Datasets:
File size: 3,343 Bytes
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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})
``` |