Create README.md

README.md

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+---
+dataset_name: fineweb2-llm-annotated
+pretty_name: JQL LLMs Multilingual Educational Quality Annotations
+license: odc-by
+source_license: Same as FineWeb2 (see upstream dataset)
+size_categories:
+  - 100K<n<1M
+language:
+  - bg
+  - cs
+  - hr
+  - mk
+  - pl
+  - sl
+  - sk
+  - sr
+  - uk
+  - da
+  - de
+  - is
+  - nl
+  - nn
+  - nb
+  - sv
+  - ca
+  - es
+  - fr
+  - ga
+  - gl
+  - it
+  - pt
+  - ro
+  - et
+  - fi
+  - hu
+  - lt
+  - lv
+  - el
+  - mt
+  - tr
+  - sq
+  - eu
+  - hy
+  - en
+---
+
+# 📚 JQL Educational Quality Annotations from LLMs
+
+This dataset provides high-quality LLMs annotations for evaluating the **educational value of web documents**, and serves as a benchmark for training and evaluating **multilingual LLM annotators**.
+
+---
+
+## 📝 Dataset Summary
+
+  Multilingual document-level quality annotations scored on a 0–5 educational value scale by three state-of-the-art LLMs:
+  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.
+  Annotations are aligned with human ratings and intended for quality estimation, distillation, and multilingual benchmark research.
+
+## 🌐 Languages
+
+  total: 35
+
+  Includes both high-resource and low-resource languages. Input documents are in their native language, but models were prompted and responded in English.
+
+## Dataset Structure: 🧱
+
+| Name             | Description                                         |
+|------------------|-----------------------------------------------------|
+| id               | Unique FW2 identifier for the document              |
+| text             | Full textual content extracted from the webpage     |
+| dum              | Common Crawl dump identifier from which the data originates              |
+| url               | Source URL of the document              |
+| date           | Timestamp indicating when the document was crawled (ISO 8601 format)              |
+| file_path               | Path to the WARC file in the Common Crawl S3 bucket              |
+| language       | ISO 639-3 language code of the document (e.g., deu)         |
+| language_script               | Script used in the document (e.g., Latn for Latin script)              |
+| language_score            | Confidence score of the language identification (float between 0 and 1)           |
+| top_langs               | JSON string mapping detected language-script pairs to their scores              |
+| minhash_cluster_size               | Number of documents in the deduplication cluster              |
+| filter_reason               | Reason for filtering or deduplication (e.g., duplicated_5_n_grams), NaN if it would have been filtered              |
+| edu_score           | Dictionary with per-model aggregated scores (modelname_score), **-1 if a invalid score was generated**   |
+| aggregation | Dictionary with per-model aggregated type (modelname_type), either majority or average       |
+
+## Data Splits: ✂️
+
+  This dataset is not pre-split. Users can generate custom splits by:
+  - Language
+  - Model agreement
+  - Prediction validity
+  - Document length or domain metadata (if available)
+
+## 🎯 Intended Use
+
+  - Training multilingual document quality models
+  - Benchmarking multilingual LLM performance
+  - Distillation and teacher-student LLM training
+  - Creating filters for noisy web-scale data
+
+## Limitations: ⚠️
+
+  - LLM-generated scores, not human-authored
+  - Some predictions may be invalid or inconsistent
+  - No domain control across documents
+  - Educational value is a subjective, task-specific metric
+
+## 📖 Citation
+
+```bibtex
+@article{mehdi2025judging,
+    title     = {Judging Quality Across Languages: A Multilingual Approach to Pretraining Data Filtering with Language Models},
+    author    = {
+      Mehdi Ali,
+      Manuel Brack,
+      Max Lübbering,
+      Elias Wendt,
+      Abbas Goher Khan,
+      Richard Rutmann,
+      Alex Jude,
+      Maurice Kraus,
+      Alexander Arno Weber,
+      Felix Stollenwerk,
+      David Kaczér,
+      Florian Mai,
+      Lucie Flek,
+      Rafet Sifa,
+      Nicolas Flores-Herr,
+      Joachim Köhler,
+      Patrick Schramowski,
+      Michael Fromm,
+      Kristian Kersting
+    },
+    year      = {2025},
+    journal   = {arXiv preprint arXiv:25XX:XXXX}
+  }
+```
+
+
+## links: 🔗
+- Base Dataset: [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2)
+- Related Work: [FineWeb2 LLM Judging Section](https://huggingface.co/papers/llm-quality-judging-fineweb2)