{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-distilbert--distilbert-base-multilingual-cased","slug":"distilbert--distilbert-base-multilingual-cased","name":"distilbert-base-multilingual-cased","type":"model","url":"https://huggingface.co/distilbert/distilbert-base-multilingual-cased","page_url":"https://unfragile.ai/distilbert--distilbert-base-multilingual-cased","categories":["model-training"],"tags":["transformers","pytorch","tf","onnx","safetensors","distilbert","fill-mask","multilingual","af","sq","ar","an","hy","ast","az","ba","eu","bar","be","bn"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-distilbert--distilbert-base-multilingual-cased__cap_0","uri":"capability://text.generation.language.multilingual.masked.token.prediction.with.distillation","name":"multilingual masked token prediction with distillation","description":"Predicts masked tokens across 104 languages using a 6-layer transformer architecture distilled from BERT-base-multilingual-cased. The model applies knowledge distillation (student-teacher training) to compress the 12-layer BERT into 6 layers while preserving multilingual semantic understanding. It uses WordPiece tokenization with a 119k shared vocabulary across all supported languages, enabling cross-lingual transfer learning through a single unified embedding space.","intents":["I need to fill in missing words in text across multiple languages without maintaining separate models per language","I want to use a lightweight multilingual model that runs efficiently on CPU or edge devices while maintaining BERT-level semantic understanding","I need to perform masked language modeling for pretraining or fine-tuning downstream NLP tasks in non-English languages","I want to leverage cross-lingual embeddings to understand semantic relationships between words in different languages"],"best_for":["NLP teams building multilingual applications with resource constraints (mobile, edge, or cost-sensitive inference)","Researchers fine-tuning models for downstream tasks (NER, classification, QA) across 104 languages","Developers implementing zero-shot cross-lingual transfer learning pipelines","Teams migrating from language-specific models to unified multilingual architectures"],"limitations":["6-layer architecture reduces model capacity compared to BERT-base (12 layers), potentially degrading performance on complex semantic tasks requiring deeper reasoning","Distillation trade-off: ~5-10% accuracy loss on masked language modeling vs full BERT-base-multilingual-cased depending on language and domain","No built-in support for character-level or subword regularization — uses fixed WordPiece vocabulary, limiting robustness to misspellings or rare morphological variants","Trained on Wikipedia and BookCorpus data; may underperform on domain-specific terminology (medical, legal, technical) without fine-tuning","Shared vocabulary across 104 languages creates token collision risk for homographs across different language pairs"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ (model supports both frameworks via transformers library)","transformers library version 4.0+","Minimum 512MB RAM for inference; 2GB+ recommended for batch processing","ONNX Runtime 1.10+ (optional, for optimized inference)","Python 3.6+"],"input_types":["raw text strings with [MASK] tokens","tokenized sequences (token IDs)","batched text inputs (up to model's max_position_embeddings of 512 tokens)"],"output_types":["logits over 119k vocabulary for masked positions","probability distributions for top-k predictions","token IDs and confidence scores for masked token candidates"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-multilingual-cased__cap_1","uri":"capability://data.processing.analysis.cross.lingual.semantic.embedding.generation","name":"cross-lingual semantic embedding generation","description":"Generates fixed-size dense embeddings (768-dimensional) for text in any of 104 supported languages by extracting the [CLS] token representation or pooling hidden states from the 6-layer transformer. The shared multilingual vocabulary and distilled architecture enable embeddings from different languages to occupy nearby regions in the same vector space, enabling semantic similarity comparisons across language boundaries without explicit translation.","intents":["I need to compute semantic similarity between text in different languages without translating them first","I want to build a multilingual semantic search index that retrieves documents regardless of query language","I need embeddings for clustering or classification tasks across multilingual datasets","I want to detect duplicate or near-duplicate content across language variants"],"best_for":["Teams building multilingual search engines or recommendation systems","Researchers studying cross-lingual semantic alignment and transfer learning","Content moderation platforms handling user-generated content in multiple languages","Developers implementing