Datasets:
File size: 3,717 Bytes
6e2848c aae9101 6e2848c aae9101 6e2848c aae9101 6e2848c aae9101 6e2848c aae9101 6e2848c aae9101 6e2848c aae9101 6e2848c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
---
license: odc-by
---
# Zyda2-5T
<!-- Provide a quick summary of the dataset. -->
Zyda2 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. Zyda2 comprises diverse sources of web data, highly educational content, math, code, and scientific papers.
To construct Zyda2, we took the best open-source datasets available: Zyda, FineWeb, DCLM, Dolma. Models trained on Zyda2 significantly outperform identical models trained on the Pile, RefinedWeb, FineWeb, FineWeb-Edu, and DCLM. Due to our post-processing deduplication, filtering, and weighting pipeline, Zyda2 outperforms all its constituent datasets in resulting model quality.
An early version of Zyda2 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 Zyda2 as a pretraining dataset.
According to our evaluations, Zyda2 is the most performant per-token open dataset available. Zyda2 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).
// TODO Ablation scores key plots
For more information, please see our technical blog (-/TODO LINK)
## How to download
// TODO YURY
## Breakdown by component
// TODO YURY
### 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. -->
// TODO IS THIS CORRECT YURY?
Dataset fields:
- `text`: contains actual text for training
- `source`: component the text is coming from
- `filtering_features`: precomputed values of different features that were used for filtering (converted to json string)
- `source_other`: metadata from the source dataset (converted to json string)
### 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-2 https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu
// Pie chart of composition -- YURY!
#### 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}
}
```
|