Datasets:

Zyphra
/

Zyda-2

	---
	license: odc-by
	pretty_name: Zyda2-5T
	task_categories:
	- text-generation
	language:
	- en
	size_categories:
	- n>1T
	configs:
	- config_name: default
	data_files:
	- split: train
	path: data///*
	- config_name: dclm_crossdeduped
	data_files:
	- split: train
	path: data/dclm_crossdeduped//
	- config_name: zyda_crossdeduped-filtered
	data_files:
	- split: train
	path: data/zyda_crossdeduped-filtered //
	- config_name: dolma-cc_crossdeduped-filtered
	data_files:
	- split: train
	path: data/dolma-cc_crossdeduped-filtered/*
	- config_name: fwe3
	data_files:
	- split: train
	path: data/fwe3//
	---

	# Zyda2-5T

	<!-- Provide a quick summary of the dataset. -->

	Zynemo is a 5 trillion token language modeling dataset created by collecting open and high quality datasets and combining them and cross-deduplication and model-based quality filtering. Zynemo comprises diverse sources of web data, highly educational content, math, code, and scientific papers.

	To construct Zynemo, we took the best open-source datasets available: Zyda, FineWeb, DCLM, Dolma. Models trained on Zynemo significantly outperform identical models trained on the Pile, RefinedWeb, FineWeb, FineWeb-Edu, and DCLM. Due to our post-processing deduplication, filtering, and weighting pipeline, Zynemo outperforms all its constituent datasets in resulting model quality.

	An early version of Zynemo was used as the primary dataset for phase 1 pretraining of our Zamba2 series [of](Zyphra/Zamba2-2.7B) [models](Zyphra/Zamba2-1.2B) which perform extremely strongly on a per-token basis and are often state-of-the-art for their size, testifying to the strength of Zynemo as a pretraining dataset.

	According to our evaluations, Zynemo is the most performant per-token open dataset available. Zynemo excels at educational and natural language reasoning content. For code performance, we reccomend mixing it with a pure code dataset such as [Starcoder](https://huggingface.co/bigcode/starcoder).


	<center>
	<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/YfOOh2JqRgkeHP1gHSSt9.png" width="600" alt="ZyNeMo evaluation scores">
	</center>



	For more information, please see our technical blog (-/TODO LINK)

	## How to download
	Since we preserved the schemas of original component datasets, attempting to dowlnoad the whole dataset using `datasets.load_dataset()` might fail during the stage of generating a split.

	To download the whole dataset we recommend to either clone the repository, or, if you must use the `datasets.load_dataset()`, download individual components separately.

	Example command to clone the repository using huggingface-cli: `huggingface-cli download Zyphra/Zyda2-5T--repo-type dataset`

	Commands to download individual components:
	- DCLM: `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="dclm_crossdeduped", split="train")`
	- Zyda: `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="zyda_crossdeduped-filtered", split="train")`
	- Dolma-CC: `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="dolma-cc_crossdeduped-filtered", split="train")`
	- Fineweb-Edu: `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="fwe3", split="train")`

	In this repository we provide raw results of cross deduplication and filtering. To achieve the best possible performance, one will need to appropriate weights during training.
	We found the following optimal weights (in the sense of weights in the resultant dataset): DCLM - 4.0, FWE3 - 4.0, Zyda - 0.16, Dolma-CC - 0.24.


	## Breakdown by component

	\| Component \| Download size (parquet, GBs) \| Documents (millions) \| gpt-neox tokens (billions) \|
	\| --- \| --- \| --- \| --- \|
	\| dclm-crossdeduped \| 8469.4 \| 2,590.5 \| 3,348.942 \|
	\| zyda-crossdeduped-filtered \| 452.4 \| 247.7 \| 163.6 \|
	\| dolma_cc-crossdeduped-filtered \| 668.2 \| 445.6 \| 238.4 \|
	\| fwe3 \| 3490.5 \| 1,279.1 \| 1,319.2 \|
	\| Total \| 13080.5 \| 4,562.8 \| 5,070.2 \|

	### Dataset Description

	<!-- Provide a longer summary of what this dataset is. -->

	- Curated by: Zyphra
	- Language(s) (NLP): Primarily English
	- License: Open Data Commons License


	## Dataset Structure

	<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

	Each component has they're own individual schema. Please, consult with their respective sources for exact information.

	However, in all components the document text is in `text` column, and unique document document id is in `nemo_id` column.

	Our Zyda1 and Dolma-CC versions also have two additional columns corresponding to prediction of Nvidia's quality model (https://huggingface.co/nvidia/quality-classifier-deberta): `quality_prob` and `quality_pred`.

	### Source Data

	Zyda2 is comprised of four high quality open-source datasets:

	Zyda1: https://huggingface.co/datasets/Zyphra/Zyda

	Dolma-1.7-cc https://huggingface.co/datasets/allenai/dolma

	DCLM-baseline: https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0

	FineWeb-Edu-score2: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-score-2

	<center>
	<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/GQenkNxzyM65M4eR2YZcV.png" width="600" alt="ZyNeMo dataset composition">
	</center>

	#### Personal and Sensitive Information

	As a language modelling dataset, it likely contains PII which has not been filtered out of the component datasets and which may have been missed by our own filters.

	## Bias, Risks, and Limitations

	As a dataset comprised of open web scrapes, it is likely that it contains biased and toxic content.

	## Licensing Information

	We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). By using this dataset, you are also bound by any license agreements and terms of use of the original data sources.

	## Citation

	If you use our dataset to train a model, please cite us at:

	```
	@misc{tokpanov2024zyda,
	title={Zyda: A 1.3T Dataset for Open Language Modeling},
	author={Yury Tokpanov and Beren Millidge and Paolo Glorioso and Jonathan Pilault and Adam Ibrahim and James Whittington and Quentin Anthony},
	year={2024},
	eprint={2406.01981},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```