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[ "<thought>\nThe sentence is mid-construct, with the phrase \"compute the\" indicating that a technical term or expression is about to be introduced, likely related to the model's output or loss function. Given the context of gradient boosting and training dynamics, the next tokens will likely be \"gradient\" or \"l...
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[ "<thought>\nThe text is mid-sentence, concluding a clause about the advantage of using boosting ensembles like LightGBM and XGBoost. The next few tokens will likely elaborate on this advantage, possibly mentioning efficiency, performance, or scalability — terms commonly associated with these models. The next 8 toke...
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[ "<thought>\nThe sentence is mid-definition, specifying the model's predicted score for classes of each instance $j$ given input $x_j$. The notation $p^*_{(i)}(y^*_j|x_j)$ is being introduced, with $i$ indexing the iteration and $y^*_j$ likely denoting the true class label. The immediate next tokens will almost cert...
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[ "<thought>\nThe text is mid-sentence, having just stated that the method \"allows to assess only training instances\". The next few tokens will likely continue the sentence, introducing what comes after this assessment—likely a contrast with other methods, or an elaboration on the limitation or implication of this ...
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[ "<thought>\nThe cut occurs mid-equation, at the end of the line \"relative\". The next token is likely a space, followed by the mathematical object being referenced — most likely the predicted class probability or score. Given the preceding notation $p^*_{(i)}(y^*_j|x_j)$, the next content is expected to be the spe...
[ 0 ]
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[ "<thought>\nThe current equation for Correctness is just beginning, with the left-hand side defined as the percentage of iterations for which the model correctly labels $x_j$. The next 8 tokens will most likely introduce the mathematical notation for this correctness measure, starting with a variable such as $c_j$ ...
[ 0 ]
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[ "<thought>\nThe next 8 tokens will continue the mathematical expression for *Correctness*, starting with the summation over iterations $ i $, likely beginning with $ \\sum_{i=1}^{T} \\mathbb{I} $, where $ \\mathbb{I} $ is an indicator function for whether the predicted label matches the true label $ y^*_j $ at iter...
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[ "<thought>\nThe cut occurs mid-equation, right after the summation expression for $ c_j $, and the next token must be the closing parenthesis of the indicator function. Since the equation is not yet closed, the next few tokens will likely include the closing parenthesis, a semicolon or comma, and possibly a continu...
[ 0 ]
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[ "<thought>\nThe cut occurs mid-equation, specifically at the end of the expression $\\sum_{i$. The next 8 tokens must complete the summation, most likely as $\\sum_{i=1}^{T}$ to align with the notation used in the preceding equations for Confidence and Correctness. Following this, the equation for Variability is li...
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[ "<thought>\nThe text cuts off mid-sentence at the end of the algorithm caption line, with \"Dataset Cartography: Training Dynamic Metrics for GBD\" — the final word is incomplete. The next 8 tokens will almost certainly complete the word \"GBD\" as \"GBDT\" (Gradient Boosted Decision Tree), followed by a colon or c...
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[ "<thought>\nThe cut occurs mid-sentence in the algorithm description, at the end of the line \"\\REQUIRE Training set: $X\". The next token must be a comma or continuation of the tuple defining the training set, likely \"$X, Y$\", since $Y$ is the natural counterpart to $X$ in supervised learning contexts. The next...
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