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---
dataset_info:
- config_name: default
features:
- name: utterance
dtype: string
- name: label
dtype: int64
splits:
- name: train
num_bytes: 857605
num_examples: 15200
- name: validation
num_bytes: 160686
num_examples: 3100
- name: test
num_bytes: 287654
num_examples: 5500
download_size: 542584
dataset_size: 1305945
- 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: 5368
num_examples: 150
download_size: 5519
dataset_size: 5368
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-*
---
# 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
banking77 = Dataset.from_hub("AutoIntent/clinc150")
```
## Source
This dataset is taken from `cmaldona/All-Generalization-OOD-CLINC150` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
```python
# define util
"""Convert clincq50 dataset to autointent internal format and scheme."""
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(
clinc150_split: HFDataset, oos_intent_name: str = "ood"
) -> tuple[list[Intent], dict[str, int]]:
"""Extract intent names and assign ids to them."""
intent_names = sorted(clinc150_split.unique("labels"))
oos_intent_id = intent_names.index(oos_intent_name)
intent_names.pop(oos_intent_id)
n_classes = len(intent_names)
assert n_classes == 150 # noqa: PLR2004, S101
name_to_id = dict(zip(intent_names, range(n_classes), strict=False))
intents_data = [Intent(id=i, name=name) for name, i in name_to_id.items()]
return intents_data, name_to_id
def convert_clinc150(
clinc150_split: HFDataset,
name_to_id: dict[str, int],
shots_per_intent: int | None = None,
oos_intent_name: str = "ood",
) -> list[Sample]:
"""Convert one split into desired format."""
oos_samples = []
classwise_samples = [[] for _ in range(len(name_to_id))]
n_unrecognized_labels = 0
for batch in clinc150_split.iter(batch_size=16, drop_last_batch=False):
for txt, name in zip(batch["data"], batch["labels"], strict=False):
if name == oos_intent_name:
oos_samples.append(Sample(utterance=txt))
continue
intent_id = name_to_id.get(name, None)
if intent_id is None:
n_unrecognized_labels += 1
continue
target_list = classwise_samples[intent_id]
if shots_per_intent is not None and len(target_list) >= shots_per_intent:
continue
target_list.append(Sample(utterance=txt, label=intent_id))
in_domain_samples = [sample for samples_from_single_class in classwise_samples for sample in samples_from_single_class]
print(f"{len(in_domain_samples)=}")
print(f"{len(oos_samples)=}")
print(f"{n_unrecognized_labels=}\n")
return in_domain_samples + oos_samples
if __name__ == "__main__":
clinc150 = load_dataset("cmaldona/All-Generalization-OOD-CLINC150")
intents_data, name_to_id = extract_intents_data(clinc150["train"])
train_samples = convert_clinc150(clinc150["train"], name_to_id)
validation_samples = convert_clinc150(clinc150["validation"], name_to_id)
test_samples = convert_clinc150(clinc150["test"], name_to_id)
clinc150_converted = Dataset.from_dict(
{"train": train_samples, "validation": validation_samples, "test": test_samples, "intents": intents_data}
)
``` |