datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
irish_fineweb_eduData translation project of https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu, sample-10BT subset. Data are translated from English to Irish using NLLB-3.3B.
fineweb-linuxlikefineweb-nanochatbpe-100M
fineweb-nanochatbpe-100M
FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
This is a 100-million-token slice for data-constrained experiments. The train.bin
is the byte-exact first 100,000,000 tokens (bytes [0, 200000000)) of the parent
alexkstern/fineweb-nanochatbpe-20B
train.bin. The val.bin is byte-identical to the parent's val.bin.… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-100M.fineweb-edu-zh-chengyu-cpt
Fineweb-Edu Chinese — Chengyu-Tagged Continued-Pretraining Corpus
A 3.74M-document Chinese corpus (~7.8B tokens) for continued pretraining on
cultural knowledge in figurative language, built from the highest-quality
tier of opencsg/Fineweb-Edu-Chinese-V2.1.
Each document is educational Chinese text containing at least one culturally
vetted chengyu, with an appended 【成语注释】 knowledge block listing every
matched idiom's figurative meaning(s) and classical source citation.
This is a… See the full description on the dataset page: https://huggingface.co/datasets/jiviteshjn/fineweb-edu-zh-chengyu-cpt.fineweb-nanochatbpe-20B
fineweb-nanochatbpe-20B
FineWeb-Edu (from karpathy/fineweb-edu-100b-shuffle), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536)
and packaged as flat uint16 token-id .bin files for fast memmap training.
file
split
tokens
train.bin
train
20,000,000,000
val.bin
val
52,336,096
train and val are disjoint held-out partitions. Each .bin is a raw
little-endian uint16 stream (no header); token count = filesize / 2, and
train.meta.json / val.meta.jsoncarry the full… See the full description on the dataset page: https://huggingface.co/datasets/alexkstern/fineweb-nanochatbpe-20B.fineweb-eduPrimus-FineWeb
PRIMUS: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
🤗 Primus-FineWeb
The Primus-FineWeb dataset is constructed by filtering cybersecurity-related text from FineWeb, a refined version of Common Crawl. We began by leveraging Primus-Seed, a high-quality dataset of manually curated cybersecurity text, as positive samples. We then sampled ten times the amount of data from FineWeb as negative samples and trained a binary cybersecurity… See the full description on the dataset page: https://huggingface.co/datasets/trendmicro-ailab/Primus-FineWeb.fineweb_edu_10B_for_crypto-LLMfineweb_2_samples_hq
fineweb_2_samples_hq
FINEWEB2-HQ dataset
Dataset Structure
This dataset contains 5 JSONL files with a total size of 26415.31 MB.
Files:
ukr_Cyrl_sample_001.jsonl: 6163.40 MB
ron_Latn_sample_001.jsonl: 3739.69 MB
kor_Hang_sample_001.jsonl: 4120.89 MB
hin_Deva_sample_001.jsonl: 6681.96 MB
heb_Hebr_sample_001.jsonl: 5709.37 MB
Usage
from datasets import load_dataset
dataset = load_dataset("path/to/this/dataset")
Loading specific files… See the full description on the dataset page: https://huggingface.co/datasets/Mayank6255/fineweb_2_samples_hq.fineweb-edu-climateFineweb-Edu-Chinese-V2.3
Chinese Fineweb Edu Dataset V2.3
中文 | English
OpenCSG 社区 | GitHub | 数据集许可协议
数据集简介
Chinese Fineweb Edu Dataset V2.3 是 OpenCSG 面向中文教育、知识问答、指令微调和文本生成场景构建的高质量中文教育 SFT 数据集。
该版本包含 23.04 万条高质量中文教育 QA pairs,并将同一批问答对发布为 Alpaca、Messages、Messages-no-system 三种训练格式。三种格式面向不同训练模板,建议训练时按模型和框架选择其中一种格式使用,而不是将不同格式简单相加作为独立知识规模。
V2.3 是在 V2.2 基础上的质量升级版本。针对 V2.2 社区反馈和内部质量审计中出现的重复模式、异常中英文混入、噪声片段、弱证据支撑回答和低质量合成输出等问题,V2.3 提高了源文本进入生成环节的门槛,并优化了问答生成与过滤逻辑。
在数据构建上,V2.3 从约 2.3T… See the full description on the dataset page: https://huggingface.co/datasets/opencsg/Fineweb-Edu-Chinese-V2.3.smollm-corpus-fineweb-edu-enPurified-openai-messages
📖 smollm-corpus-fineweb-edu-enPurified-openai-messages
smollm-corpus-fineweb-edu-enPurified is a highly curated, "prose-first" subset of the fineweb-edu-dedup subset found in HuggingFaceTB/smollm-corpus.
