php
Datasets
All datasets matching “php”SWE-smith-phpPHP-Code-LargePHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code… See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/PHP-Code-Large.PHP-Code-LargePHP-Code-Large
PHP-Code-Large is a large-scale corpus of PHP source code comprising more than 12 million lines of PHP code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the PHP ecosystem.
By providing a high-volume, language-specific corpus, PHP-Code-Large enables systematic experimentation in PHP-focused model training, domain adaptation, and downstream code… See the full description on the dataset page: https://huggingface.co/datasets/xormania/PHP-Code-Large.php_cat1ph-pretrain
PH Pretrain — Philippine Languages Corpus (v0.6-ph-unified)
👉 Looking to train a Filipino/Tagalog model? Use jpaulpoliquit/ph-pretrain-03 instead — it is the recommended dataset. It is a ~1B-token, Tagalog-forward, quality-filtered web corpus. This dataset (ph-pretrain) is ~95% bot-templated Cebuano/Waray Wikipedia and is best only when you specifically want mass regional-language (Cebuano/Waray) coverage.
A cleaned, deduplicated, document-level pretraining corpus for… See the full description on the dataset page: https://huggingface.co/datasets/jpaulpoliquit/ph-pretrain.cornstack_php_ru_enThe part of CoRNStack Dataset translated into Russian. Translation was done with Qwen3 model.
Samples that satisfy the dual consistency filtering condition (samples where the document_rank is 0 or 1 and document_score > 0.7) were translated.
Source code you can find here. For support: fedor.yaronskiy@gmail.com