multilingual document clustering or deduplication"],"limitations":["Embedding quality degrades for low-resource languages (e.g., Amharic, Basque) due to underrepresentation in training data relative to high-resource languages (English, Spanish, Chinese)","Fixed 768-dimensional embeddings may be suboptimal for some downstream tasks; no built-in dimensionality reduction or task-specific projection layers","Cross-lingual alignment is approximate — semantic distance between languages is not uniform; some language pairs (e.g., Spanish-Portuguese) align better than distant pairs (e.g., English-Bengali)","No contextual fine-tuning per language; all languages share identical model parameters, which can lead to interference in multilingual fine-tuning scenarios"],"requires":["transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","512MB+ RAM for single-sample inference; 4GB+ for batch embedding generation","Python 3.6+"],"input_types":["raw text strings in any of 104 supported languages","tokenized sequences (token IDs with attention masks)","batched text inputs (variable length, padded to max_length)"],"output_types":["768-dimensional float32 vectors (embeddings)","cosine similarity scores between embedding pairs","structured arrays of embeddings for batch inputs"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-multilingual-cased__cap_2","uri":"capability://text.generation.language.language.agnostic.token.classification.with.shared.vocabulary","name":"language-agnostic token classification with shared vocabulary","description":"Provides contextualized token representations (from intermediate layers) suitable for fine-tuning on token-level tasks (NER, POS tagging, chunking) across 104 languages using a single model. The WordPiece tokenization and shared embedding space enable transfer learning where a model fine-tuned on English NER can generalize to other languages with minimal additional training data, leveraging the multilingual pretraining.","intents":["I want to fine-tune a single model for named entity recognition across multiple languages without training separate models","I need to perform part-of-speech tagging or syntactic chunking in languages where labeled training data is scarce","I want to leverage English-trained token classifiers to bootstrap models for low-resource languages","I need to extract structured information (entities, attributes) from multilingual documents"],"best_for":["NLP teams building multilingual information extraction pipelines","Researchers studying zero-shot and few-shot cross-lingual transfer for token-level tasks","Companies processing multilingual customer support tickets or documents for entity extraction","Teams with limited labeled data in target languages who can leverage high-resource language annotations"],"limitations":["WordPiece tokenization creates subword tokens that don't align 1:1 with linguistic tokens; requires special handling (e.g., taking first subword token or averaging subword representations) for token-level predictions","Fine-tuning requires task-specific labeled data; zero-shot performance is limited and highly dependent on language similarity and task complexity","Distillation trade-off: reduced model capacity (6 vs 12 layers) may limit performance on complex token-level tasks requiring deep contextual reasoning","No built-in support for character-level features or morphological information; relies entirely on subword tokenization"],"requires":["transformers library 4.0+ with fine-tuning utilities","PyTorch 1.9+ or TensorFlow 2.4+","Labeled training data in at least one language (preferably high-resource) for fine-tuning","4GB+ RAM for fine-tuning; 8GB+ recommended for batch training","Python 3.6+"],"input_types":["raw text with token-level annotations (BIO/BIOES tags, POS labels, etc.)","tokenized sequences with corresponding label sequences","batched text inputs with variable-length sequences"],"output_types":["logits over label vocabulary for each token position","predicted label sequences (BIO tags, POS tags, etc.)","confidence scores per token prediction","contextualized token embeddings (from hidden layers)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-multilingual-cased__cap_3","uri":"capability://automation.workflow.efficient.inference.with.model.quantization.and.onnx.export","name":"efficient inference with model quantization and onnx export","description":"Supports export to ONNX format and quantization techniques (INT8, FP16) enabling deployment on resource-constrained devices (mobile, edge, embedded systems) with minimal accuracy loss. The 6-layer distilled architecture is inherently smaller than BERT-base, and combined with ONNX Runtime optimization and quantization, achieves 4-8x speedup and 75% model size reduction compared to full-precision PyTorch inference.","intents":["I need to deploy multilingual NLP models on mobile devices or edge servers with strict latency and memory constraints","I want to reduce inference costs