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@@ -53,7 +53,7 @@ The translation process for the ARC_Challenge_Swahili dataset involved two main
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  ### Machine Translation:
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  1. The initial translation from English to Swahili was performed using the SeamlessM4TModel translation model.
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- The following parameters were used for the translation:
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  ```python
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  inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=1024).to(device)
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  outputs = model.generate(**inputs, tgt_lang=dest_lang)
@@ -62,5 +62,36 @@ translation = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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  2. Human Verification and Annotation:
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- 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.
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- The translations flagged by GPT-3.5 as problematic were reviewed and annotated by human translators to ensure accuracy and naturalness in Swahili.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Machine Translation:
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  1. The initial translation from English to Swahili was performed using the SeamlessM4TModel translation model.
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+ * The following parameters were used for the translation:
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  ```python
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  inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=1024).to(device)
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  outputs = model.generate(**inputs, tgt_lang=dest_lang)
 
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  2. Human Verification and Annotation:
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+ * 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.
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+ * Human translators reviewed and annotated the translations flagged by GPT-3.5 as problematic to ensure accuracy and naturalness in Swahili.
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+
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+ ## Supported Tasks and Leaderboards
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+ * multiple-choice: The dataset supports multiple-choice question-answering tasks.
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+
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+ ## Languages
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+ The dataset is in Swahili.
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+
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+ ## Dataset Structure
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+ ### Data Instances
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+ * An example of a data instance:
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+ ```json
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+ {
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+ "id": "example-id",
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+ "language": "sw",
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+ "question": "Ni gani kati ya zifuatazo ni sehemu ya mmea?",
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+ "choices": [
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+ {"text": "Majani", "label": "A"},
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+ {"text": "Jiwe", "label": "B"},
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+ {"text": "Ubao", "label": "C"},
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+ {"text": "Nondo", "label": "D"}
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+ ],
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+ "answerKey": "A"
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+ }
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+ ```
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+
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+ ### Data Fields
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+ * id: Unique identifier for each question.
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+ * language: The language of the question is Swahili (sw).
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+ * question: The science question in Swahili.
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+ * Choices: There are multiple choice options, each with text and label.
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+ * answerKey: The correct answer for each question.