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--- |
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dataset_name: fineweb2-llm-annotated |
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pretty_name: JQL LLMs Multilingual Educational Quality Annotations |
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license: odc-by |
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source_license: Same as FineWeb2 (see upstream dataset) |
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size_categories: |
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- 10M<n<100M |
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language: |
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- bg |
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- cs |
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- hr |
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- mk |
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- pl |
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- sl |
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- sk |
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- sr |
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- uk |
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- da |
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- de |
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- is |
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- nl |
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- nn |
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- nb |
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- sv |
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- ca |
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- es |
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- fr |
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- ga |
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- gl |
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- it |
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- pt |
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- ro |
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- et |
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- fi |
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- hu |
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- lt |
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- lv |
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- el |
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- mt |
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- tr |
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- sq |
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- eu |
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- hy |
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- en |
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--- |
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# 📚 JQL Educational Quality Annotations from LLMs |
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This dataset provides 17,186,606 documents with high-quality LLM annotations for evaluating the **educational value of web documents**, and serves as a benchmark for training and evaluating **multilingual LLM annotators** as described in the JQL [paper](https://arxiv.org/abs/2505.22232). |
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--- |
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## 📝 Dataset Summary |
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Multilingual document-level quality annotations scored on a 0–5 educational value scale by three state-of-the-art LLMs: |
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Gemma-3-27B-it, Mistral-3.1-24B-it, and LLaMA-3.3-70B-it. Up to 500k documents per language from FineWeb2 are included. |
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Annotations are aligned with human ratings and intended for quality estimation, distillation, and multilingual benchmark research. |
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## 🌐 Languages |
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In total we included 35 European languages. Input documents are in their native language, but models were prompted and responded in English. |
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## 🧱 Dataset Structure: |
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| Name | Description | |
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|------------------|-----------------------------------------------------| |
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| id | Unique FW2 identifier for the document | |
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| text | Full textual content extracted from the webpage | |
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| dum | Common Crawl dump identifier from which the data originates | |
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| url | Source URL of the document | |
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| date | Timestamp indicating when the document was crawled (ISO 8601 format) | |
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| file_path | Path to the WARC file in the Common Crawl S3 bucket | |
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| language | ISO 639-3 language code of the document (e.g., deu) | |
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| language_script | Script used in the document (e.g., Latn for Latin script) | |
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| language_score | Confidence score of the language identification (float between 0 and 1) | |
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| top_langs | JSON string mapping detected language-script pairs to their scores | |
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| minhash_cluster_size | Number of documents in the deduplication cluster | |
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| filter_reason | Reason for filtering or deduplication (e.g., duplicated_5_n_grams), NaN if it would have been filtered | |
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| edu_score | Dictionary with per-model aggregated scores (modelname_score), **-1 if a invalid score was generated** | |
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| aggregation | Dictionary with per-model aggregated type (modelname_type), either majority or average | |
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## ✂️ Data Splits: |
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This dataset is not pre-split. Users can generate custom splits by: |
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- Language |
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- Model agreement |
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- Prediction validity |
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- Document length or other features |
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## 🎯 Intended Use |
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- Training multilingual document quality models |
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- Benchmarking multilingual LLM performance |
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- Distillation and teacher-student LLM training |
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- Creating filters for noisy web-scale data |
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## ⚠️ Limitations: |
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- LLM-generated scores, not human-authored |
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- Some predictions may be invalid or inconsistent |
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- No domain control across documents |
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- Educational value is a subjective, task-specific metric |
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## 📖 Citation |
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```bibtex |
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@article{ali2025judging, |
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title = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models}, |
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author = { |
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Mehdi Ali, |
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Manuel Brack, |
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Max Lübbering, |
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Elias Wendt, |
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Abbas Goher Khan, |
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Richard Rutmann, |
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Alex Jude, |
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Maurice Kraus, |
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Alexander Arno Weber, |
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Felix Stollenwerk, |
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David Kaczér, |
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Florian Mai, |
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Lucie Flek, |
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Rafet Sifa, |
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Nicolas Flores-Herr, |
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Joachim Köhler, |
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Patrick Schramowski, |
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Michael Fromm, |
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Kristian Kersting |
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}, |
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year = {2025}, |
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journal = {arXiv preprint arXiv:2505:22232} |
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} |
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``` |
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## 🔗 Links: |
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- Base Dataset: [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) |
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- Related Work: [FineWeb2 LLM Judging Section](https://huggingface.co/papers/llm-quality-judging-fineweb2) |
