{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebookai--roberta-large","slug":"facebookai--roberta-large","name":"roberta-large","type":"model","url":"https://huggingface.co/FacebookAI/roberta-large","page_url":"https://unfragile.ai/facebookai--roberta-large","categories":["research-search"],"tags":["transformers","pytorch","tf","jax","onnx","safetensors","roberta","fill-mask","exbert","en","dataset:bookcorpus","dataset:wikipedia","arxiv:1907.11692","arxiv:1806.02847","license:mit","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-facebookai--roberta-large__cap_0","uri":"capability://text.generation.language.masked.language.model.token.prediction.with.bidirectional.context","name":"masked language model token prediction with bidirectional context","description":"Predicts masked tokens in text by processing the entire input sequence bidirectionally through 24 transformer layers (355M parameters), learning contextual representations from both left and right context simultaneously. Uses RoBERTa's improved BERT pretraining approach with dynamic masking, larger batch sizes, and extended training on BookCorpus + Wikipedia to generate probability distributions over the vocabulary for masked positions. Outputs top-k token predictions with confidence scores via the fill-mask pipeline.","intents":["I need to predict what word should fill a [MASK] token in a sentence for data augmentation or text completion","I want to identify the most likely tokens that could replace a masked position to understand contextual word relationships","I need to generate multiple plausible completions for a masked span to evaluate semantic coherence","I want to use masked prediction as a feature for downstream NLP tasks like semantic similarity or paraphrase detection"],"best_for":["NLP researchers prototyping fill-mask applications without training custom models","teams building text augmentation pipelines for data-scarce domains","developers implementing semantic search or entity linking systems that need contextual token understanding","builders creating interactive text editing tools that suggest contextually appropriate word replacements"],"limitations":["Requires explicit [MASK] token placement in input — cannot infer which positions should be masked from raw text","Vocabulary limited to 50,265 tokens from RoBERTa's BPE tokenizer — cannot predict out-of-vocabulary subword combinations","Bidirectional context means it cannot be used for true left-to-right generation or causal language modeling tasks","Inference latency ~100-200ms per sequence on CPU, requires GPU for batch processing >32 sequences efficiently","Maximum sequence length 512 tokens — longer documents must be chunked, losing cross-chunk context","English-only model — no multilingual support despite BERT-multilingual alternatives existing"],"requires":["transformers library >= 4.0 (HuggingFace)","PyTorch >= 1.9 OR TensorFlow >= 2.4 OR JAX (framework choice)","~1.4 GB GPU memory for inference, ~4 GB for batch processing","Python 3.7+","Optional: ONNX Runtime for optimized CPU inference"],"input_types":["text (string with [MASK] tokens)","tokenized sequences (input_ids, attention_mask, token_type_ids as tensors)"],"output_types":["structured predictions: list of dicts with 'sequence', 'score', 'token', 'token_str' for each top-k result","logits tensor (batch_size, sequence_length, vocab_size) for custom post-processing"],"categories":["text-generation-language","nlp-pretraining"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--roberta-large__cap_1","uri":"capability://memory.knowledge.transfer.learning.via.frozen.embeddings.and.fine.tuning","name":"transfer learning via frozen embeddings and fine-tuning","description":"Exposes pretrained transformer weights (all 24 layers, 355M parameters) that can be frozen or selectively unfrozen for downstream task adaptation. Supports parameter-efficient fine-tuning through LoRA, adapter modules, or full gradient-based optimization by integrating with HuggingFace's Trainer API. Weights are distributed in multiple formats (PyTorch .bin, TensorFlow SavedModel, JAX, ONNX, safetensors) enabling framework-agnostic transfer learning across research and production environments.","intents":["I want to fine-tune RoBERTa on my domain-specific corpus (legal documents, biomedical text) while keeping most weights frozen to reduce training time","I need to adapt this model for a classification task (sentiment, intent detection) with minimal labeled data by leveraging pretrained representations","I want to export the model to ONNX or TensorFlow for deployment in production systems that don't use PyTorch","I need to apply parameter-efficient fine-tuning (LoRA) to reduce memory footprint when fine-tuning on consumer GPUs"],"best_for":["ML engineers