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README.md
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@@ -295,9 +295,9 @@ There are three mathematics tasks. Each task is structured as a classification p
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```python
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from datasets import load_dataset
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### Algorithms
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- `id`: a `string` feature
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#### Maxflow
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#### Isomorphism
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### Games
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[More Information Needed]
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```python
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from datasets import load_dataset
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dataset_parity = load_dataset('isobench/IsoBench', 'math_parity', split='validation')
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dataset_convexity = load_dataset('isobench/IsoBench', 'math_convexity', split='validation')
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dataset_breakpoint = load_dataset('isobench/IsoBench', 'math_breakpoint', split='validation')
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```
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### Algorithms
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- `id`: a `string` feature
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#### Maxflow
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- `image`: a PIL Image feature
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- `source_node`: a `unit32` feature, denoting the index of the source node
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- `source_node_color`: a `string` feature, denoting the color of the `source_node` rendered in the `image`
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- `sink_node`: a `unit32` feature, denoting the index of the sink node
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- `sink_node_color`: a `string` feature, denoting the color of the `sink_node` rendered in the `image`
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- `adjacency_matrix`: a `string` feature, a string of an 2d array representing the adjacency matrix of a graph. The value in entry (i,j) denotes the capacity of flowing from node `i` to node `j`.
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- `label`: a `uint32` feature
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- `id`: a `string` feature
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#### Isomorphism
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- `image`: a PIL Image feature, consisting of two graphs `G` and `H`
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- `adjacency_matrix_G`: a `string` feature, a string of an 2d array representing the adjacency matrix of graph `G`
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- `adjacency_matrix_H`: a `string` feature, a string of an 2d array representing the adjacency matrix of graph `H`
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- `label`: a `bool` feature, with possible values including `True` (graphs `G` and `H` are isomorphic) and `False` (not isomorphic)
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- `id`: a `string` feature
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### Games
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[More Information Needed]
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