{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated","slug":"helsinki-nlp--fineweb-edu-translated","name":"fineweb-edu-translated","type":"dataset","url":"https://huggingface.co/datasets/Helsinki-NLP/fineweb-edu-translated","page_url":"https://unfragile.ai/helsinki-nlp--fineweb-edu-translated","categories":["model-training"],"tags":["task_categories:translation","task_categories:text-generation","language:bos","language:bul","language:cat","language:ces","language:dan","language:deu","language:ell","language:eng","language:est","language:eus","language:fin","language:fra","language:gle","language:glg","language:hrv","language:hun","language:isl","language:ita"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_0","uri":"capability://data.processing.analysis.multilingual.educational.text.corpus.retrieval","name":"multilingual educational text corpus retrieval","description":"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.","intents":["Train multilingual language models on high-quality educational content without manual curation","Build language-specific NLP datasets for low-resource European languages","Evaluate cross-lingual transfer learning by accessing parallel educational texts","Create domain-specific training corpora for educational AI applications"],"best_for":["NLP researchers training multilingual models (especially for low-resource languages like Icelandic, Irish, Galician)","Teams building educational AI assistants requiring diverse language support","Organizations fine-tuning foundation models on domain-specific educational content"],"limitations":["Translations are machine-generated via neural MT, not human-curated — may contain systematic translation artifacts or domain-specific terminology errors","Dataset is static snapshot; no versioning or incremental updates after initial release","Language coverage is limited to 19 European languages; no support for non-Latin scripts or non-European languages","Quality varies by language pair and source domain; no per-document quality filtering mechanism exposed in API","No built-in deduplication across language variants — parallel documents may have slight content divergence"],"requires":["HuggingFace datasets library (>=2.0.0) for programmatic access","Minimum 50GB disk space for full dataset download","Python 3.7+ for data loading and preprocessing scripts","Internet connectivity for initial dataset download from HuggingFace Hub"],"input_types":["language code (ISO 639-3 format: eng, fin, deu, etc.)","optional filtering parameters (domain, quality threshold)"],"output_types":["text documents (UTF-8 encoded)","structured metadata (JSON/parquet with language, source URL, translation confidence)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_1","uri":"capability://data.processing.analysis.language.specific.document.filtering.and.sampling","name":"language-specific document filtering and sampling","description":"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.","intents":["Download only documents for a specific language to reduce storage and bandwidth requirements","Create balanced train/validation splits for language-specific model evaluation","Sample representative subsets for rapid prototyping before committing to full-dataset training","Perform language-pair contrastive analysis on parallel educational texts"],"best_for":["Researchers with limited compute/storage working on specific language pairs","Teams prototyping multilingual models and needing fast iteration cycles","Organizations building language-specific fine-tuning datasets from a larger corpus"],"limitations":["Filtering is applied at load time, not pre-computed — repeated queries for same language subset incur redundant I/O","No built-in stratification by document length, source domain, or quality score — sampling may be skewed toward longer or lower-quality documents","Streaming API requires persistent network connection; offline sampling not supported","Random seed control is limited to HuggingFace's default PRNG — reproducibility across different library versions not guaranteed"],"requires":["HuggingFace datasets library with streaming support enabled","Language code in ISO 639-3 format (e.g., 'fin', 'deu', 'eng')","Sufficient RAM to hold sampled batch in memory (typically 1-4GB for 10K documents)"],"input_types":["language code (string, ISO 639-3)","sample size (integer, optional)","random seed (integer, optional)"],"output_types":["iterable dataset of text documents with metadata","pandas DataFrame or Arrow Table for batch processing"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_2","uri":"capability://data.processing.analysis.neural.machine.translation.quality.assessment.via.metadata","name":"neural machine translation quality assessment via metadata","description":"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.","intents":["Filter out low-confidence translations to improve downstream model training quality","Analyze which language pairs have systematic translation quality issues","Create high-confidence subsets for critical applications (e.g., educational