Datasets:
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---
dataset_info:
- config_name: default
features:
- name: utterance
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 763742
num_examples: 13084
- name: test
num_bytes: 83070
num_examples: 1400
download_size: 409335
dataset_size: 846812
- 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: 260
num_examples: 7
download_size: 3112
dataset_size: 260
- config_name: intentsqwen3-32b
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: tags
sequence: 'null'
- name: regex_full_match
sequence: 'null'
- name: regex_partial_match
sequence: 'null'
- name: description
dtype: string
splits:
- name: intents
num_bytes: 719
num_examples: 7
download_size: 3649
dataset_size: 719
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-*
- config_name: intentsqwen3-32b
data_files:
- split: intents
path: intentsqwen3-32b/intents-*
task_categories:
- text-classification
language:
- en
---
# 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 = Dataset.from_hub("AutoIntent/snips")
```
## Source
This dataset is taken from `benayas/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_dataset
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("category"))
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_snips(split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
"""Convert one split into desired format."""
n_classes = len(name_to_id)
classwise_samples = [[] for _ in range(n_classes)]
for batch in split.iter(batch_size=16, drop_last_batch=False):
for txt, name in zip(batch["text"], batch["category"], strict=False):
intent_id = name_to_id[name]
target_list = classwise_samples[intent_id]
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__":
snips = load_dataset("benayas/snips")
name_to_id, intents_data = _extract_intents_data(snips["train"])
train_samples = convert_snips(snips["train"], name_to_id)
test_samples = convert_snips(snips["test"], name_to_id)
dataset = Dataset.from_dict({"train": train_samples, "test": test_samples, "intents": intents_data})
``` |