---
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](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_datasets("AutoIntent/events")
```

## Source

This dataset is taken from `knowledgator/events_classification_biotech` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
"""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) -> tuple[list[Intent], dict[str, int]]:
    """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)
    name_to_id = {name: i for i, name in enumerate(intent_names)}
    intents_data = [Intent(id=i,name=name) for i, name in enumerate(intent_names)]
    return intents_data, name_to_id


def converting_mapping(example: dict, name_to_id: dict[str, int]) -> dict[str, str | list[int]]:
    """Extract utterance and label and drop the rest."""
    return {
        "utterance": example["content"],
        "label": [
            name_to_id[intent_name] for intent_name in example["all_labels"] if intent_name not in names_to_remove
        ],
    }


def convert_events(events_split: HFDataset, name_to_id: 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={"name_to_id": name_to_id}
    )

    in_domain_samples = []
    oos_samples = []  # actually this dataset doesn't contain oos_samples so this will stay empty
    for sample in events_split.to_list():
        if sample["utterance"] is None:
            continue
        if len(sample["label"]) == 0:
            sample.pop("label")
            oos_samples.append(sample)
        else:
            in_domain_samples.append(sample)

    return [Sample(**sample) for sample in in_domain_samples + oos_samples]

if __name__ == "__main__":
    # FYI: https://github.com/huggingface/datasets/issues/7248
    events_dataset = load_dataset("knowledgator/events_classification_biotech", trust_remote_code=True)

    intents_data, name_to_id = extract_intents_data(events_dataset)

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

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