README.md

metadata

pretty_name: Samsoup GraphArch
configs:
  - config_name: full
    data_files:
      - split: train
        path: full/train.jsonl
      - split: validation
        path: full/validation.jsonl
      - split: test
        path: full/test.jsonl
  - config_name: subset_10k
    data_files:
      - split: train
        path: subset_10k/train.jsonl
      - split: validation
        path: subset_10k/validation.jsonl
      - split: test
        path: subset_10k/test.jsonl

GraphArch

This dataset is the MO-RELISH graph architecture regression collection. Each row contains serialized neural-network graph text and execution/benchmark measurements from neural architecture search spaces.

Configs And Splits

Config	Train	Validation	Test
full	475,257	10,000	10,000
subset_10k	10,000	1,000	1,000

The full config keeps all strict-clean rows, with 10,000 validation rows and 10,000 test rows sampled deterministically and stratified by search space. The subset_10k config is a deterministic 10,000/1,000/1,000 sample from the full splits, also stratified by search space.

Columns

Input columns:

• source_text: task-level context.
• input_text: serialized neural-network graph text.
• reference_outputs: empty list; this is a regression benchmark.
• prompt_components.problem_context: same task context as source_text.
• prompt_components.input_to_evaluate_column: points to input_text.
• prompt_components.input_format: serialized neural-network graph text.
• prompt_components.search_space: NAS search-space name.

Prediction targets:

• targets.val_accuracy: Validation accuracy reported for the neural architecture.
• targets.flops: Floating-point operation count for the architecture.

Retained measurements:

• measurements.val_accuracy: Validation accuracy reported for the neural architecture.
• measurements.flops: Floating-point operation count for the architecture.
• measurements.params: Number of trainable parameters in the architecture.

params is retained for analysis but is not a default prediction target because it is usually deterministic from the architecture.

The original zero-cost-proxy metadata is not included in rows because it may leak target values such as FLOPs, parameter count, or validation accuracy.

Loading

from datasets import load_dataset

full_ds = load_dataset("Samsoup/GraphArch", "full")
small_ds = load_dataset("Samsoup/GraphArch", "subset_10k")