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Outputs probability distributions over the 30,522-token vocabulary for each masked position.","intents":["I need to fill in missing words in a sentence to complete text generation tasks","I want to predict what token should replace a [MASK] token in a document","I need to evaluate language model perplexity on masked language modeling benchmarks","I want to use a pre-trained encoder for downstream NLU tasks via fine-tuning"],"best_for":["NLP researchers benchmarking masked language model performance","teams building text completion or autocorrect systems","developers fine-tuning on domain-specific masked prediction tasks","organizations evaluating encoder-only architectures for classification/NER"],"limitations":["Requires full sequence context to predict masked tokens — cannot generate text autoregressively without modification","Maximum sequence length of 512 tokens; longer documents must be chunked or truncated","No built-in support for multi-lingual masking — trained on English-only corpus","Disentangled attention adds ~15-20% computational overhead vs standard BERT during inference","Predictions are context-dependent; identical masked tokens in different contexts produce different outputs"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ runtime","Transformers library 4.0+","Minimum 512MB GPU VRAM for batch_size=1 inference (1.5GB+ for batch_size=8)","Input text tokenized to max 512 subword tokens"],"input_types":["text (raw strings with [MASK] tokens)","tokenized sequences (input_ids, attention_mask, token_type_ids tensors)"],"output_types":["logits (batch_size, sequence_length, 30522 vocabulary scores)","top-k predictions with confidence scores","probability distributions over vocabulary"],"categories":["text-generation-language","nlp-encoder"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-microsoft--deberta-v3-base__cap_1","uri":"capability://text.generation.language.fine.tuning.for.downstream.nlp.tasks","name":"fine-tuning-for-downstream-nlp-tasks","description":"Provides a pre-trained encoder backbone (12 layers, 768 hidden dims, 110M parameters) that can be efficiently fine-tuned for downstream tasks like text classification, named entity recognition, semantic similarity, and question answering. 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The disentangled attention mechanism separates these weights into content-to-content, content-to-position, and position-to-position attention patterns, enabling more granular analysis of what linguistic phenomena the model has learned compared to standard multi-head attention.","intents":["I want to visualize which words the model attends to for a given prediction","I need to debug model behavior by inspecting attention patterns for specific examples","I want to analyze what linguistic relationships the model has learned","I need to generate attention-based explanations for model predictions"],"best_for":["researchers studying transformer interpretability and attention mechanisms","practitioners debugging unexpected model predictions","teams building explainable AI systems that require attention-based explanations","educators teaching how transformers work through attention visualization"],"limitations":["Attention weights are not guaranteed to be faithful explanations of model behavior; 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