by running quantized models on CPU-only infrastructure instead of GPU","I need to serve multilingual models at scale with minimal hardware investment","I want to enable real-time inference for applications like live translation or on-device content moderation"],"best_for":["Mobile app developers building multilingual NLP features for iOS/Android","Edge computing teams deploying models on IoT devices or embedded systems","Cost-conscious teams running inference at scale on CPU-only cloud infrastructure","Researchers benchmarking model compression techniques for multilingual transformers"],"limitations":["ONNX export requires manual conversion; no built-in one-click export from transformers library (requires onnx and onnxruntime packages)","INT8 quantization introduces ~1-3% accuracy degradation depending on task; FP16 quantization is more stable but provides less compression (2x vs 4x)","ONNX Runtime optimization is hardware-specific; optimal performance requires tuning for target device (CPU architecture, instruction sets)","No built-in support for dynamic quantization per-layer; uniform quantization may be suboptimal for layers with different activation distributions","Quantized models are less flexible for fine-tuning; retraining quantized models requires special techniques (quantization-aware training)"],"requires":["onnx 1.10+","onnxruntime 1.10+ (with appropriate execution providers for target hardware)","transformers library 4.0+","PyTorch 1.9+ (for ONNX export)","Python 3.6+","For mobile deployment: iOS 12+ or Android 5.0+ with appropriate ML frameworks (CoreML, TensorFlow Lite, or ONNX Mobile)"],"input_types":["raw text strings (converted to token IDs by ONNX model)","pre-tokenized sequences (token IDs and attention masks)","batched inputs with variable sequence lengths"],"output_types":["logits over vocabulary (for fill-mask task)","hidden states from intermediate layers (for embedding extraction)","quantized model artifacts (ONNX protobuf format)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilbert-base-multilingual-cased__cap_4","uri":"capability://text.generation.language.multilingual.language.understanding.with.case.sensitive.tokenization","name":"multilingual language understanding with case-sensitive tokenization","description":"Preserves case information during tokenization and embedding generation, enabling the model to distinguish between proper nouns, acronyms, and common words based on capitalization patterns. This is particularly valuable for languages with rich morphological systems (e.g., German, Russian) where case carries grammatical meaning, and for tasks requiring entity recognition where capitalization is a strong signal.","intents":["I need to preserve case information for proper noun detection and entity recognition across multiple languages","I want to leverage capitalization patterns as a feature for downstream NLP tasks in morphologically rich languages","I need to distinguish between acronyms and common words in multilingual text","I want to maintain case sensitivity for domain-specific applications (e.g., programming language code analysis, chemical compound names)"],"best_for":["NLP teams building entity recognition systems that rely on capitalization as a signal","Researchers working with morphologically rich languages where case carries grammatical information","Teams processing domain-specific text (code, chemical names, medical terminology) where case is semantically significant","Multilingual content moderation systems that need to distinguish proper nouns from common words"],"limitations":["Case-sensitive tokenization increases vocabulary size compared to case-insensitive models; the 119k vocabulary includes separate tokens for uppercase and lowercase variants","Case information is language-specific; some languages (e.g., Arabic, Chinese) don't use case, making case-sensitive tokenization less beneficial","Fine-tuning on mixed-case data may lead to overfitting to capitalization patterns; requires careful data preprocessing and augmentation","Case sensitivity can be a liability for robustness; models may fail on all-caps or all-lowercase text if training data doesn't include such variations"],"requires":["transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Input text with preserved case information (no lowercasing preprocessing)","Python 3.6+"],"input_types":["raw text with original case preserved","tokenized sequences with case-sensitive token IDs","batched text inputs maintaining case information"],"output_types":["case-sensitive embeddings and logits","token predictions that distinguish case variants","contextualized representations preserving case information"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":49,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ (model supports both frameworks via transformers library)","transformers library version 4.0+","Minimum 512MB RAM for inference; 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