building domain-specific NLP classifiers with limited labeled data (100-10K examples)","researchers comparing transfer learning effectiveness across different downstream tasks","teams deploying models to heterogeneous inference environments (mobile, edge, cloud with different frameworks)","practitioners optimizing for GPU memory constraints during fine-tuning on 8GB-16GB consumer hardware"],"limitations":["Fine-tuning on small datasets (<1K examples) risks overfitting despite pretrained initialization — requires careful regularization","No built-in support for continual learning or catastrophic forgetting mitigation — sequential fine-tuning on multiple tasks degrades performance","Cross-framework conversion (PyTorch → TensorFlow) may introduce numerical precision differences (float32 vs float16 handling)","LoRA/adapter fine-tuning adds inference latency (~5-10%) due to additional matrix multiplications during forward pass","Pretrained weights are English-only — transfer to non-English tasks requires additional language-specific pretraining or multilingual model alternatives"],"requires":["transformers >= 4.0 with Trainer API support","PyTorch >= 1.9 (for fine-tuning) OR TensorFlow >= 2.4 (for TF SavedModel)","8GB+ GPU memory for full fine-tuning, 4GB+ for LoRA fine-tuning","peft library >= 0.4 (for LoRA/adapter support)","Optional: onnx, onnxruntime for model conversion and inference"],"input_types":["raw text (auto-tokenized by pipeline)","pretokenized sequences (input_ids, attention_mask, token_type_ids)","PyTorch/TensorFlow datasets or HuggingFace Dataset objects"],"output_types":["fine-tuned model weights (PyTorch .bin, TensorFlow SavedModel, ONNX, safetensors)","training metrics (loss, accuracy, F1, per-epoch validation scores)","adapter weights (LoRA .safetensors) for parameter-efficient storage"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--roberta-large__cap_2","uri":"capability://data.processing.analysis.semantic.representation.extraction.for.downstream.embeddings","name":"semantic representation extraction for downstream embeddings","description":"Extracts dense vector representations (embeddings) from intermediate transformer layers by pooling token outputs (mean pooling, CLS token, or max pooling) to create fixed-size vectors (1024-dim for large variant) that capture semantic meaning. These representations can be used directly for similarity search, clustering, or as input features to lightweight downstream models. Supports layer-wise extraction (access any of 24 layers) enabling analysis of how semantic information evolves through the network depth.","intents":["I need to convert text documents into dense vectors for semantic search or similarity matching without training a separate embedding model","I want to extract contextual word embeddings that capture meaning beyond static word2vec-style representations","I need to analyze how semantic information is encoded across different transformer layers to understand model behavior","I want to use RoBERTa embeddings as features for lightweight downstream classifiers (logistic regression, SVM) on new tasks"],"best_for":["teams building semantic search systems over document collections without dedicated embedding model training","researchers analyzing transformer internals and probing for linguistic knowledge encoded in different layers","practitioners creating lightweight downstream classifiers that leverage pretrained representations","builders implementing similarity-based recommendation systems or duplicate detection without retraining"],"limitations":["Mean-pooled embeddings lose positional information — not ideal for tasks requiring fine-grained token-level semantics","1024-dimensional vectors require more storage and compute than smaller embeddings (384-dim from DistilBERT) for large-scale retrieval","No built-in normalization or dimensionality reduction — requires manual L2 normalization for cosine similarity or PCA for compression","Extraction requires full forward pass through all 24 layers (~100-200ms per sequence) — slower than models optimized for embedding generation","Embeddings are not trained for semantic similarity tasks — may underperform compared to sentence-transformers fine-tuned on NLI/STS data"],"requires":["transformers >= 4.0","PyTorch >= 1.9 OR TensorFlow >= 2.4","4GB+ GPU memory for batch embedding extraction","Optional: scikit-learn for dimensionality reduction, faiss for similarity search"],"input_types":["text (strings, auto-tokenized)","pretokenized sequences (input_ids, attention_mask, token_type_ids)","batches of documents (list of strings or Dataset objects)"],"output_types":["dense vectors (torch.Tensor or tf.Tensor, shape: batch_size × 1024)","layer-wise embeddings (dict mapping layer_idx → embeddings)","numpy arrays for