fineweb-edu-translated

Provides access to a curated dataset of 384,377 educational web documents translated across 19+ European languages using neural machine translation. The dataset is structured as HuggingFace-compatible parquet files with metadata fields (language codes, source URLs, quality scores) enabling filtered retrieval by language, domain, or quality tier. Documents are pre-tokenized and formatted for direct consumption by transformer-based language models without additional preprocessing.

Enables selective loading of documents by language code using HuggingFace's streaming API, allowing users to sample subsets without downloading the entire 384K-document corpus. Filtering is implemented via language-tagged metadata in parquet row groups, enabling efficient columnar filtering at the storage layer. Supports random sampling, stratified sampling by source domain, and deterministic splits for reproducible train/validation/test partitions.

Exposes translation confidence scores and source-target language pair metadata for each document, enabling users to filter by translation quality without re-running MT evaluation. Scores are computed during the translation pipeline (likely using cross-entropy loss or back-translation scoring) and stored as numeric fields in the dataset metadata. Users can threshold documents by confidence score to create higher-quality subsets or analyze translation quality distribution across language pairs.

Maintains document-level alignment across language variants (e.g., same educational article translated to Finnish, German, and English) through shared source document IDs in metadata. Users can retrieve all language variants of a document by querying on source ID, enabling cross-lingual analysis, contrastive learning, or multilingual fine-tuning. Alignment is implicit (via metadata keys) rather than explicit (no sentence-level alignment), suitable for document-level tasks but not word-level alignment.

Provides pre-filtered educational content sourced from FineWeb's pedagogical quality assessment pipeline, which uses heuristics (e.g., presence of educational keywords, structured content markers, domain-specific signals) to identify educational documents from web crawls. The filtering is applied upstream during dataset creation; users access only documents already vetted as educational. Metadata may include domain tags (e.g., STEM, humanities, language learning) enabling secondary filtering.

Provides machine-translated versions of educational content for 19 European languages, including low-resource languages (Icelandic, Irish, Galician, Estonian, Basque) that typically have limited training data. Translation is performed via neural MT (likely mBART or similar multilingual model) to create synthetic training data for languages with scarce educational corpora. This enables training of language-specific models without relying solely on limited native-language sources.