README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: language
      dtype: string
    - name: question
      dtype: string
    - name: choices
      struct:
        - name: text
          sequence: string
        - name: label
          sequence: string
    - name: answerKey
      dtype: string
  splits:
    - name: train
      num_bytes: 357825
      num_examples: 1119
    - name: validation
      num_bytes: 98118
      num_examples: 299
    - name: test
      num_bytes: 382265
      num_examples: 1172
  download_size: 433794
  dataset_size: 838208
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: apache-2.0
language:
  - sw

Dataset Card for ARC_Challenge_Swahili

Dataset Summary

ARC_Challenge_Swahili is a Swahili translation of the original English ARC (AI2 Reasoning Challenge) dataset. This dataset evaluates the ability of AI systems to answer grade-school level multiple-choice science questions. The Swahili version was created using a combination of machine translation and human annotation to ensure high-quality and accurate translations.

Translation Methodology

The translation process for the ARC_Challenge_Swahili dataset involved two main stages:

Machine Translation:

1. The initial translation from English to Swahili was performed using the SeamlessM4TModel translation model.

• The following parameters were used for the translation:

inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=1024).to(device)
outputs = model.generate(**inputs, tgt_lang=dest_lang)
translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)

2. Human Verification and Annotation:

• After the initial machine translation, the translations were passed through GPT-3.5 for verification. This step involved checking the quality of the translations and identifying any that were not up to standard.
• Human translators reviewed and annotated the translations flagged by GPT-3.5 as problematic to ensure accuracy and naturalness in Swahili.

Supported Tasks and Leaderboards

• multiple-choice: The dataset supports multiple-choice question-answering tasks.

Languages

The dataset is in Swahili.

Dataset Structure

Data Instances

• An example of a data instance:

{
  "id": "example-id",
  "language": "sw",
  "question": "Ni gani kati ya zifuatazo ni sehemu ya mmea?",
  "choices": [
    {"text": "Majani", "Jiwe", "Ubao", "Nondo"},
    {"label": "A", "B": "C", "D"},
  ],
  "answerKey": "A"
}

Data Fields

• id: Unique identifier for each question.
• language: The language of the question is Swahili (sw).
• question: The science question in Swahili.
• Choices: There are multiple-choice options, each with text and label.
• answerKey: The correct answer for each question.

Datasplit

Split	Num Rows
train	1119
validation	299
test	1172