content for language learners)","Benchmark translation quality across different source languages"],"best_for":["Teams training models on translated content and wanting to control quality thresholds","Researchers analyzing machine translation quality at scale","Organizations building educational applications where translation errors have pedagogical impact"],"limitations":["Confidence scores are proxy metrics (likely MT model confidence, not human evaluation) — may not correlate with actual translation quality or domain appropriateness","No per-sentence or per-phrase granularity — scores are document-level only, masking localized translation errors","Scoring methodology is not documented; unclear if scores account for domain-specific terminology or educational content requirements","No human-validated ground truth for calibrating confidence thresholds","Threshold selection is empirical; no principled guidance on quality cutoffs for different use cases"],"requires":["Access to metadata fields in dataset (language_pair, confidence_score, or similar)","Understanding of translation quality metrics and their limitations","Ability to parse and filter numeric metadata from parquet files"],"input_types":["confidence score threshold (float, 0.0-1.0)","language pair filter (tuple of ISO 639-3 codes, optional)"],"output_types":["filtered dataset of documents meeting quality threshold","quality distribution statistics (histogram, percentiles)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_3","uri":"capability://data.processing.analysis.parallel.multilingual.document.alignment.and.retrieval","name":"parallel multilingual document alignment and retrieval","description":"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.","intents":["Train multilingual models using parallel documents as contrastive pairs","Perform cross-lingual information retrieval (retrieve documents in language A, find equivalents in language B)","Analyze how educational concepts are explained differently across languages","Create multilingual training batches with aligned document pairs"],"best_for":["Researchers training multilingual embeddings or cross-lingual transfer models","Teams building multilingual search or recommendation systems","Organizations studying how educational content translates across languages"],"limitations":["Alignment is document-level only; no sentence or phrase-level alignment for fine-grained contrastive learning","Not all documents have translations in all 19 languages — alignment is sparse and irregular across language pairs","Source document IDs may not be exposed in public API; users may need to infer alignment from content hashing or metadata","No explicit quality metrics for alignment accuracy — translations may diverge significantly from source, breaking alignment assumptions","Alignment metadata may be incomplete or inconsistent across dataset versions"],"requires":["Source document ID or content hash for querying aligned variants","Ability to filter and join documents across language subsets","Understanding of document-level vs. sentence-level alignment trade-offs"],"input_types":["source document ID (string)","list of target languages (ISO 639-3 codes)"],"output_types":["set of parallel documents in requested languages","metadata mapping source to translated variants"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_4","uri":"capability://data.processing.analysis.educational.domain.content.filtering.and.curation","name":"educational domain content filtering and curation","description":"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.","intents":["Train models specifically on educational content without manual curation of web-scale data","Avoid training on low-quality or non-educational web text that could degrade model behavior","Build domain-specific educational models (e.g., STEM tutoring, language learning)","Evaluate model performance on educational benchmarks using in-domain training data"],"best_for":["Teams building educational AI assistants or tutoring systems","Researchers studying how domain-specific pretraining affects downstream task performance","Organizations fine-tuning models for educational applications"],"limitations":["Educational filtering heuristics are not transparent; unclear which signals determine 'educational' classification","Filtering may be biased toward certain types of educational content (e.g., formal curricula over informal learning resources)","No fine-grained domain tags (e.g., 'high school biology' vs. 