metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_bytes: 924830
        num_examples: 11514
    download_size: 347436
    dataset_size: 924830
  - 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: 2266
        num_examples: 60
    download_size: 3945
    dataset_size: 2266
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:
  - ru

Russian massive

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

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

Source

This dataset is taken from mteb/amazon_massive_intent and formatted with our AutoIntent Library:

from datasets import load_dataset

def convert_massive(massive_train):
    intent_names = sorted(massive_train.unique("label"))
    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 massive_train.iter(batch_size=16, drop_last_batch=False):
        for txt, name in zip(batch["text"], batch["label"], 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})

massive = load_dataset("mteb/amazon_massive_intent", "ru")
massive_converted = convert_massive(massive["train"])