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@@ -32,11 +32,9 @@ Built with [PyTorch Lightning](https://www.pytorchlightning.ai/), this implement
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  Each input sample is formatted as follows:
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  ```
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- truefalse: [MASK or answer] passage: [passage] </s>
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  ```
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- During training, the answer is occasionally replaced with the `[MASK]` token (controlled by a defined masking probability). This strategy encourages the model to learn both to predict the answer and to generate a corresponding question.
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-
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  ### Target Construction
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  Each target sample is formatted as:
@@ -68,8 +66,6 @@ The model’s performance was evaluated using BLEU scores for both the generated
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  | BLEU-3 | 0.3089 |
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  | BLEU-4 | 0.2431 |
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- Additionally, the answer generation performance is notably strong, achieving a **BLEU-1 score of 90**.
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-
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  *Note: These metrics offer a quantitative assessment of the model’s quality in generating coherent and relevant question-answer pairs.*
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  ## How to Use
@@ -82,9 +78,7 @@ from transformers import pipeline
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  generator = pipeline("text2text-generation", model="Fares7elsadek/boolq-t5-base-question-generation")
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  # Example inference:
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- input_text = "truefalse: [MASK] passage: [Your passage here] </s>"
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- # Alternatively, specify an answer directly:
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- # input_text = "truefalse: yes passage: [Your passage here] </s>"
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  result = generator(input_text)
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  print(result)
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  ```
 
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  Each input sample is formatted as follows:
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  ```
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+ truefalse: [answer] passage: [passage] </s>
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  ```
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  ### Target Construction
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  Each target sample is formatted as:
 
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  | BLEU-3 | 0.3089 |
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  | BLEU-4 | 0.2431 |
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  *Note: These metrics offer a quantitative assessment of the model’s quality in generating coherent and relevant question-answer pairs.*
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  ## How to Use
 
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  generator = pipeline("text2text-generation", model="Fares7elsadek/boolq-t5-base-question-generation")
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  # Example inference:
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+ input_text = "truefalse: [answer] passage: [Your passage here] </s>"
 
 
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  result = generator(input_text)
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  print(result)
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  ```