'college biology') — only coarse-grained educational classification","Filtering is static; no mechanism to update or refine educational quality assessment post-release","False positives/negatives in filtering may introduce subtle biases (e.g., overrepresentation of STEM content if filtering favors technical writing)"],"requires":["Understanding of FineWeb's educational filtering methodology (not fully documented)","Acceptance that 'educational' is a heuristic classification, not ground truth"],"input_types":["optional domain filter (e.g., 'STEM', 'humanities')","optional quality tier filter (if available in metadata)"],"output_types":["filtered dataset of educational documents","domain distribution statistics"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-dataset-helsinki-nlp--fineweb-edu-translated__cap_5","uri":"capability://data.processing.analysis.low.resource.language.dataset.augmentation.via.translation","name":"low-resource language dataset augmentation via translation","description":"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.","intents":["Train language models for low-resource European languages using translated educational content","Augment limited native-language datasets with high-quality translated educational material","Evaluate how translation-based data augmentation affects downstream task performance for low-resource languages","Build multilingual models with balanced representation across high- and low-resource languages"],"best_for":["Researchers working on low-resource language NLP (especially European languages)","Teams building language-specific models for underrepresented languages","Organizations supporting multilingual applications in less-resourced language communities"],"limitations":["Translations are synthetic; may introduce systematic biases or artifacts that differ from native-language educational content","Translation quality likely varies significantly across language pairs — some low-resource languages may receive lower-quality translations due to MT model limitations","Synthetic data may not capture language-specific pedagogical conventions or educational terminology","No human evaluation of translation quality for low-resource languages; quality assessment relies on automatic metrics","Using translated data may inadvertently propagate biases or errors from source language into target language models"],"requires":["Acceptance that synthetic translated data has different characteristics than native-language content","Ability to evaluate model performance on native-language benchmarks to assess translation quality impact","Understanding of low-resource language NLP challenges and translation limitations"],"input_types":["target language code (ISO 639-3, e.g., 'isl', 'gle', 'eus')","optional quality threshold for translation confidence"],"output_types":["translated educational documents in target language","metadata indicating source language and translation confidence"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"low","permissions":["HuggingFace datasets library (>=2.0.0) for programmatic access","Minimum 50GB disk space for full dataset download","Python 3.7+ for data loading and preprocessing scripts","Internet connectivity for initial dataset download from HuggingFace Hub","HuggingFace datasets library with streaming support enabled","Language code in ISO 639-3 format (e.g., 'fin', 'deu', 'eng')","Sufficient RAM to hold sampled batch in memory (typically 1-4GB for 10K documents)","Access to metadata fields in dataset (language_pair, confidence_score, or similar)","Understanding of translation quality metrics and their limitations","Ability to parse and filter numeric metadata from parquet files"],"failure_modes":["Translations are machine-generated via neural MT, not human-curated — may contain systematic translation artifacts or domain-specific terminology errors","Dataset is static snapshot; no versioning or incremental updates after initial release","Language coverage is limited to 19 European languages; no support for non-Latin scripts or non-European languages","Quality varies by language pair and source domain; no per-document quality filtering mechanism exposed in API","No built-in deduplication across language variants — parallel documents may have slight content divergence","Filtering is applied at load time, not pre-computed — repeated queries for same language subset incur redundant I/O","No built-in stratification by document length, source domain, or quality score — sampling may be skewed toward longer or lower-quality documents","Streaming API requires persistent network connection; offline sampling not supported","Random seed control is limited to HuggingFace's default PRNG — reproducibility across different library versions not guaranteed","Confidence scores are proxy metrics (likely MT model confidence, not human evaluation) — may not correlate with actual translation quality or domain appropriateness","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.764Z","last_scraped_at":"2026-05-03T14:22:48.064Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=helsinki-nlp--fineweb-edu-translated","compare_url":"https://unfragile.ai/compare?artifact=helsinki-nlp--fineweb-edu-translated"}},"signature":"pkQfOxQRmUKyW61fZ/fsDKQ/8UC71M2XbvkUoRUi3zliaJIQS7F8Zd3tiS8VF06TnbWkqkDunNmu6irtUhXZDg==","signedAt":"2026-06-21T23:00:54.466Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/helsinki-nlp--fineweb-edu-translated","artifact":"https://unfragile.ai/helsinki-nlp--fineweb-edu-translated","verify":"https://unfragile.ai/api/v1/verify?slug=helsinki-nlp--fineweb-edu-translated","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}