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README.md
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@@ -35,7 +35,7 @@ Repository: **[Github code for SFT Fine-tuning on MathDial](https://github.com/e
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Training input and output:
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The model was fine-tuned on the **[MathDial dataset](https://huggingface.co/datasets/eth-nlped/mathdial-chat/viewer/default/train?views%5B%5D=train&row=0)**.
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Each training example consisted of a **Instruction**, **Student's Name**, **Math Word Problem and Solution
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To incorporate the whole conversation, a sliding window approach was used. Every input has the same format:
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For each step in a conversation, the model input included **all previous turns** in the dialogue (sliding window), followed by the student’s next message. The model’s output was then the **next tutor response** from the dataset.
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This approach ensures the model learns to generate responses that are context-aware.
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Training input and output:
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The model was fine-tuned on the **[MathDial dataset](https://huggingface.co/datasets/eth-nlped/mathdial-chat/viewer/default/train?views%5B%5D=train&row=0)**.
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Each training example consisted of a **Instruction**, **Student's Name**, **Math Word Problem and Solution** and **The students initial approach** as input, followed by the **tutor’s step-by-step solution** as the target output.
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To incorporate the whole conversation, a sliding window approach was used. Every input has the same format:
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For each step in a conversation, the model input included **all previous turns** in the dialogue (sliding window), followed by the student’s next message. The model’s output was then the **next tutor response** from the dataset.
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This approach ensures the model learns to generate responses that are context-aware.
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