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build_tokenizer

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

build_tokenizer appears in the code Syntology harvested for 19 papers, as 21 distinct code bodies found in 21 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 build_tokenizer 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 4 of the 21 distinct code bodies named build_tokenizer; 17 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
3ran
17unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 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; 3 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
Don't Retrain-Align: Adapting Autoregressive LMs to Diffusion LMs via Representation Alignment added by Syntology 2026-05 (from id) pengzhangzhi/Open-dLLM/veomni/models/auto.py 7c6cb8d3e6d5a33b unverified Apache-2.0 (permissive)
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching 31 Oct 2024 JZCS2018/SMAT/data_utils.py 49fe481db3ed5ef0 unverified no licence file found · pointer only
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language Modeling 9 Sep 2024 gpantaz/vl_mamba/src/vl_mamba/train_hf.py b6f70d524bb042b8 ran MIT (permissive)
Shaking Up VLMs: Comparing Transformers and Structured State Space Models for Vision & Language Modeling 9 Sep 2024 gpantaz/vl_mamba/src/vl_mamba/evaluation/base_evaluator.py 6f151d425fafa70f ran MIT (permissive)
Theory, Analysis, and Best Practices for Sigmoid Self-Attention 6 Sep 2024 apple/ml-sigmoid-attention/attention_simulator/examples/language_modeling/train_autoregressive_language_model.py f4ff8259db7ed4ff ran · our draft was wrong licence not identified · pointer only
Critique-out-Loud Reward Models 21 Aug 2024 zankner/cloud/cloud/train/utils.py 2ac2186c8e723169 unverified licence not identified · pointer only
Hydra: Bidirectional State Space Models Through Generalized Matrix Mixers 13 Jul 2024 goombalab/hydra/hydra/bert/src/text_data.py 9d30f2c46fd5058c ran MIT (permissive)
CroissantLLM: A Truly Bilingual French-English Language Model 1 Feb 2024 manuelfay/llm-data-hub/dataset_construction/fit_tokenizer.py 2acdc362a8126567 unverified no licence file found · pointer only
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks 1 Oct 2023 TIGER-AI-Lab/TIGERScore/tigerscore/candidates_generation/model_utils.py 200483ce1173890a unverified MIT (permissive)
HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models 27 Sep 2023 hypotheses-paradise/hypo2trans/generate_data/whisper/whisper/tokenizer.py f475a7a8460e2abc unverified MIT (permissive)
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion 5 Jun 2023 yuchenlin/LLM-Blender/llm_blender/candidates_generation/model_utils.py 9e63c7f428bcf976 unverified Apache-2.0 (permissive)
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion 5 Jun 2023 yuchenlin/LLM-Blender/llm_blender/pair_ranker/model_util.py dea0c7d7d27028b1 unverified Apache-2.0 (permissive)
Robust Speech Recognition via Large-Scale Weak Supervision 6 Dec 2022 briansidp/whisperbiasing/whisper/tokenizer.py acb6a55d43c7f465 unverified MIT recorded; this copy not marked cleared · pointer only
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions? 27 Oct 2022 hritikbansal/entigen_emnlp/models/minDALL-E/dalle/models/tokenizer.py 0d23c31b80ba171d unverified MIT (permissive)
Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees 2 Jun 2022 DS3Lab/AC-SGD/modules/tokenizer.py b0c8736d6680465b unverified MIT (permissive)
K-LITE: Learning Transferable Visual Models with External Knowledge 20 Apr 2022 microsoft/klite/model/model.py d7ee228866431711 unverified MIT (permissive)
Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup 15 Sep 2021 songyouwei/ABSA-PyTorch/data_utils.py b97da16eaf330fad unverified MIT (permissive)
Effective LSTMs for Target-Dependent Sentiment Classification 3 Dec 2015 hiyouga/PBAN-PyTorch/data_utils.py 3477796acf72175a unverified MIT (permissive)
arXiv:aaai_6517 hiyouga/RepWalk/data_utils.py a5a2f8b523c09f5d unverified MIT (permissive)
arXiv:aaai_29911 shuoyinn/TextGT/data_utils.py bcd67ca3bd9006fa unverified MIT (permissive)
arXiv:2025.findings-acl.1207 EIT-NLP/StreamingLLM/StreamingThinker/finetune_streaming.py 263c52767281b14d unverified Apache-2.0 (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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