README.md

metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 763742
        num_examples: 13084
    download_size: 366002
    dataset_size: 763742
  - 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: 260
        num_examples: 7
    download_size: 3112
    dataset_size: 260
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

snips

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

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

Source

This dataset is taken from benayas/snips and formatted with our AutoIntent Library:

# define util
from datasets import load_dataset
from autointent import Dataset

def convert_snips(snips_train):
    intent_names = sorted(snips_train.unique("category"))
    name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))
    n_classes = len(intent_names)

    classwise_utterance_records = [[] for _ in range(n_classes)]
    intents = [
        {
            "id": i,
            "name": name,
        }
        for i, name in enumerate(intent_names)
    ]

    for batch in snips_train.iter(batch_size=16, drop_last_batch=False):
        for txt, name in zip(batch["text"], batch["category"], strict=False):
            intent_id = name_to_id[name]
            target_list = classwise_utterance_records[intent_id]
            target_list.append({"utterance": txt, "label": intent_id})

    utterances = [rec for lst in classwise_utterance_records for rec in lst]
    return Dataset.from_dict({"intents": intents, "train": utterances})

# load and format
snips = load_dataset("benayas/snips")
snips_converted = convert_snips(snips["train"])