metadata

license: cc
multilinguality: multilingual
task_categories:
  - multiple-choice
pretty_name: Tokenization Robustness
tags:
  - multilingual
  - tokenization
configs:
  - config_name: code_technical_content
    data_files:
      - split: dev
        path: code_technical_content/dev-*
      - split: test
        path: code_technical_content/test-*
dataset_info:
  config_name: code_technical_content
  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
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: dev
      num_bytes: 1424
      num_examples: 4
    - name: test
      num_bytes: 34450
      num_examples: 90
  download_size: 20916
  dataset_size: 35874

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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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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