---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 923371
    num_examples: 11492
  - name: validation
    num_bytes: 162616
    num_examples: 2031
  - name: test
    num_bytes: 235839
    num_examples: 2968
  download_size: 564588
  dataset_size: 1321826
- 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: 2187
    num_examples: 58
  download_size: 3921
  dataset_size: 2187
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-*
task_categories:
- text-classification
language:
- ru
---

# Russian 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](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

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

## Source

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

```python
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", "ru")
    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}
    )
```