metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        sequence: int64
    splits:
      - name: train
        num_bytes: 2163109
        num_examples: 16077
    download_size: 444270
    dataset_size: 2177768.2414629594
  - 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: 387
        num_examples: 13
    download_size: 3096
    dataset_size: 387
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
  - 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.

Usage

It is intended to be used with our AutoIntent Library:

from autointent import Dataset

dstc3 = Dataset.from_datasets("AutoIntent/dstc3")

Source

This dataset is taken from marcel-gohsen/dstc3 and formatted with our AutoIntent Library:

from datasets import load_dataset
from autointent import Dataset

# load original data
dstc3 = load_dataset("marcel-gohsen/dstc3")

# extract intent names
dstc3["test"] = dstc3["test"].filter(lambda example: example["transcript"] != "")
intent_names = sorted(set(name for intents in dstc3["test"]["intent"] for name in intents))
intent_names.remove("reqmore")
dstc3["test"].filter(lambda example: "reqmore" in example["intent"])
name_to_id = {name: i for i, name in enumerate(intent_names)}

# parse complicated dstc format
def transform(example: dict):
    return {
        "utterance": example["transcript"],
        "label": [name_to_id[intent_name] for intent_name in example["intent"] if intent_name != "reqmore"],
    }
dstc_converted = dstc3["test"].map(transform, remove_columns=dstc3["test"].features.keys())

# format to autointent.Dataset
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
utterances = []
oos_utterances = []
for rec in dstc_converted.to_list():
    if len(rec["label"]) == 0:
        rec.pop("label")
        oos_utterances.append(rec["utterance"])
    else:
        utterances.append(rec)
oos_records = [{"utterance": ut} for ut in set(oos_utterances)]
dstc_converted = Dataset.from_dict({"intents": intents, "train": utterances + oos_records})