---
language:
- de
- es
- fr
- pt
- it
- nl
- el
- pl
- cs
- sk
task_categories:
- text-generation
pretty_name: Occiglot Fineweb v1.0
size_categories:
- 10B<n<100B
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---

# Occiglot Fineweb v1.0

We present a more mature version of the multilingual Occiglot Fineweb corpus. In this early form, the dataset contains roughly 430M 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 different levels of depuplicated. 

We provide the data at 3 levels of processing: 

1. After filtering
2. After local deduplication (within data sources)
3. After global deduplocation (for each language)

We are actively working on extending this dataset with more data and further languages. For more information please refer to our [blog post](https://occiglot.eu/posts/occiglot-fineweb/) or join our [Discord server](https://discord.gg/wUpvYs4XvM).

**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](https://github.com/malteos/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 our [Community-Oscar](https://huggingface.co/datasets/oscar-corpus/community-oscar) dataset. 

## Filtering

All data was rigorously filtered using language-specific pipelines built upon [Huggingface's fine-web filters](https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py). 
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

Compared to the our [prior version](https://huggingface.co/datasets/occiglot/occiglot-fineweb-v0.5), we improved the configuration of the filtering settings, cleaned up the encoding of every document using ftfy and ran an additional language id filtering step for datasources from countries with multiple official languages (e.g. Belgium). 

## 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 the globally deduplicated dataset. 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

For the global deduplciated set: 

| Language | lang-code | # Documents | # Tokens (Llama-3)
| -- | -- | -- | --
| German |de |  82.60M | 135.46B
| Spanish |es | 91.89M | 108.15B
| French | fr | 61.80M | 87.61B
| Portugese | pt | 46.97M | 54.87B
| Italian | it |  37.14M | 58.24B
| Dutch | nl | 29.00M | 33.78B
| Greek | el |  17.55M | 24.21B
| Polish | pl | 21.43M | 35.35B
| Czech | cs | 38.98M | 25.23B
| Slovak | sk | 4.18M | 11.13B
| | | |
| **Total** | | **431.53M** | **574.03B**

## Acknowledgements

The dataset creation by a compute grant at the [42 supercomputer](https://hessian.ai/)  which is a central component in the development of [hessian AI](https://hessian.ai/), the [AI Innovation Lab](https://hessian.ai/infrastructure/ai-innovationlab/) (funded by the [Hessian Ministry of Higher Education, Research and the Art (HMWK)](https://wissenschaft.hessen.de) & the [Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)](https://innen.hessen.de)) and the [AI Service Centers](https://hessian.ai/infrastructure/ai-service-centre/) (funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)).
Some preliminary computations were conducted on the [DFKI Pegasus Cluster](https://www.dfki.de/en/web).
Parts of the preliminary data curation were funded by the [German Federal Ministry for Economic Affairs and Climate Action (BMWK)](https://www.bmwk.de/Navigation/EN/Home/home.html)
through the project [OpenGPT-X](https://opengpt-x.de/en/) (project no. 68GX21007D).