---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 1287117
    num_examples: 10003
  - name: test
    num_bytes: 369341
    num_examples: 3080
  download_size: 551449
  dataset_size: 1656458
- 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: 3420
    num_examples: 77
  download_size: 4651
  dataset_size: 3420
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 banking77

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

banking77_ru = Dataset.from_hub("AutoIntent/banking77_ru")
```

## Source

This dataset is taken from github private repository `LadaNikitina/RuBanking77` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
"""Convert banking77 dataset to autointent internal format and scheme."""  # noqa: INP001

import json

import requests
from datasets import Dataset as HFDataset
from datasets import load_from_disk

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


def get_intents_data(github_file: str | None = None) -> list[Intent]:
    """Load specific json from HF repo."""
    github_file = github_file or "https://huggingface.co/datasets/PolyAI/banking77/resolve/main/dataset_infos.json"
    raw_text = requests.get(github_file, timeout=5).text
    dataset_description = json.loads(raw_text)
    intent_names = dataset_description["default"]["features"]["label"]["names"]
    return [Intent(id=i, name=name) for i, name in enumerate(intent_names)]


def convert_banking77(
    banking77_split: HFDataset, intents_data: list[Intent], shots_per_intent: int | None = None
) -> list[Sample]:
    """Convert one split into desired format."""
    all_labels = sorted(banking77_split.unique("label"))
    n_classes = len(intents_data)
    if all_labels != list(range(n_classes)):
        msg = "Something's wrong"
        raise ValueError(msg)

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

    for sample in banking77_split:
        txt, intent_id = sample["text"], sample["label"]
        target_list = classwise_samples[intent_id]
        if shots_per_intent is not None and len(target_list) >= shots_per_intent:
            continue
        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__":
    intents_data = get_intents_data()

    # load dataset
    # ! git clone git@github.com:LadaNikitina/RuBanking77 "data/RuBanking77"
    # ! rm -rf data/RuBanking77/.git
    banking77 = load_from_disk("data/RuBanking77")

    train_samples = convert_banking77(banking77["train"], intents_data=intents_data)
    test_samples = convert_banking77(banking77["test"], intents_data=intents_data)

    banking77_converted = Dataset.from_dict({"train": train_samples, "test": test_samples, "intents": intents_data})
```