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anrilombard/mzansi-text

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Hugging Face2026-03-25 更新2026-03-29 收录
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--- language: - af - en - nso - sot - ssw - tsn - tso - ven - xho - zul - nbl tags: - pretraining - south-african-languages - multilingual - mzansitext license: apache-2.0 --- # MzansiText **MzansiText** is a curated multilingual pretraining corpus for all eleven official South African languages. [![GitHub](https://img.shields.io/badge/GitHub-Anri--Lombard/sallm-blue)](https://github.com/Anri-Lombard/sallm) [![Paper](https://img.shields.io/badge/Paper-arXiv_2603.20732-red.svg)](https://arxiv.org/abs/2603.20732) [![Model](https://img.shields.io/badge/Model-MzansiLM_125M-green)](https://huggingface.co/anrilombard/mzansilm-125m) [![Collection](https://img.shields.io/badge/Collection-MzansiLM-orange)](https://huggingface.co/collections/anrilombard/mzansilm-69635ca7b60efedb9dfcb09e) ## Dataset Details - Languages: `af`, `en`, `nso`, `sot`, `ssw`, `tsn`, `tso`, `ven`, `xho`, `zul`, `nbl` - Schema: ```json { "text": "string", "lang": "string" } ``` - This repository contains the raw train, validation, and test text splits used for the MzansiLM pretraining release. - The token distribution table below matches the paper-reported corpus statistics. ### Token Distribution (after filtering + 65,536-vocab BPE tokenizer) | Language | Train Tokens | % | Val Tokens | Test Tokens | |---|---:|---:|---:|---:| | Afrikaans | 2,475,913,822 | 64.96 | 1,865,255 | 1,875,605 | | English | 740,994,679 | 19.44 | 1,813,651 | 1,821,803 | | isiZulu | 320,224,015 | 8.40 | 2,017,406 | 2,021,343 | | isiXhosa | 152,212,403 | 3.99 | 2,016,503 | 2,012,000 | | Sesotho | 97,558,939 | 2.56 | 2,315,298 | 2,316,170 | | Setswana | 10,082,930 | 0.26 | 1,216,539 | 1,413,473 | | Sepedi | 6,697,358 | 0.18 | 685,425 | 778,656 | | Xitsonga | 3,013,408 | 0.08 | 510,463 | 319,496 | | siSwati | 1,932,989 | 0.05 | 196,247 | 225,810 | | Tshivenda | 1,852,481 | 0.05 | 191,495 | 243,315 | | isiNdebele | 818,549 | 0.02 | 106,224 | 143,458 | | **Total** | **3,811,301,573** | **100** | **12,934,506** | **13,171,129** | Validation and test sets are capped at approximately 2M tokens per language to prevent high-resource languages from dominating early stopping. ## Usage ```python from datasets import load_dataset ds = load_dataset("anrilombard/mzansi-text", split="train") print(ds[0]) ``` ## Related Releases - Paper: [arXiv:2603.20732](https://arxiv.org/abs/2603.20732) - Model: [anrilombard/mzansilm-125m](https://huggingface.co/anrilombard/mzansilm-125m) - Tokenized corpus: [anrilombard/mzansi-text-tokenized](https://huggingface.co/datasets/anrilombard/mzansi-text-tokenized) - GitHub code and configs: [https://github.com/Anri-Lombard/sallm](https://github.com/Anri-Lombard/sallm) Full preprocessing pipeline (including this exact cleaning script) is in [`data/cleaning/`](https://github.com/Anri-Lombard/sallm/tree/main/data/cleaning) on GitHub. ## Citation Please cite the paper: ```bibtex @misc{lombard2026mzansitextmzansilmopencorpus, title={MzansiText and MzansiLM: An Open Corpus and Decoder-Only Language Model for South African Languages}, author={Anri Lombard and Simbarashe Mawere and Temi Aina and Ethan Wolff and Sbonelo Gumede and Elan Novick and Francois Meyer and Jan Buys}, year={2026}, eprint={2603.20732}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2603.20732}, } ``` ## License Apache License 2.0

