---
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
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-*
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](https://deeppavlov.github.io/AutoIntent/index.html).

## Usage

It is intended to be used with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
from autointent import Dataset

snips = Dataset.from_datasets("AutoIntent/snips")
```

## Source

This dataset is taken from `benayas/snips` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
"""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})
```