README.md

---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 1315631
    num_examples: 13784
  - name: test
    num_bytes: 68315
    num_examples: 700
  download_size: 581872
  dataset_size: 1383946
- 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: 270
    num_examples: 7
  download_size: 3121
  dataset_size: 270
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:
- ru
---

# Russian 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_ru = Dataset.from_hub("AutoIntent/snips_ru")
```

## Source

This dataset is taken from private github repository `LadaNikitina/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_from_disk

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

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

    for sample in snips_split:
        txt, name = sample["text"], sample["intent"]
        intent_id = name_to_id[name]
        target_list = classwise_utterance_records[intent_id]
        target_list.append({"utterance": txt, "label": intent_id})

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


if __name__ == "__main__":
    # ! git clone git@github.com:LadaNikitina/Snips data/RuSnips
    # ! rm -rf data/RuSnips/.git
    rusnips = load_from_disk("data/RuSnips")

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

    train_samples = convert_rusnips(rusnips["train"], name_to_id=name_to_id)
    test_samples = convert_rusnips(rusnips["test"], name_to_id=name_to_id)

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