downstream ML pipelines"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--roberta-large__cap_3","uri":"capability://automation.workflow.multi.framework.model.serialization.and.deployment","name":"multi-framework model serialization and deployment","description":"Distributes pretrained weights in 5 serialization formats (PyTorch .bin, TensorFlow SavedModel, JAX, ONNX, safetensors) with automatic format detection and conversion via transformers library. Enables deployment across heterogeneous inference environments: PyTorch for research, TensorFlow for production ML pipelines, ONNX for edge/mobile via ONNX Runtime, and safetensors for secure weight loading without arbitrary code execution. Each format maintains numerical equivalence (within float32 precision) across frameworks.","intents":["I need to deploy RoBERTa in a TensorFlow-based production system but want to leverage PyTorch-optimized training","I want to run inference on edge devices or mobile using ONNX Runtime without PyTorch dependencies","I need to load model weights securely without executing arbitrary Python code (safetensors advantage)","I want to benchmark inference performance across frameworks (PyTorch, TensorFlow, ONNX) to choose the fastest for my hardware"],"best_for":["ML ops teams managing multi-framework production systems (PyTorch training, TensorFlow serving)","edge/mobile developers deploying models with minimal dependencies via ONNX Runtime","security-conscious teams requiring safe weight loading without pickle/arbitrary code execution","researchers comparing inference performance across frameworks on specific hardware (GPU, CPU, TPU)"],"limitations":["ONNX conversion requires opset version compatibility — older ONNX Runtime versions may not support all RoBERTa operations","JAX format requires jax >= 0.3 and jit compilation overhead on first inference (~500ms warmup)","TensorFlow SavedModel conversion may introduce float32 ↔ float16 precision mismatches in mixed-precision inference","safetensors format is read-only — cannot modify weights in-place without converting back to PyTorch","Cross-framework conversion adds ~5-10% numerical drift in edge cases due to different implementations of layer normalization and attention"],"requires":["transformers >= 4.0 with auto_model_for_masked_lm support","PyTorch >= 1.9 (for .bin format)","TensorFlow >= 2.4 (for SavedModel format)","onnx >= 1.12 and onnxruntime >= 1.13 (for ONNX format)","jax >= 0.3 (for JAX format)","safetensors >= 0.3 (for safetensors format)"],"input_types":["pretrained model weights (from HuggingFace Hub or local cache)","framework-specific model objects (AutoModelForMaskedLM, tf.keras.Model, etc.)"],"output_types":["serialized weights in target format (.bin, SavedModel, .onnx, .safetensors)","framework-specific model objects ready for inference","inference graphs (ONNX) optimized for specific hardware"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--roberta-large__cap_4","uri":"capability://planning.reasoning.attention.mechanism.visualization.and.interpretability","name":"attention mechanism visualization and interpretability","description":"Exposes attention weights from all 24 transformer layers and 16 attention heads per layer, enabling visualization of which input tokens the model attends to when processing each position. Supports extraction of attention patterns for interpretability analysis: head-level attention (which tokens does head i focus on), layer-level aggregation (average attention across heads), and full attention matrices (batch_size × num_heads × seq_len × seq_len). Integrates with exbert-style visualization tools for interactive exploration of learned attention patterns.","intents":["I want to understand which tokens the model attends to when predicting a masked position to debug unexpected predictions","I need to visualize attention patterns to identify if the model learns linguistic structure (e.g., subject-verb agreement, coreference)","I want to extract attention weights as features for probing tasks that test what linguistic knowledge is encoded in the model","I need to analyze failure cases by examining attention patterns when the model makes incorrect predictions"],"best_for":["NLP researchers studying transformer internals and linguistic knowledge encoded in attention","model interpretability practitioners building explainability systems for NLP models","teams debugging unexpected model behavior by analyzing attention patterns","educators teaching how transformers work by visualizing learned attention mechanisms"],"limitations":["Attention weights do not directly explain model predictions — high attention to a token doesn't guarantee it influences the output","Attention visualization is most interpretable for short sequences (<100 tokens) — longer sequences