language: - 南非荷兰语(af) - 英语(en) - 北索托语(nso) - 索托语(sot) - 斯瓦蒂语(ssw) - 茨瓦纳语(tsn) - 聪加语(tso) - 文达语(ven) - 科萨语(xho) - 祖鲁语(zul) - 恩德贝莱语(nbl) tags: - 预训练(pretraining) - 南非语言(south-african-languages) - 多语言(multilingual) - mzansitext license: apache-2.0 # MzansiText **MzansiText** 是一套经过精心甄选的多语言预训练语料库,涵盖南非全部11种官方语言。 [![GitHub仓库](https://img.shields.io/badge/GitHub-Anri--Lombard/sallm-blue)](https://github.com/Anri-Lombard/sallm) [![论文](https://img.shields.io/badge/Paper-arXiv_2603.20732-red.svg)](https://arxiv.org/abs/2603.20732) [![模型](https://img.shields.io/badge/Model-MzansiLM_125M-green)](https://huggingface.co/anrilombard/mzansilm-125m) [![数据集合集](https://img.shields.io/badge/Collection-MzansiLM-orange)](https://huggingface.co/collections/anrilombard/mzansilm-69635ca7b60efedb9dfcb09e) ## 数据集详情 - 支持语言:`af`(南非荷兰语)、`en`(英语)、`nso`(北索托语)、`sot`(索托语)、`ssw`(斯瓦蒂语)、`tsn`(茨瓦纳语)、`tso`(聪加语)、`ven`(文达语)、`xho`(科萨语)、`zul`(祖鲁语)、`nbl`(恩德贝莱语) - 数据格式(Schema): json { "text": "string", "lang": "string" } 本仓库包含用于MzansiLM预训练发布的原始训练、验证与测试文本划分集。下文的分词分布表与论文中报告的语料库统计数据一致。 ### 分词分布(经过滤处理 + 65536词表的字节对编码(BPE)分词器) | 语言 | 训练分词数 | 占比 | 验证分词数 | 测试分词数 | |---|---:|---:|---:|---:| | 南非荷兰语(Afrikaans) | 2,475,913,822 | 64.96 | 1,865,255 | 1,875,605 | | 英语(English) | 740,994,679 | 19.44 | 1,813,651 | 1,821,803 | | 祖鲁语(isiZulu) | 320,224,015 | 8.40 | 2,017,406 | 2,021,343 | | 科萨语(isiXhosa) | 152,212,403 | 3.99 | 2,016,503 | 2,012,000 | | 索托语(Sesotho) | 97,558,939 | 2.56 | 2,315,298 | 2,316,170 | | 茨瓦纳语(Setswana) | 10,082,930 | 0.26 | 1,216,539 | 1,413,473 | | 北索托语(Sepedi) | 6,697,358 | 0.18 | 685,425 | 778,656 | | 聪加语(Xitsonga) | 3,013,408 | 0.08 | 510,463 | 319,496 | | 斯瓦蒂语(siSwati) | 1,932,989 | 0.05 | 196,247 | 225,810 | | 文达语(Tshivenda) | 1,852,481 | 0.05 | 191,495 | 243,315 | | 恩德贝莱语(isiNdebele) | 818,549 | 0.02 | 106,224 | 143,458 | | **总计** | **3,811,301,573** | **100** | **12,934,506** | **13,171,129** | 验证集与测试集的每语言分词数均限制在约200万,以避免高资源语言主导早停过程。 ## 使用方法 python from datasets import load_dataset ds = load_dataset("anrilombard/mzansi-text", split="train") print(ds[0]) ## 相关发布 - 论文:[arXiv:2603.20732](https://arxiv.org/abs/2603.20732) - 模型:[anrilombard/mzansilm-125m](https://huggingface.co/anrilombard/mzansilm-125m) - 分词后语料库:[anrilombard/mzansi-text-tokenized](https://huggingface.co/datasets/anrilombard/mzansi-text-tokenized) - GitHub代码与配置:[https://github.com/Anri-Lombard/sallm](https://github.com/Anri-Lombard/sallm) 完整的预处理流水线(包含本数据集所用的清洗脚本)可在GitHub仓库的[`data/cleaning/`](https://github.com/Anri-Lombard/sallm/tree/main/data/cleaning)路径中获取。 ## 引用方式 请引用以下论文: bibtex @misc{lombard2026mzansitextmzansilmopencorpus, title={"MzansiText与MzansiLM:面向南非语言的开源语料库与仅解码器语言模型"}, author={Anri Lombard and Simbarashe Mawere and Temi Aina and Ethan Wolff and Sbonelo Gumede and Elan Novick and Francois Meyer and Jan Buys}, year={2026}, eprint={2603.20732}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2603.20732}, } ## 许可证 Apache许可证2.0
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