatted with our AutoIntent Library:

# define util
"""Convert clincq50 dataset to autointent internal format and scheme."""

from datasets import Dataset as HFDataset
from datasets import load_dataset

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


def extract_intents_data(
    clinc150_split: HFDataset, oos_intent_name: str = "ood"
) -> tuple[list[Intent], dict[str, int]]:
    """Extract intent names and assign ids to them."""
    intent_names = sorted(clinc150_split.unique("labels"))
    oos_intent_id = intent_names.index(oos_intent_name)
    intent_names.pop(oos_intent_id)

    n_classes = len(intent_names)
    assert n_classes == 150  # noqa: PLR2004, S101

    name_to_id = dict(zip(intent_names, range(n_classes), strict=False))
    intents_data = [Intent(id=i, name=name) for name, i in name_to_id.items()]
    return intents_data, name_to_id


def convert_clinc150(
    clinc150_split: HFDataset,
    name_to_id: dict[str, int],
    shots_per_intent: int | None = None,
    oos_intent_name: str = "ood",
) -> list[Sample]:
    """Convert one split into desired format."""
    oos_samples = []
    classwise_samples = [[] for _ in range(len(name_to_id))]
    n_unrecognized_labels = 0

    for batch in clinc150_split.iter(batch_size=16, drop_last_batch=False):
        for txt, name in zip(batch["data"], batch["labels"], strict=False):
            if name == oos_intent_name:
                oos_samples.append(Sample(utterance=txt))
                continue
            intent_id = name_to_id.get(name, None)
            if intent_id is None:
                n_unrecognized_labels += 1
                continue
            target_list = classwise_samples[intent_id]
            if shots_per_intent is not None and len(target_list) >= shots_per_intent:
                continue
            target_list.append(Sample(utterance=txt, label=intent_id))

    in_domain_samples = [sample for samples_from_single_class in classwise_samples for sample in samples_from_single_class]
    
    print(f"{len(in_domain_samples)=}")
    print(f"{len(oos_samples)=}")
    print(f"{n_unrecognized_labels=}\n")
    
    return in_domain_samples + oos_samples


if __name__ == "__main__":
    clinc150 = load_dataset("cmaldona/All-Generalization-OOD-CLINC150")

    intents_data, name_to_id = extract_intents_data(clinc150["train"])

    train_samples = convert_clinc150(clinc150["train"], name_to_id)
    validation_samples = convert_clinc150(clinc150["validation"], name_to_id)
    test_samples = convert_clinc150(clinc150["test"], name_to_id)

    clinc150_converted = Dataset.from_dict(
        {"train": train_samples, "validation": validation_samples, "test": test_samples, "intents": intents_data}
    )