UnibQuAD / README.md
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metadata
pretty_name: SQuAD-UNIB
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
  - found
language:
  - id
license:
  - cc-by-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - extended|wikipedia
task_categories:
  - question-answering
task_ids:
  - extractive-qa
paperswithcode_id: squad-unib
train-eval-index:
  - config: plain_text
    task: question-answering
    task_id: extractive_question_answering
    splits:
      train_split: train
      eval_split: validation
    col_mapping:
      question: question
      context: context
      answers:
        text: text
        answer_start: answer_start
    metrics:
      - type: squad
        name: SQuAD
dataset_info:
  features:
    - name: id
      dtype: string
    - name: title
      dtype: string
    - name: context
      dtype: string
    - name: question
      dtype: string
    - name: answers
      sequence:
        - name: text
          dtype: string
        - name: answer_start
          dtype: int32
  config_name: plain_text

Dataset Card for [Squad-UNIB]

Table of Contents

Dataset Description

  • Homepage:
  • Repository:
  • Paper:
  • Leaderboard:
  • Point of Contact:

Dataset Summary

This dataset create by own colecting for NLP task individual

Supported Tasks and Leaderboards

[More Information Needed]

Languages

[More Information Needed]

Dataset Structure

Data Instances

An example of 'train' looks as follows.

{
    "answers": {
        "answer_start": [1],
        "text": ["This is a test text"]
    },
    "context": "This is a test context.",
    "id": "1",
    "question": "Is this a test?",
    "title": "train test"
}

Data Fields

The data fields are the same among all splits.

plain_text

  • id: a string feature.
  • title: a string feature.
  • context: a string feature.
  • question: a string feature.
  • answers: a dictionary feature containing:
    • text: a string feature.
    • answer_start: a int32 feature.

Data Splits

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

Citation Information

[More Information Needed]

Contributions

Thanks to @github-username for adding this dataset.