---
dataset_info:
- config_name: default
  features:
  - name: utterance
    dtype: string
  - name: label
    sequence: int64
  splits:
  - name: train
    num_bytes: 1074443.008463079
    num_examples: 12384
  - name: test
    num_bytes: 268523.991536921
    num_examples: 3095
  download_size: 300800
  dataset_size: 1342967.0
- config_name: intents
  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: 'null'
  splits:
  - name: intents
    num_bytes: 207
    num_examples: 7
  download_size: 2996
  dataset_size: 207
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:
- en
---

# dstc3

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

dstc3 = Dataset.from_hub("AutoIntent/dstc3")
```

## Source

This dataset is taken from `marcel-gohsen/dstc3` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):

```python
import datasets
from autointent import Dataset
from autointent.context.data_handler import split_dataset


def extract_intent_info(ds: datasets.Dataset) -> list[str]:
    ds = ds.filter(lambda example: example["transcript"] != "")
    intent_names = sorted(
        set(name for intents in ds["intent"] for name in intents)
    )
    intent_names.remove("reqmore")
    ds.filter(lambda example: "reqmore" in example["intent"])
    return intent_names

def parse(ds: datasets.Dataset, intent_names: list[str]):
    def transform(example: dict):
        return {
            "utterance": example["transcript"],
            "label": [int(name in example["intent"]) for name in intent_names],
        }
    return ds.map(
        transform, remove_columns=ds.features.keys()
    )

def calc_fractions(ds: datasets.Dataset, intent_names: list[str]) -> list[float]:
    res = [0] * len(intent_names)
    for sample in ds:
        for i, indicator in enumerate(sample["label"]):
            res[i] += indicator
    for i in range(len(intent_names)):
        res[i] /= len(ds)
    return res

def remove_low_resource_classes(ds: datasets.Dataset, intent_names: list[str], fraction_thresh: float = 0.01) -> tuple[list[dict], list[str]]:
    remove_or_not = [(frac < fraction_thresh) for frac in calc_fractions(ds, intent_names)]
    intent_names = [name for i, name in enumerate(intent_names) if not remove_or_not[i]]
    res = []
    for sample in ds:
        if sum(sample["label"]) == 1 and remove_or_not[sample["label"].index(1)]:
            continue
        sample["label"] = [
            indicator for indicator, low_resource in
            zip(sample["label"], remove_or_not, strict=True) if not low_resource
        ]
        res.append(sample)
    return res, intent_names

def remove_oos(ds: datasets.Dataset):
    return ds.filter(lambda sample: sum(sample["label"]) != 0)


if __name__ == "__main__":
    dstc3 = datasets.load_dataset("marcel-gohsen/dstc3")
    
    intent_names = extract_intent_info(dstc3["test"])
    parsed = parse(dstc3["test"], intent_names)
    filtered, intent_names = remove_low_resource_classes(remove_oos(parsed), intent_names)
    intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
    dstc_final = Dataset.from_dict({"intents": intents, "train": filtered})
    dstc_final["train"], dstc_final["test"] = split_dataset(
        dstc_final, split="train", test_size=0.2, random_seed=42
    )
```