|
--- |
|
license: odc-by |
|
task_categories: |
|
- text-generation |
|
language: |
|
- en |
|
pretty_name: Zyda |
|
size_categories: |
|
- n>1T |
|
--- |
|
|
|
Zyda is a unified dataset, released under a permissive license, comprising most of the largest and highest quality existing open-source datasets available. |
|
Upon these, we have performed extensive additional filtering, beyond what was originally applied, and thorough intra- and inter-dataset deduplication. |
|
Our aim with this work is to create a growing and extendable meta-dataset which can be easily used by practitioners to train trillion-token scale language models |
|
while also consolidating and unifying the efforts made by disparate open-source groups who have released their datasets. Ultimately, we hope that our work can |
|
provide an 'off-the-shelf' accessible trillion-scale high quality pretraining dataset which can be used by groups interested in pretraining their own LLM models. |
|
|
|
While not performing new filtering from common-crawl, we believe that our work is an important and valuable step towards the creation of large scale |
|
high quality open datasets given that there exist a number of high quality existing datasets but few to none of them individually reach the scale |
|
necessary for training frontier models. Collating, filtering, and deduplicating the existing datasets needed to create a trillion token dataset is |
|
nontrivial work and extremely important to raise the quality of the dataset and prevent significant amounts of inter-dataset duplicates. |
|
This latter operation of deduplication between datasets is extremely important given the degree of duplicated documents we discovered in |
|
the datasets that we collected in this work. |
|
|
|
Here we are releasing a version of the dataset that was deduplicated using LSH minhash technique with 40% Jaccard similarity threshold. |
