metadata

license: cc
multilinguality: multilingual
task_categories:
  - multiple-choice
pretty_name: Tokenization Robustness
tags:
  - multilingual
  - tokenization
dataset_info:
  config_name: social_media_informal_text
  features:
    - name: question
      dtype: string
    - name: choices
      sequence: string
    - name: answer
      dtype: int64
    - name: answer_label
      dtype: string
    - name: split
      dtype: string
    - name: subcategories
      dtype: string
    - name: lang
      dtype: string
    - name: second_lang
      dtype: string
    - name: coding_lang
      dtype: string
    - name: notes
      dtype: string
    - name: id
      dtype: string
    - name: set_id
      dtype: float64
    - name: variation_id
      dtype: string
  splits:
    - name: test
      num_bytes: 23057
      num_examples: 107
  download_size: 13108
  dataset_size: 23057
configs:
  - config_name: social_media_informal_text
    data_files:
      - split: test
        path: social_media_informal_text/test-*

Dataset Card for Tokenization Robustness

A comprehensive evaluation dataset for testing robustness of different tokenization strategies.

Dataset Details

Dataset Description

This dataset evaluates how robust language models are to different tokenization strategies and edge cases. It includes questions with multiple choice answers designed to test various aspects of tokenization handling.

Curated by: R3
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: cc

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Uses

Direct Use

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Out-of-Scope Use

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Dataset Structure

The dataset contains multiple-choice questions with associated metadata about tokenization types and categories.

Dataset Creation

Curation Rationale

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Source Data

Data Collection and Processing

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Annotation process

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Personal and Sensitive Information

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Bias, Risks, and Limitations

The dataset focuses primarily on English text and may not generalize to other languages or tokenization schemes not covered in the evaluation.

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

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