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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.

  1. Adjust average-word length filters according to lingusitic characteristics of each language
  2. Add language-specific stop words
  3. 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).

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