produce dense, hard-to-read attention matrices","Extracting attention for large batches requires significant GPU memory (~2GB for batch_size=32, seq_len=512)","Attention patterns are task-agnostic (learned during pretraining) — may not reflect task-specific reasoning for downstream applications","No built-in statistical significance testing — requires manual analysis to distinguish meaningful attention patterns from noise"],"requires":["transformers >= 4.0 with output_attentions=True support","PyTorch >= 1.9 OR TensorFlow >= 2.4","4GB+ GPU memory for batch attention extraction","Optional: matplotlib, seaborn for visualization; exbert-style tools for interactive exploration"],"input_types":["text (strings, auto-tokenized)","pretokenized sequences (input_ids, attention_mask, token_type_ids)"],"output_types":["attention tensors (batch_size × num_heads × seq_len × seq_len)","aggregated attention matrices (batch_size × seq_len × seq_len)","visualization-ready numpy arrays or matplotlib figures"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--roberta-large__cap_5","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding.and.sequence.bucketing","name":"batch inference with dynamic padding and sequence bucketing","description":"Processes multiple sequences of varying lengths in a single batch by dynamically padding to the longest sequence in the batch (not fixed 512 tokens) and applying attention masks to ignore padding tokens. Supports sequence bucketing (grouping sequences by length before batching) to minimize wasted computation on padding. Integrates with HuggingFace DataCollator for automatic batching in data loaders, and supports distributed inference via DistributedDataParallel (DDP) for multi-GPU processing of large document collections.","intents":["I need to process thousands of documents with varying lengths efficiently without padding all to 512 tokens","I want to maximize GPU utilization by batching sequences intelligently (bucketing by length) to reduce padding overhead","I need to run inference on multiple GPUs to process large document collections in parallel","I want to measure inference throughput (tokens/second) and optimize batch size for my hardware"],"best_for":["teams processing large document collections (100K+) for embedding extraction or classification","practitioners optimizing inference cost by reducing padding overhead in batch processing","ML engineers deploying inference pipelines on multi-GPU systems (2-8 GPUs)","researchers benchmarking inference performance across different batch sizes and sequence lengths"],"limitations":["Dynamic padding adds ~5-10ms overhead per batch for padding/unpadding operations","Sequence bucketing requires sorting documents by length, which may break original document order (requires post-processing to restore)","Distributed inference (DDP) requires synchronized batch processing across GPUs — cannot use variable batch sizes per GPU","Attention mask computation adds memory overhead (~10% for typical batches) compared to fixed-length sequences","Maximum sequence length still limited to 512 tokens — longer documents must be chunked, losing cross-chunk context"],"requires":["transformers >= 4.0 with DataCollatorWithPadding","PyTorch >= 1.9 with DistributedDataParallel support","torch.utils.data.DataLoader for batching","Optional: torch.nn.parallel.DistributedDataParallel for multi-GPU inference","2GB+ GPU memory per GPU for batch_size >= 32"],"input_types":["list of text strings (variable length)","HuggingFace Dataset objects with text column","pretokenized sequences with attention masks"],"output_types":["batched tensors (input_ids, attention_mask, token_type_ids)","model outputs (logits, embeddings) for each sequence in batch","inference metrics (throughput, latency, GPU utilization)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"high","permissions":["transformers library >= 4.0 (HuggingFace)","PyTorch >= 1.9 OR TensorFlow >= 2.4 OR JAX (framework choice)","~1.4 GB GPU memory for inference, ~4 GB for batch processing","Python 3.7+","Optional: ONNX Runtime for optimized CPU inference","transformers >= 4.0 with Trainer API support","PyTorch >= 1.9 (for fine-tuning) OR TensorFlow >= 2.4 (for TF SavedModel)","8GB+ GPU memory for full fine-tuning, 4GB+ for LoRA fine-tuning","peft library >= 0.4 (for LoRA/adapter support)","Optional: onnx, onnxruntime for model conversion and 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(PyTorch → TensorFlow) may introduce numerical precision differences (float32 vs float16 handling)","LoRA/adapter fine-tuning adds inference latency (~5-10%) due to additional matrix multiplications during forward pass","builder identity is not verified yet","no observed match outcomes 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