metadata

language:
  - de
  - es
  - fr
  - pt
  - it
  - nl
  - el
  - pl
  - cs
  - sk
task_categories:
  - text-generation
pretty_name: Occiglot Fineweb v0.5
size_categories:
  - 10B<n<100B
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Occiglot Fineweb v0.5

We present a preliminary version of the multilingual Occiglot Fineweb corpus. In this early form, the dataset contains roughly 230M heavily cleaned documents from 10 languages. Occiglot Fineweb builds on our existing collection of curated datasets and pre-filtered web data. Subsequently, all documents were filtered with language-specific derivatives of the fine-web processing pipeline and globally depuplicated.

We are actively working on extending this dataset with more data and further languages. For more information please refer to our blog post or join our Discord server.

Unfortunately, some of the datasets we used do not allow for re-distribution. Consequently, we had to exclude those from this version of our dataset. We are exploring different avenues to make this data available to the public as well.

Datasources

We mainly relied on two sources of data.

1. LLM-Dataset

From LLM-Datasets we took all available datasets for the considered languages (excluding OSCAR). This collection of data for LLM training is curated from various sources and contains multiple high-quality datasets.

2. Web-Data

We sourced web-crawled data from 12 Common-Crawl releases from 2015 until June 2023. All releases were then processed with OSCAR's Ungoliant pipeline.

Filtering

All data was rigorously filtered using language-specific pipelines built upon Huggingface's fine-web filters. In addition to some minor hyper-parameter adjustments we mainly modified 3 aspects to ensure language-specific quality filtering.

Adjust average-word length filters according to lingusitic characteristics of each language
Add language-specific stop words
Add a language-specific policy filter for policy and cookie filtering

Deduplication

We performed minhash deduplication on all data of each language. Importantly, we always retain the duplicate not contained in the web-crawled data. For example, if a wikipedia page is also contained in OSCAR, we drop the OSCAR duplicate, thus keeping the wikipedia subset complete. This dataset structure allows to reliably over- or undersample the custom subsets.

Statistics

Language	lang-code	# Documents	# Tokens (Llama-3)
German	de	43.40M	65.02B
Spanish	es	42.05M	50.96B
French	fr	31.44M	39.90B
Portugese	pt	22.58M	26.06B
Italian	it	17.32M	25.21B
Dutch	nl	16.15M	16.90B
Greek	el	12.74M	16.07B
Polish	pl	10.03M	15.51B
Czech	cs	32.83M	12.74B
Slovak	sk	2.86M	9.03B

Acknowledgements

The dataset creation by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)). Some preliminary computations were conducted on the DFKI Pegasus Cluster. Parts of the preliminary data curation were funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).