The enPurified collection is built on a specific philosophy: Specialization. While the original dataset is excellent for general pre-training, high-quality fluent English prose often gets diluted when mixed with syntax-heavy code, rigid math formulas, or low-information web junk.… See the full description on the dataset page: https://huggingface.co/datasets/enPurified/smollm-corpus-fineweb-edu-enPurified-openai-messages.fineweb-c-progressFineweb_2_500k_removedfineweb_bltfineweb-edu-v2-FastT
FineWeb-Edu v2 - FastText Climate Filtered
A climate and environment-focused subset of sraj/finewebedu-climate-v2, further filtered using a trained FastText binary classifier.
Overview
This dataset applies a supervised FastText climate classifier to the FineWeb-Edu climate v2 dataset. Each record includes a climate probability score from the classifier, providing a confidence measure for climate relevance.
Pipeline
Source: sraj/finewebedu-climate-v2… See the full description on the dataset page: https://huggingface.co/datasets/Michaelyya/fineweb-edu-v2-FastT.fineweb-edu-dedup6mfineweb-edu-pretokenized-llama3-100b
FineWeb-Edu Pretokenized with Llama 3.1 (100B)
This repository contains the sample/100BT subset of FineWeb-Edu, pretokenized for Megatron-LM-style training with meta-llama/Meta-Llama-3.1-8B.
It is a derived, pretokenized version of the upstream data, not an official Hugging Face FineWeb release.
Dataset summary
140 indexed shards
97,270,686 non-empty documents
97,458,793,013 tokens
English web text from FineWeb-Edu sample/100BT
Source dataset revision:… See the full description on the dataset page: https://huggingface.co/datasets/ZhuofengLi/fineweb-edu-pretokenized-llama3-100b.fineweb-edufineweb_score4_sortedFineWeb2-HQ-ja-20B元のデータセットFineWeb2-HQ
元のデータセットは多言語で巨大なため、扱いやすい用に日本語データを約200GBだけ抽出したデータセットです
wc 結果
1763269 38541549 5370473709 fineweb_jpn_Jpan_chunk_0.jsonl
1784158 37430170 5370514369 fineweb_jpn_Jpan_chunk_1.jsonl
1639554 40065129 5370372344 fineweb_jpn_Jpan_chunk_10.jsonl
1575127 42167166 5370298354 fineweb_jpn_Jpan_chunk_11.jsonl
1686375 39225898 5370402506 fineweb_jpn_Jpan_chunk_12.jsonl
1786948 36456352 5370498572… See the full description on the dataset page: https://huggingface.co/datasets/dahara1/FineWeb2-HQ-ja-20B.fineweb-4langfineweb_2_500k_both_deduplicatedFineweb_2_500k_filteredfineweb-edu-100BT-samples-not-in-10BTFineweb-InstructWe convert the pre-training corpus from Fineweb-Edu (https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) to instruction following format. We select a subset with quality filter and then use GPT-4 to extract instruction-following pairs. The dataset contains roughly 16M instruction pairs. The basic concept is similar to MAmmoTH2 (https://arxiv.org/abs/2405.03548).
Citation
If you use dataset useful, please cite the following paper:
@article{yue2024mammoth2… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/Fineweb-Instruct.finewebs-scandeval-results
ScandEval Results on English NLU
We use ScandEval in revision 8766d2a to conduct experiments with our pretrained FineWeb LMs.
Additionally, results for BERT, RoBERTa and ELECTRA were also performed to have a nice comparison.
Model ID
Avg. Score
CoNLL-En
SST5
ScaLA-En
SQuAD
model-garden-lms/bert-base-finewebs-1m
69.03
88.98 ± 0.43 / 88.67 ± 0.36
58.11 ± 1.2 / 59.77 ± 1.49
57.29 ± 3.57 / 77.15 ± 2.17
55.82 ± 1.35 / 66.46 ± 1.51
model-garden-lms/bert-base-finewebs-951k… See the full description on the dataset page: https://huggingface.co/datasets/model-garden-lms/finewebs-scandeval-results.fineweb_class5-0Fineweb_2_500k_bothfineweb2-chinese
FineWeb2 - Chinese
From cmn_Hani subset
Region
Rows
MAINLAND_CHINA
834356
TAIWAN
83875
HONG_KONG
10411
AMBIGUOUS_TRADITIONAL_TW_OR_HK
67700
OTHER
3658
