README.md

metadata

license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - data-preprocessing
  - automl
  - benchmarks
size_categories:
  - n<1K
  - 1K<n<10K
  - 10K<n<100K
  - 100K<n<1M
dataset_info:
  - config_name: imdb
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_examples: 18750
      - name: test
        num_examples: 25000
      - name: validation
        num_examples: 6250
  - config_name: twenty_newsgroups
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
      - name: label_text
        dtype: string
    splits:
      - name: train
        num_examples: 8485
      - name: test
        num_examples: 7532
      - name: validation
        num_examples: 2829
  - config_name: banking77
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_examples: 7502
      - name: test
        num_examples: 3080
      - name: validation
        num_examples: 2501
  - config_name: trec
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_examples: 4089
      - name: test
        num_examples: 500
      - name: validation
        num_examples: 1363
  - config_name: financial_phrasebank
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_examples: 1358
      - name: test
        num_examples: 453
      - name: validation
        num_examples: 453
  - config_name: MASSIVE
    features:
      - name: text
        dtype: string
      - name: label
        dtype: int64
    splits:
      - name: train
        num_examples: 11514
      - name: test
        num_examples: 2974
      - name: validation
        num_examples: 2033
configs:
  - config_name: imdb
    data_files:
      - split: train
        path: imdb/train.csv
      - split: test
        path: imdb/test.csv
      - split: validation
        path: imdb/validation.csv
  - config_name: twenty_newsgroups
    data_files:
      - split: train
        path: twenty_newsgroups/train.csv
      - split: test
        path: twenty_newsgroups/test.csv
      - split: validation
        path: twenty_newsgroups/validation.csv
  - config_name: banking77
    data_files:
      - split: train
        path: banking77/train.csv
      - split: test
        path: banking77/test.csv
      - split: validation
        path: banking77/validation.csv
  - config_name: trec
    data_files:
      - split: train
        path: trec/train.csv
      - split: test
        path: trec/test.csv
      - split: validation
        path: trec/validation.csv
  - config_name: financial_phrasebank
    data_files:
      - split: train
        path: financial_phrasebank/train.csv
      - split: test
        path: financial_phrasebank/test.csv
      - split: validation
        path: financial_phrasebank/validation.csv
  - config_name: MASSIVE
    data_files:
      - split: train
        path: MASSIVE/train.csv
      - split: test
        path: MASSIVE/test.csv
      - split: validation
        path: MASSIVE/validation.csv

Data Preprocessing AutoML Benchmarks

This repository contains text classification datasets with known data quality issues for preprocessing research in AutoML.

Usage

Load a specific dataset configuration like this:

from datasets import load_dataset
# Example for loading the TREC dataset
dataset = load_dataset("MothMalone/data-preprocessing-automl-benchmarks", "trec")

Available Datasets

Below are the details for each dataset configuration available in this repository.

Of course. Here are the completed descriptions for your dataset card.

imdb

• Description: A large movie review dataset for binary sentiment classification, containing 25,000 highly polarized movie reviews for training and 25,000 for testing.
• Data Quality Issue: N/A
• Classes: 2
• Training Samples: 18750
• Validation Samples: 6250
• Test Samples: 25000

twenty_newsgroups

• Description: A collection of approximately 20,000 newsgroup documents, partitioned evenly across 20 different newsgroups, making it a classic benchmark for text classification.
• Data Quality Issue: N/A
• Classes: 20
• Training Samples: 8485
• Validation Samples: 2829
• Test Samples: 7532

banking77

• Description: A fine-grained dataset of 13,083 customer service queries from the banking domain, annotated with 77 distinct intents.
• Data Quality Issue: N/A
• Classes: 77
• Training Samples: 7502
• Validation Samples: 2501
• Test Samples: 3080

trec

• Description: The Text REtrieval Conference (TREC) question classification dataset, containing questions categorized by their answer type (e.g., Person, Location, Number).
• Data Quality Issue: N/A
• Classes: 6
• Training Samples: 4089
• Validation Samples: 1363
• Test Samples: 500

financial_phrasebank

• Description: A collection of sentences from English financial news, annotated for sentiment (positive, negative, or neutral) by financial experts.
• Data Quality Issue: N/A
• Classes: 3
• Training Samples: 1358
• Validation Samples: 453
• Test Samples: 453

MASSIVE

• Description: A multilingual dataset of 1 million utterances for intent classification and slot filling, covering 52 languages. The en-US configuration is used here.
• Data Quality Issue: N/A
• Classes: 60
• Training Samples: 11514
• Validation Samples: 2033
• Test Samples: 2974