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create_batch

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

create_batch appears in the code Syntology harvested for 12 papers, as 11 distinct code bodies found in 12 places (a place is one code body under one paper). At least one of them ran in 2 of the papers; 1 of the code bodies carries 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 create_batch 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 11 distinct code bodies named create_batch; 9 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
1ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
1ran
9unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 12 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

12 papers shown of 12, newest first; 12 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. 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
Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings 9 Dec 2024 zhaoliu0914/LLM-Bias-Benchmark/OpenAI_API.py 99eb6c1b26fcc9ae unverified no licence file found · pointer only
MTL-LoRA: Low-Rank Adaptation for Multi-Task Learning 12 Oct 2024 pumpkin-co/mtl-lora/mlora_evaluate.py dcba3394053b84cd ran MIT (permissive)
PolyFormer: Scalable Node-wise Filters via Polynomial Graph Transformer 19 Jul 2024 air029/polyformer/node_classification_large_graph/training_batch.py c92a4ce1dc31074e unverified no licence file found · pointer only
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation 8 Oct 2022 raghul-parthipan/dont_waste_data/KS/helper.py 1d1a44a5a1e1e848 unverified MIT (permissive)
Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model 28 Mar 2022 raghul-parthipan/l96_rnn/saved_models/rnn/models_for_full_dataset/helper.py 1d1a44a5a1e1e848 unverified MIT (permissive)
Exploiting a Zoo of Checkpoints for Unseen Tasks 5 Nov 2021 baidu-research/task_space/LMs/data.py de1977f4d2b883e3 unverified MIT (permissive)
Spectral Clustering with Graph Neural Networks for Graph Pooling 30 Jun 2019 FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling/Graph_Classification.py 0de9bc2db1425653 unverified MIT (permissive)
A Direct Approach to Robust Deep Learning Using Adversarial Networks 23 May 2019 whxbergkamp/RobustDL_GAN/cifar10/adversarial_networks/inputs.py aba11452314b3900 unverified MIT (permissive)
Human Motion Analysis with Deep Metric Learning 30 Jul 2018 dhesenkamp/attentive-lstm/util.py dbe995781db97e28 unverified MIT (permissive)
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering 12 Jun 2018 lanwuwei/SPM_toolkit/DecAtt/main_mnli.py 2468a208d1b1a074 unverified no licence file found · pointer only
End-to-End Speech-Driven Facial Animation with Temporal GANs 23 May 2018 PrashanthaTP/wav2mov/wav2mov/inference/generate.py c1cc5ac84d39c69f ran · violated contract fingerprinted no licence file found · pointer only
Show and Tell: A Neural Image Caption Generator 17 Nov 2014 guptakhil12/show-tell/utils.py ed75cf97c296beab 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections