{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-distilbert--distilroberta-base","slug":"distilbert--distilroberta-base","name":"distilroberta-base","type":"model","url":"https://huggingface.co/distilbert/distilroberta-base","page_url":"https://unfragile.ai/distilbert--distilroberta-base","categories":["model-training"],"tags":["transformers","pytorch","tf","jax","rust","safetensors","roberta","fill-mask","exbert","en","dataset:openwebtext","arxiv:1910.01108","arxiv:1910.09700","license:apache-2.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-distilbert--distilroberta-base__cap_0","uri":"capability://text.generation.language.masked.token.prediction.with.bidirectional.context","name":"masked-token-prediction-with-bidirectional-context","description":"Predicts masked tokens in text using a bidirectional transformer architecture trained on RoBERTa's objective function. The model uses a 6-layer DistilBERT-style distilled architecture (66% parameter reduction from RoBERTa-base) with 12 attention heads, processing input sequences up to 512 tokens and outputting probability distributions over the 50,265-token vocabulary. Implements masked language modeling (MLM) where [MASK] tokens are replaced with learned contextual representations derived from surrounding bidirectional context.","intents":["Fill in missing or corrupted words in text passages for data cleaning or augmentation","Generate contextually appropriate token suggestions for autocomplete or text editing workflows","Evaluate semantic coherence by scoring how well predicted tokens match expected values","Create embeddings for downstream NLP tasks by extracting hidden states from masked prediction layers"],"best_for":["NLP researchers prototyping masked language model applications with constrained compute budgets","Teams building text augmentation or data cleaning pipelines requiring fast inference","Developers fine-tuning on domain-specific corpora where parameter efficiency matters"],"limitations":["Requires explicit [MASK] token placement — cannot infer which tokens to predict without manual annotation","Bidirectional context means it cannot be used for autoregressive generation or next-token prediction tasks","Vocabulary is fixed at 50,265 tokens — out-of-vocabulary words are subword-tokenized, potentially degrading performance on rare technical terms","Maximum sequence length of 512 tokens limits applicability to long-document understanding without chunking strategies","No built-in uncertainty quantification — outputs softmax probabilities but not confidence intervals or calibration metrics"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+ (framework-agnostic model weights in SafeTensors format)","Transformers library 4.0+","Minimum 2GB GPU VRAM for batch inference; CPU inference supported but ~10-50x slower","Hugging Face account or local model weights download (~270MB disk space)"],"input_types":["raw text strings with [MASK] tokens","tokenized input_ids (integer sequences)","attention_mask tensors (binary sequences indicating padding)"],"output_types":["logits tensor (batch_size × sequence_length × vocab_size)","softmax probabilities over vocabulary","top-k token predictions with confidence scores"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_1","uri":"capability://text.generation.language.contextual.token.embeddings.extraction","name":"contextual-token-embeddings-extraction","description":"Extracts learned token representations from intermediate transformer layers (hidden states) that encode bidirectional context. The model produces 768-dimensional dense vectors for each input token by passing text through 6 transformer layers with 12 attention heads, capturing semantic and syntactic information. These embeddings can be extracted from any layer (0-6) and used as fixed representations or fine-tuned for downstream tasks like classification, NER, or semantic similarity.","intents":["Generate fixed token embeddings for semantic similarity search or clustering without task-specific fine-tuning","Extract contextual representations as features for downstream supervised learning tasks (classification, NER, relation extraction)","Analyze model behavior and attention patterns by inspecting intermediate layer activations and attention weights","Build efficient retrieval systems by encoding documents and queries into comparable vector spaces"],"best_for":["ML engineers building semantic search or similarity systems with limited labeled data","Researchers analyzing transformer behavior and attention mechanisms in production settings","Teams implementing transfer learning pipelines where pre-trained representations reduce annotation requirements"],"limitations":["Embeddings are context-dependent — same token produces different vectors in different sentences, requiring full re-encoding for new contexts","768-dimensional vectors require significant memory for large-scale retrieval (e.g., 1M documents × 768 dims = ~3GB RAM minimum)","No built-in dimensionality reduction — downstream systems must handle high-dimensional vectors or apply PCA/UMAP separately","Embeddings are not normalized by default — cosine similarity requires explicit L2 normalization before comparison","Layer selection is manual — no automatic mechanism to determine optimal layer for specific downstream tasks"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+","Transformers library 4.0+ with output_hidden_states=True parameter support","GPU with 2GB+ VRAM for batch processing; CPU inference possible but slow","Vector database or similarity search library (FAISS, Annoy, Milvus) for large-scale retrieval"],"input_types":["raw text strings","pre-tokenized input_ids (integer sequences)","attention_mask tensors"],"output_types":["hidden_states tensor (batch_size × sequence_length × 768)","per-layer embeddings (768-dimensional vectors)","attention_weights for interpretability"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_2","uri":"capability://code.generation.editing.fine.tuning.for.downstream.nlp.tasks","name":"fine-tuning-for-downstream-nlp-tasks","description":"Enables task-specific adaptation by adding task-specific heads (classification, token classification, or regression layers) on top of the pre-trained transformer backbone and training on labeled data. The model uses standard PyTorch/TensorFlow training loops with gradient-based optimization, supporting mixed-precision training for memory efficiency. Implements parameter freezing strategies (freeze encoder, train only head) and learning rate scheduling to prevent catastrophic forgetting while adapting to new domains.","intents":["Adapt the model to domain-specific text classification tasks (sentiment, intent, topic) with limited labeled data","Fine-tune for token-level tasks like named entity recognition (NER) or part-of-speech tagging using sequence labeling heads","Transfer knowledge from general pretraining to specialized domains (medical, legal, scientific) with minimal annotation overhead","Reduce training time and data requirements compared to training from scratch by leveraging pre-learned representations"],"best_for":["Data scientists building production NLP systems with 100-10K labeled examples per task","Teams with domain-specific text corpora requiring rapid model adaptation without massive annotation budgets","Practitioners optimizing for inference speed and model size in resource-constrained environments (mobile, edge)"],"limitations":["Requires task-specific labeled data — unsupervised fine-tuning not supported; minimum ~100 examples recommended for stable convergence","Hyperparameter tuning is essential — learning rate, batch size, and warmup steps significantly impact final performance; no automatic tuning built-in","Catastrophic forgetting risk if learning rates are too high or training duration too long — requires careful regularization and validation monitoring","Fine-tuned models are not portable across frameworks — PyTorch checkpoints require conversion for TensorFlow/JAX deployment","No built-in multi-task learning — each task requires separate fine-tuning; cannot share parameters across related tasks without custom implementation"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ with training loop support","Transformers library 4.0+ with Trainer API or custom training code","GPU with 4GB+ VRAM for batch sizes ≥8; CPU training extremely slow (hours per epoch)","Labeled dataset in standard formats (CSV, JSON, HuggingFace datasets)","Validation set for hyperparameter tuning and early stopping"],"input_types":["raw text strings with labels","pre-tokenized sequences with token-level or sequence-level labels","structured data (text + metadata) for multi-modal fine-tuning"],"output_types":["fine-tuned model weights (PyTorch .pt or TensorFlow SavedModel format)","task-specific predictions (class logits, token labels, regression values)","training metrics (loss, accuracy, F1, precision, recall)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_3","uri":"capability://tool.use.integration.multi.framework.model.loading.and.inference","name":"multi-framework-model-loading-and-inference","description":"Provides unified model loading across PyTorch, TensorFlow, JAX, and Rust through HuggingFace's transformers library and SafeTensors format. The model weights are stored in SafeTensors (a safe, fast binary format) enabling zero-copy loading and automatic framework detection. Supports lazy loading, quantization (int8, fp16), and distributed inference across multiple GPUs or TPUs through framework-native APIs.","intents":["Load and run inference in any deep learning framework without manual weight conversion or format translation","Deploy models to production environments with different framework preferences (PyTorch for research, TensorFlow for serving, JAX for research)","Optimize inference latency and memory usage through quantization and mixed-precision inference without retraining","Scale inference across multiple devices (multi-GPU, TPU) using framework-native distributed inference patterns"],"best_for":["Teams with heterogeneous ML stacks requiring framework-agnostic model deployment","Production systems requiring inference optimization (quantization, batching) without model retraining","Researchers experimenting across frameworks without manual weight conversion overhead"],"limitations":["SafeTensors format is read-only during inference — custom weight modifications require conversion back to framework-native formats","Quantization support varies by framework — int8 quantization available in PyTorch but not all TensorFlow backends","Distributed inference requires explicit framework configuration — no automatic multi-GPU orchestration; users must handle device placement","JAX implementation requires functional programming patterns unfamiliar to PyTorch/TensorFlow users; no automatic conversion of stateful code","Framework-specific optimizations (ONNX, TensorRT) require separate export and conversion steps; not built-in to transformers library"],"requires":["PyTorch 1.9+ OR TensorFlow 2.4+ OR JAX 0.2.0+ (at least one framework installed)","Transformers library 4.0+","SafeTensors library for efficient weight loading","2GB+ disk space for model weights","GPU drivers and CUDA 11.0+ for GPU inference (optional but recommended)"],"input_types":["raw text strings","pre-tokenized input_ids (integer tensors)","attention_mask tensors","token_type_ids for segment classification"],"output_types":["framework-native tensors (torch.Tensor, tf.Tensor, jax.Array)","logits, hidden_states, attention_weights","serialized predictions in JSON/NumPy format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_4","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding","name":"batch-inference-with-dynamic-padding","description":"Processes multiple variable-length sequences in a single forward pass using dynamic padding and attention masks to avoid unnecessary computation on padding tokens. The model automatically pads sequences to the longest length in the batch, applies attention masks to ignore padding positions, and uses efficient batched matrix operations to compute predictions for all sequences simultaneously. Supports configurable batch sizes and sequence truncation strategies.","intents":["Process large document collections efficiently by batching inference and minimizing padding overhead","Reduce per-sample inference latency through GPU parallelization across multiple sequences in a single batch","Handle variable-length inputs without manual padding logic or sequence length normalization","Optimize throughput for production serving systems handling concurrent requests"],"best_for":["Production NLP systems serving high-throughput inference requests (100+ sequences/second)","Batch processing pipelines for document analysis, classification, or embedding generation","Teams optimizing inference cost by maximizing GPU utilization through batching"],"limitations":["Batch size is constrained by GPU memory — larger batches require proportionally more VRAM; no automatic batch size tuning","Dynamic padding adds overhead for highly variable sequence lengths — if batch contains one 512-token sequence and 99 10-token sequences, all 100 are padded to 512","Attention mask computation adds ~5-10% overhead compared to fixed-length sequences; not negligible for very short sequences","Batch processing introduces latency variance — single-sample inference is faster than batched inference for latency-sensitive applications","No built-in request queuing or dynamic batching — requires external orchestration (Ray, TensorFlow Serving) for production deployment"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ with batching support","Transformers library 4.0+","GPU with 4GB+ VRAM for batch sizes ≥8; larger batches require 8GB+ VRAM","Tokenizer for converting text to input_ids and attention_mask"],"input_types":["list of text strings with variable lengths","pre-tokenized input_ids with variable sequence lengths","attention_mask tensors (automatically generated or user-provided)"],"output_types":["batched logits tensor (batch_size × sequence_length × vocab_size)","batched hidden_states (batch_size × sequence_length × 768)","batched predictions with confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_5","uri":"capability://planning.reasoning.model.interpretability.through.attention.visualization","name":"model-interpretability-through-attention-visualization","description":"Exposes attention weights from all 12 attention heads across 6 layers, enabling analysis of which input tokens the model attends to when making predictions. The model outputs attention_weights tensors (batch_size × num_heads × sequence_length × sequence_length) that can be visualized as heatmaps or aggregated to identify important token relationships. Supports attention head pruning analysis and layer-wise attention pattern inspection for model debugging and understanding.","intents":["Debug model predictions by visualizing which tokens the model attends to for masked token prediction","Analyze learned linguistic patterns (e.g., attention to subject-verb relationships, coreference resolution) without explicit supervision","Identify and remove redundant attention heads through pruning analysis to reduce model size","Explain model behavior to non-technical stakeholders through attention visualizations"],"best_for":["NLP researchers studying transformer behavior and learned linguistic patterns","Model debugging and error analysis workflows for production systems","Teams building explainable AI systems requiring interpretability for regulatory compliance"],"limitations":["Attention weights are not guaranteed to be interpretable — high attention to a token does not necessarily mean the model uses that token's information for prediction","Attention visualization is post-hoc and does not directly explain model decisions — requires additional analysis (gradient-based attribution, saliency maps) for true interpretability","Attention patterns are task-dependent — attention weights from masked language modeling may not transfer to downstream tasks","Visualization tools are not built-in — requires external libraries (BertViz, Exbert) for interactive exploration","Attention head analysis is computationally expensive for large models — requires storing and processing (batch_size × 12 × 512 × 512) tensors"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ with output_attentions=True parameter support","Transformers library 4.0+","Visualization library (BertViz, Matplotlib, Plotly) for rendering attention heatmaps","GPU with 2GB+ VRAM for batch inference with attention output"],"input_types":["raw text strings","pre-tokenized input_ids","attention_mask tensors"],"output_types":["attention_weights tensor (batch_size × num_heads × sequence_length × sequence_length)","aggregated attention heatmaps (sequence_length × sequence_length)","attention head importance scores"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_6","uri":"capability://automation.workflow.quantization.aware.inference.optimization","name":"quantization-aware-inference-optimization","description":"Supports inference-time quantization (int8, fp16) through PyTorch's quantization APIs and HuggingFace's quantization utilities, reducing model size by 75% (int8) and memory bandwidth requirements without retraining. The model can be quantized post-training using dynamic or static quantization, enabling deployment on memory-constrained devices. Quantized models maintain 95-99% of original accuracy for most NLP tasks while reducing inference latency by 2-4x on CPU and 1.5-2x on GPU.","intents":["Deploy models to edge devices (mobile, IoT) with limited memory and compute resources","Reduce inference latency and memory bandwidth for high-throughput serving systems","Optimize cost of cloud inference by reducing GPU memory requirements and enabling smaller instance types","Enable on-device inference for privacy-sensitive applications without sending data to servers"],"best_for":["Mobile and edge ML engineers deploying NLP models to resource-constrained devices","Production teams optimizing inference cost and latency for high-throughput systems","Privacy-focused applications requiring on-device inference without cloud connectivity"],"limitations":["Quantization is post-training and not fine-tuned for specific tasks — may degrade accuracy by 1-5% depending on task and quantization method","Dynamic quantization adds ~10-20% overhead per inference due to runtime quantization computation; static quantization requires calibration data","Quantized models are framework-specific — int8 quantization in PyTorch is not directly compatible with TensorFlow or ONNX without conversion","Limited support for quantization-aware training (QAT) in transformers library — requires custom implementation for optimal quantized accuracy","Quantization benefits vary by hardware — older CPUs may not have efficient int8 support; benefits are smaller on modern GPUs with native int8 operations"],"requires":["PyTorch 1.6+ with quantization support OR TensorFlow 2.5+ with quantization APIs","Transformers library 4.0+","Calibration dataset for static quantization (optional but recommended for accuracy)","Target hardware with int8 or fp16 support (most modern CPUs and GPUs)"],"input_types":["pre-trained model weights in fp32 format","calibration data (unlabeled text) for static quantization","inference inputs (text, input_ids, attention_mask)"],"output_types":["quantized model weights (int8 or fp16 format)","quantized predictions with reduced precision","quantization metrics (accuracy drop, latency improvement)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-distilbert--distilroberta-base__cap_7","uri":"capability://code.generation.editing.knowledge.distillation.from.roberta.base","name":"knowledge-distillation-from-roberta-base","description":"The model is a distilled version of RoBERTa-base created through knowledge distillation, where a smaller student model (6 layers, 82M parameters) learns to mimic the outputs of the larger teacher model (12 layers, 125M parameters) using a combination of MLM loss and distillation loss. The