Datasets:
File size: 3,868 Bytes
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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})
``` |