Datasets:
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---
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}
)
``` |