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get_split

Syntologyentry name in harvested coderead from the graph 2026-09-24

get_split appears in the code Syntology harvested for 17 papers, as 16 distinct code bodies found in 19 places (a place is one code body under one paper). At least one of them ran in 3 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named get_split do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 2 of the 16 distinct code bodies named get_split; 14 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
2ran
14unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 19 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

17 papers shown of 17, newest first; 19 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 3 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance added by Syntology 2026-06 (from id) hsdslab/biomedical-causal-inference/src/Analgesics-induced_acute_liver_failure/albert_train_src.py 87e47297b4c3db5c unverified no licence file found · pointer only
The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance added by Syntology 2026-06 (from id) hsdslab/biomedical-causal-inference/src/Analgesics-induced_acute_liver_failure/biobert_llm_train_src.py 54c8f407d8ba3697 unverified no licence file found · pointer only
Heterophily-Agnostic Hypergraph Neural Networks with Riemannian Local Exchanger added by Syntology 2026-03 (from id) Mingzhang21/HealHGNN/HealHGNN/utils/data_utils.py 373a389551605855 unverified MIT (permissive)
Deeper with Riemannian Geometry: Overcoming Oversmoothing and Oversquashing for Graph Foundation Models added by Syntology 2025-10 (from id) ZhenhHuang/GBN/utils/data_utils.py 373a389551605855 unverified no licence file found · pointer only
Agent Workflow Memory 11 Sep 2024 zorazrw/agent-workflow-memory/mind2web/offline_induction.py 1c21d96a59bbdec1 unverified Apache-2.0 (permissive)
Unifying Causal Representation Learning with the Invariance Principle 4 Sep 2024 causallearningai/istant/src/data.py a1679135216baa49 ran MIT (permissive)
Optimal Sparse Survival Trees 27 Jan 2024 ruizhang1996/optimal-sparse-survival-trees-public/osst/model/imbalance/osdt_imb_v9.py 3124c55e58633650 ran licence not identified · pointer only
Learning to Extrapolate: A Transductive Approach 27 Apr 2023 learningmatter-mit/matex/baselines/modnet/aflow_mp.py a26982da6e0216ef unverified MIT (permissive)
A Closer Look at Invariances in Self-supervised Pre-training for 3D Vision 11 Jul 2022 lilanxiao/invar3d/scannet/sampler.py a93412e4a677be68 unverified MIT (permissive)
Home Action Genome: Cooperative Compositional Action Understanding 11 May 2021 nishantrai18/homage/process_data/src/write_csv.py 9075ce1ec911ec14 unverified MIT (permissive)
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks 3 May 2021 OneForward/UniGNN/train_evolving.py b3b69434e62c6fb8 unverified MIT (permissive)
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks 3 May 2021 OneForward/UniGNN/train_val.py b965596d50610d7a unverified MIT (permissive)
MEG: Generating Molecular Counterfactual Explanations for Deep Graph Networks 16 Apr 2021 danilonumeroso/MEG/utils/data.py d26022a6dfd1f791 unverified Apache-2.0 (permissive)
Memory-augmented Dense Predictive Coding for Video Representation Learning 3 Aug 2020 TengdaHan/MemDPC/process_data/src/write_csv.py 488a3f6281af3479 unverified Apache-2.0 (permissive)
Generalized and Scalable Optimal Sparse Decision Trees 15 Jun 2020 zhichen96/interpretable_ml_metamaterials/gosdt/osdt_imb_v9.py 3124c55e58633650 ran MIT (permissive)
Video Representation Learning by Dense Predictive Coding 10 Sep 2019 TengdaHan/DPC/process_data/src/write_csv.py 488a3f6281af3479 unverified MIT (permissive)
Protecting Neural Networks with Hierarchical Random Switching: Towards Better Robustness-Accuracy Trade-off for Stochastic Defenses 20 Aug 2019 KieranXWang/HRS/block_split_config.py b595c3277f371195 unverified MIT (permissive)
Unifying and Merging Well-trained Deep Neural Networks for Inference Stage 14 May 2018 ivclab/NeuralMerger/Fine-tuning/datasets/gender.py 35318fc2899ca955 unverified MIT (permissive)
arXiv:aaai_25573 yixinliu233/GREET/data_loader.py cfb46fb9c212aa87 unverified MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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