distillation process preserves 95-98% of the teacher's performance while reducing model size by 66% and inference latency by 40-50%, enabling efficient deployment without retraining on the original pretraining corpus.","intents":["Deploy RoBERTa-quality models with significantly reduced latency and memory requirements","Reduce training and inference costs by using smaller models that maintain competitive performance","Enable real-time inference on latency-sensitive applications (search, recommendation, chatbots) without sacrificing accuracy","Understand knowledge distillation techniques and their effectiveness for transformer compression"],"best_for":["Production teams requiring RoBERTa-quality performance with 40-50% latency reduction","Researchers studying knowledge distillation and model compression techniques","Teams with strict latency SLAs (e.g., <100ms per request) requiring efficient models"],"limitations":["Distillation is task-agnostic — performance gains are measured on MLM task; downstream task performance may differ from RoBERTa-base","Knowledge distillation requires access to teacher model outputs during training — cannot be applied to proprietary or closed-source models","Distilled models may have reduced capacity for complex linguistic phenomena — performance gaps appear on tasks requiring deep semantic understanding","Distillation hyperparameters (temperature, distillation weight) are not published — reproducing exact results requires experimentation","No fine-tuning guidance specific to distilled models — standard fine-tuning practices may not be optimal for compressed models"],"requires":["Understanding of knowledge distillation concepts (teacher-student training, KL divergence loss)","PyTorch 1.9+ or TensorFlow 2.4+ for inference","Transformers library 4.0+","GPU with 2GB+ VRAM for inference; CPU inference supported but slow"],"input_types":["text with [MASK] tokens for MLM evaluation","pre-tokenized sequences for downstream task evaluation"],"output_types":["MLM predictions (logits, probabilities)","hidden_states for downstream task fine-tuning","performance metrics (accuracy, F1, latency)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+ (framework-agnostic model weights in SafeTensors format)","Transformers library 4.0+","Minimum 2GB GPU VRAM for batch inference; CPU inference supported but ~10-50x slower","Hugging Face account or local model weights download (~270MB disk space)","PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+","Transformers library 4.0+ with output_hidden_states=True parameter support","GPU with 2GB+ VRAM for batch processing; CPU inference possible but slow","Vector database or similarity search library (FAISS, Annoy, Milvus) for large-scale retrieval","PyTorch 1.9+ or TensorFlow 2.4+ with training loop support","Transformers library 4.0+ with Trainer API or custom training code"],"failure_modes":["Requires explicit [MASK] token placement — cannot infer which tokens to predict without manual annotation","Bidirectional context means it cannot be used for autoregressive generation or next-token prediction tasks","Vocabulary is fixed at 50,265 tokens — out-of-vocabulary words are subword-tokenized, potentially degrading performance on rare technical terms","Maximum sequence length of 512 tokens limits applicability to long-document understanding without chunking strategies","No built-in uncertainty quantification — outputs softmax probabilities but not confidence intervals or calibration metrics","Embeddings are context-dependent — same token produces different vectors in different sentences, requiring full re-encoding for new contexts","768-dimensional vectors require significant memory for large-scale retrieval (e.g., 1M documents × 768 dims = ~3GB RAM minimum)","No built-in dimensionality reduction — downstream systems must handle high-dimensional vectors or apply PCA/UMAP separately","Embeddings are not normalized by default — cosine similarity requires explicit L2 normalization before comparison","Layer selection is manual — no automatic mechanism to determine optimal layer for specific downstream tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7223665796562138,"quality":0.26,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"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":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:56.133Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":1073316,"model_likes":177}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=distilbert--distilroberta-base","compare_url":"https://unfragile.ai/compare?artifact=distilbert--distilroberta-base"}},"signature":"fo5zSAhgtrR2rDRVcZ1NaI2DVYaS8qg5/f+Lpqsn2G3NLF699fUxmoVJvevEvBGZ1Kz+0miprtbG2S9a7HQZBA==","signedAt":"2026-06-19T19:10:43.280Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/distilbert--distilroberta-base","artifact":"https://unfragile.ai/distilbert--distilroberta-base","verify":"https://unfragile.ai/api/v1/verify?slug=distilbert--distilroberta-base","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"}}