README.md

metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 763742
        num_examples: 13084
      - name: test
        num_bytes: 83070
        num_examples: 1400
    download_size: 409335
    dataset_size: 846812
  - 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: 260
        num_examples: 7
    download_size: 3112
    dataset_size: 260
  - config_name: intentsqwen3-32b
    features:
      - name: id
        dtype: int64
      - name: name
        dtype: string
      - name: tags
        sequence: 'null'
      - name: regex_full_match
        sequence: 'null'
      - name: regex_partial_match
        sequence: 'null'
      - name: description
        dtype: string
    splits:
      - name: intents
        num_bytes: 719
        num_examples: 7
    download_size: 3649
    dataset_size: 719
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
  - config_name: intents
    data_files:
      - split: intents
        path: intents/intents-*
  - config_name: intentsqwen3-32b
    data_files:
      - split: intents
        path: intentsqwen3-32b/intents-*
task_categories:
  - text-classification
language:
  - en

snips

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

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

Source

This dataset is taken from benayas/snips and formatted with our AutoIntent Library:

"""Convert snips dataset to autointent internal format and scheme."""  # noqa: INP001

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(split: HFDataset) -> tuple[dict[str, int], list[Intent]]:
    intent_names = sorted(split.unique("category"))
    name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))

    return name_to_id, [Intent(id=i, name=name) for i, name in enumerate(intent_names)]


def convert_snips(split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
    """Convert one split into desired format."""
    n_classes = len(name_to_id)

    classwise_samples = [[] for _ in range(n_classes)]

    for batch in split.iter(batch_size=16, drop_last_batch=False):
        for txt, name in zip(batch["text"], batch["category"], strict=False):
            intent_id = name_to_id[name]
            target_list = classwise_samples[intent_id]
            target_list.append({"utterance": txt, "label": intent_id})

    return [Sample(**sample) for samples_from_one_class in classwise_samples for sample in samples_from_one_class]


if __name__ == "__main__":
    snips = load_dataset("benayas/snips")

    name_to_id, intents_data = _extract_intents_data(snips["train"])

    train_samples = convert_snips(snips["train"], name_to_id)
    test_samples = convert_snips(snips["test"], name_to_id)

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