README.md

metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        sequence: int64
    splits:
      - name: train
        num_bytes: 9119630
        num_examples: 2755
      - name: test
        num_bytes: 1275997
        num_examples: 380
    download_size: 11308024
    dataset_size: 10395627
  - 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: 1054
        num_examples: 25
    download_size: 3570
    dataset_size: 1054
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-*

events

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.

Usage

It is intended to be used with our AutoIntent Library:

from autointent import Dataset
banking77 = Dataset.from_datasets("AutoIntent/events")

Source

This dataset is taken from knowledgator/events_classification_biotech and formatted with our AutoIntent Library:

"""Convert events 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

# these classes contain too few sampls
names_to_remove = [
    "partnerships & alliances",
    "patent publication",
    "subsidiary establishment",
    "department establishment",
]

def extract_intents_data(events_dataset: HFDataset) -> list[Intent]:
    """Extract intent names and assign ids to them."""
    intent_names = sorted({name for intents in events_dataset["train"]["all_labels"] for name in intents})
    for n in names_to_remove:
        intent_names.remove(n)
    return [Intent(id=i,name=name) for i, name in enumerate(intent_names)]


def converting_mapping(example: dict, intents_data: list[Intent]) -> dict[str, str | list[int] | None]:
    """Extract utterance and OHE label and drop the rest."""
    res = {
        "utterance": example["content"],
        "label": [
            int(intent.name in example["all_labels"]) for intent in intents_data
        ]
    }
    if sum(res["label"]) == 0:
        res["label"] = None
    return res


def convert_events(events_split: HFDataset, intents_data: dict[str, int]) -> list[Sample]:
    """Convert one split into desired format."""
    events_split = events_split.map(
        converting_mapping, remove_columns=events_split.features.keys(),
        fn_kwargs={"intents_data": intents_data}
    )

    samples = []
    for sample in events_split.to_list():
        if sample["utterance"] is None:
            continue
        samples.append(sample)

    mask = [sample["label"] is None for sample in samples]
    n_oos_samples = sum(mask)
    n_in_domain_samples = len(samples) - n_oos_samples
    
    print(f"{n_oos_samples=}")
    print(f"{n_in_domain_samples=}\n")

    # actually there are too few oos samples to include them, so filter out
    samples = list(filter(lambda sample: sample["label"] is not None, samples))

    return [Sample(**sample) for sample in samples]

if __name__ == "__main__":
    # `load_dataset` might not work
    # fix is here: https://github.com/huggingface/datasets/issues/7248
    events_dataset = load_dataset("knowledgator/events_classification_biotech", trust_remote_code=True)

    intents_data = extract_intents_data(events_dataset)

    train_samples = convert_events(events_dataset["train"], intents_data)
    test_samples = convert_events(events_dataset["test"], intents_data)

    events_converted = Dataset.from_dict(
        {"train": train_samples, "test": test_samples, "intents": intents_data}
    )