---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 1422940
    num_examples: 15250
  - name: validation
    num_bytes: 286013
    num_examples: 3100
  - name: test
    num_bytes: 517027
    num_examples: 5500
  download_size: 1202523
  dataset_size: 2249306.8852459015
- config_name: intents
  features:
  - name: id
    dtype: int64
  - name: name
    dtype: 'null'
  - 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: 3000
    num_examples: 150
  download_size: 3651
  dataset_size: 3000
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 clinc150

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

clinc150_ru = Dataset.from_hub("AutoIntent/clinc150_ru")
```

## Source

This dataset is taken from private github repository `LadaNikitina/clinc150` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
from autointent import Dataset
from autointent.schemas import Sample
from datasets import load_from_disk, Dataset as HFDataset


def convert_ruclinc150(clinc150_train: HFDataset, ood_index=42):
    all_labels = sorted(clinc150_train.unique("intent"))
    assert all_labels == list(range(151))

    in_domain_samples = clinc150_train.filter(lambda x: x["intent"] != ood_index)
    oos_samples = clinc150_train.filter(lambda x: x["intent"] == ood_index)

    classwise_samples = [[] for _ in range(150)]

    for batch in in_domain_samples.iter(batch_size=16, drop_last_batch=False):
        for txt, intent_id in zip(batch["text"], batch["intent"], strict=False):
            intent_id -= int(intent_id > ood_index)
            target_list = classwise_samples[intent_id]
            target_list.append({"utterance": txt, "label": intent_id})

    train_samples = [sample for samples_from_one_class in classwise_samples for sample in samples_from_one_class]
    oos_samples = [{"utterance": txt} for txt in oos_samples["text"]]

    return [Sample(**sample) for sample in train_samples + oos_samples]

if __name__ == "__main__":
    # git clone git@github.com:LadaNikitina/clinc150 data/RuClinc150
    # rm -rf data/RuClinc150/.git

    clinc150 = load_from_disk("data/RuClinc150")
    train_samples = convert_ruclinc150(clinc150["train"])
    val_samples = convert_ruclinc150(clinc150["validation"])
    test_samples = convert_ruclinc150(clinc150["test"])

    clinc150_converted = Dataset.from_dict(
        {"train": train_samples, "validation": val_samples, "test": test_samples}
    )
```