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tokenize_function

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

tokenize_function appears in the code Syntology harvested for 19 papers, as 19 distinct code bodies found in 21 places (a place is one code body under one paper). At least one of them ran in 10 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 tokenize_function 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 10 of the 19 distinct code bodies named tokenize_function; 9 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
4ran · our draft was wrong
0ran · fixture could not drive it
6ran
9unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 6 of the 21 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

19 papers shown of 19, newest first; 21 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 1 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
High-quality data augmentation for code comment classification added by Syntology 2026-01 (from id) ThomBors/NLBSE2026/src/utils.py 96aa26edf0c56f70 unverified MIT (permissive)
Self-Training Elicits Concise Reasoning in Large Language Models 27 Feb 2025 tergelmunkhbat/concise-reasoning/src/training_utils.py 0b6eb4d9aa2a6729 unverified MIT (permissive)
Householder Pseudo-Rotation: A Novel Approach to Activation Editing in LLMs with Direction-Magnitude Perspective 16 Sep 2024 VinAIResearch/HPR/activation_editing/get_activations.py b2a2c7262c9af473 ran BSD-3-Clause (permissive)
A Psychology-based Unified Dynamic Framework for Curriculum Learning 9 Aug 2024 nd-ball/cl-irt/PUDF_GLUE/PUDF_glue_DebertaV3.py 3dc66ac0e9e83724 ran · our draft was wrong MIT (permissive)
A Psychology-based Unified Dynamic Framework for Curriculum Learning 9 Aug 2024 nd-ball/cl-irt/gen_difficulty/GLUE_difficulty.py c19241ab56039094 ran · our draft was wrong MIT (permissive)
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection 8 Aug 2024 mbzuai-nlp/llm-detectaive/pipeline/model_pipeline.py dbc9d07c6c1f86f7 ran no licence file found · pointer only
ReSi: A Comprehensive Benchmark for Representational Similarity Measures 1 Aug 2024 mklabunde/resi/nlp/bert_finetune.py aa6161b70c72b969 ran CC-BY-4.0 · pointer only
Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models 15 Jul 2024 qcznlp/uncertainty_attack/fine_tuning.py 6b4ff6a70b9692b6 ran · our draft was wrong no licence file found · pointer only
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling 9 Jun 2024 QizhiPei/3D-MolT5/3d_molt5/utils/copied_utils.py 8a721263c3f886fd ran Apache-2.0 (permissive)
Few-Shot Detection of Machine-Generated Text using Style Representations 12 Jan 2024 llnl/luar/fewshot_iclr2024/baseline_training/finetune_roberta.py a3e19197e69c1a89 ran Apache-2.0 (permissive)
The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models 1 Dec 2023 namburisrinath/llmcompression/bert_prune.py ca23fc05f2adc8a8 unverified no licence file found · pointer only
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems 16 Nov 2023 stanford-futuredata/ares/ares/LLM_as_a_Judge_Adaptation/General_Binary_Classifier.py 042e539c6d8bd3de ran Apache-2.0 (permissive)
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models 20 Oct 2023 rickyskywalker/synthesis_step-by-step_official/ModelTraining/main_IMDb.py c01766547414e4b9 unverified no licence file found · pointer only
nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources 5 Sep 2023 piotrnawrot/nanot5/nanoT5/utils/copied_utils.py 8a721263c3f886fd ran Apache-2.0 (permissive)
Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models 17 May 2023 BunsenFeng/Knowledge_Card/card_training.py add928626b7e8654 unverified MIT (permissive)
Predicting Fine-Tuning Performance with Probing 13 Oct 2022 spoclab-ca/performance_prediction/scripts/glue_classify.py 6a5472e4dd828178 ran · our draft was wrong no licence file found · pointer only
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection 30 Jun 2022 bit-ml/anoshift/language_models/data_utils.py 184f64ac20617dd0 unverified BSD-3-Clause (permissive)
Secure Distributed Training at Scale 21 Jun 2021 yandex-research/btard/albert/experiments/tokenize_wikitext103.py d2b88e48b87e9450 unverified Apache-2.0 (permissive)
Distributed Deep Learning in Open Collaborations 18 Jun 2021 yandex-research/DeDLOC/albert/tokenize_wikitext103.py d2b88e48b87e9450 unverified Apache-2.0 (permissive)
arXiv:2024.findings-emnlp.286 kgarg8/Stanceformer/train_automodel2.py 22184da3623db21e unverified MIT (permissive)
arXiv:2024.findings-emnlp.286 kgarg8/Stanceformer/ABSA/train_automodel2.py 28e1b38faf4dd119 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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