{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-facebookai--xlm-roberta-large","slug":"facebookai--xlm-roberta-large","name":"xlm-roberta-large","type":"model","url":"https://huggingface.co/FacebookAI/xlm-roberta-large","page_url":"https://unfragile.ai/facebookai--xlm-roberta-large","categories":["research-search"],"tags":["transformers","pytorch","tf","jax","onnx","safetensors","xlm-roberta","fill-mask","exbert","multilingual","af","am","ar","as","az","be","bg","bn","br","bs"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-facebookai--xlm-roberta-large__cap_0","uri":"capability://text.generation.language.multilingual.masked.token.prediction.with.cross.lingual.transfer","name":"multilingual masked token prediction with cross-lingual transfer","description":"Predicts masked tokens across 101 languages using a 24-layer transformer encoder trained on 2.5TB of CommonCrawl data with XLM-R's unified vocabulary of 250K subword tokens. The model learns language-agnostic representations through masked language modeling (MLM) on parallel and monolingual corpora, enabling zero-shot cross-lingual transfer where predictions trained on one language generalize to unseen languages. Architecture uses absolute positional embeddings, 16 attention heads per layer, and 1024 hidden dimensions to capture both language-specific and universal linguistic patterns.","intents":["Fill in missing words in multilingual text for data augmentation or text completion tasks","Detect and correct spelling/grammar errors across 101 languages without language-specific models","Extract contextual word embeddings for downstream NLP tasks like classification or NER in low-resource languages","Perform zero-shot language transfer by leveraging representations learned from high-resource languages"],"best_for":["NLP researchers building multilingual systems without language-specific fine-tuning","Teams handling code-switched or low-resource language text (Amharic, Assamese, Azerbaijani, etc.)","Developers needing a single model to handle 101 languages instead of maintaining language-specific pipelines"],"limitations":["Inference latency ~150-300ms per sequence on CPU; requires GPU for batch processing of >32 sequences","Model size 560MB (fp32) or 280MB (fp16) — memory-intensive for edge deployment without quantization","Performance degrades on extremely low-resource languages (Breton, Basque) due to limited pretraining data representation","Masked token prediction requires contiguous context window; cannot predict tokens in very long documents (>512 tokens) without sliding window approach","No built-in support for domain-specific vocabulary — requires fine-tuning for specialized terminology (medical, legal, code)"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+","Transformers library 4.0+","4GB+ RAM for single-sequence inference; 8GB+ for batch processing","CUDA 11.0+ for GPU acceleration (optional but recommended)"],"input_types":["text (raw strings with [MASK] tokens indicating positions to predict)","tokenized sequences (input_ids, attention_mask, token_type_ids as PyTorch tensors or TensorFlow arrays)"],"output_types":["logits (batch_size × sequence_length × 250000 vocabulary probabilities)","predicted token IDs (batch_size × sequence_length)","contextual embeddings (batch_size × sequence_length × 1024 hidden dimensions)"],"categories":["text-generation-language","multilingual-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--xlm-roberta-large__cap_1","uri":"capability://data.processing.analysis.contextual.word.embedding.extraction.for.downstream.tasks","name":"contextual word embedding extraction for downstream tasks","description":"Extracts dense 1024-dimensional contextual embeddings from the final transformer layer for each input token, capturing semantic and syntactic information influenced by surrounding context. These embeddings can be used as input features for downstream tasks like named entity recognition, sentiment classification, or semantic similarity without task-specific fine-tuning. The embeddings are language-agnostic due to XLM-R's multilingual pretraining, allowing the same embedding space to represent semantically similar words across different languages.","intents":["Generate fixed-size vector representations of words/phrases for clustering or similarity search across languages","Use pretrained embeddings as frozen features for lightweight downstream classifiers (logistic regression, SVM) on low-resource languages","Build semantic search systems that match queries and documents across different languages in a unified embedding space","Detect semantic drift or word sense changes by comparing embeddings across different contexts"],"best_for":["Teams building multilingual semantic search or clustering without fine-tuning","Researchers studying cross-lingual word representations and language universals","Developers needing lightweight feature extraction for downstream ML pipelines on constrained hardware"],"limitations":["Embeddings are context-dependent; same word produces different vectors in different sentences, requiring careful aggregation for static word representations","1024-dimensional vectors require dimensionality reduction (PCA, UMAP) for efficient similarity search at scale (>1M documents)","Embedding quality varies by language; high-resource languages (English, Chinese) have better representations than low-resource languages (Breton, Assamese)","No built-in pooling strategy — requires custom logic to aggregate token embeddings into sentence/document representations"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","NumPy for embedding manipulation and similarity computation","Optional: scikit-learn for dimensionality reduction, FAISS for large-scale similarity search"],"input_types":["text (raw strings, max 512 tokens)","tokenized sequences (input_ids, attention_mask tensors)"],"output_types":["embeddings (batch_size × sequence_length × 1024 float32 arrays)","pooled embeddings (batch_size × 1024 for sentence-level representations via mean/max pooling)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--xlm-roberta-large__cap_2","uri":"capability://data.processing.analysis.language.detection.and.script.identification.via.embedding.space.geometry","name":"language detection and script identification via embedding space geometry","description":"Implicitly detects language and script through the learned embedding space geometry — tokens from the same language cluster together in the 1024-dimensional space due to multilingual pretraining. By analyzing the distribution of token embeddings or using a lightweight classifier trained on top of pooled embeddings, the model can identify which of 101 languages a text belongs to without explicit language classification layers. This works because XLM-R learns language-specific patterns during pretraining while maintaining a shared vocabulary.","intents":["Automatically detect the language of input text before routing to language-specific downstream models","Identify code-switched text (mixing multiple languages) by analyzing embedding clusters per token","Classify text into language families (Indo-European, Sino-Tibetan, Afro-Asiatic) based on embedding space structure","Handle multilingual input streams by detecting language boundaries without external language detection tools"],"best_for":["Multilingual NLP pipelines that need lightweight language detection without external libraries","Researchers studying language universals and cross-lingual linguistic structure","Systems processing user-generated content with unknown language composition"],"limitations":["Language detection is implicit and requires training a separate classifier on top of embeddings; no built-in language ID output","Accuracy degrades on code-switched text or text mixing scripts (Latin + Cyrillic) due to shared vocabulary","Cannot distinguish between closely related languages (e.g., Serbian vs Croatian) without fine-tuning","Requires labeled data to train language detection classifier; not zero-shot out of the box"],"requires":["PyTorch or TensorFlow for embedding extraction","Labeled dataset of texts in target languages for training language classifier (100-1000 examples per language recommended)","scikit-learn or similar for training lightweight classifier on embeddings"],"input_types":["text (raw strings or tokenized sequences)"],"output_types":["language embeddings (batch_size × 1024) that can be fed to downstream classifier","language probabilities (batch_size × 101) if classifier is trained"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--xlm-roberta-large__cap_3","uri":"capability://code.generation.editing.fine.tuning.for.task.specific.multilingual.adaptation","name":"fine-tuning for task-specific multilingual adaptation","description":"Supports efficient fine-tuning on downstream tasks (classification, NER, QA) across any of 101 languages by unfreezing transformer layers and training on task-specific labeled data. The model uses standard transformer fine-tuning patterns: task-specific head (linear layer for classification, CRF for sequence labeling) added on top of pretrained representations, optimized with cross-entropy loss or task-specific objectives. Fine-tuning leverages the multilingual pretraining as initialization, reducing data requirements for low-resource languages through transfer learning.","intents":["Adapt the model to domain-specific tasks (sentiment analysis, NER, question answering) in any of 101 languages with minimal labeled data","Build low-resource language NLP systems by fine-tuning on 100-1000 examples instead of training from scratch","Create language-specific classifiers that maintain cross-lingual knowledge from pretraining while specializing to task","Perform few-shot learning by fine-tuning on small labeled datasets (10-100 examples) in target language"],"best_for":["Teams building production NLP systems for low-resource languages without large labeled datasets","Researchers studying transfer learning and multilingual adaptation","Developers needing to customize the model for domain-specific terminology or tasks"],"limitations":["Fine-tuning requires labeled data; performance scales with dataset size (diminishing returns after 10K examples per language)","Catastrophic forgetting can occur if fine-tuning learning rate is too high; requires careful hyperparameter tuning (learning rate 1e-5 to 5e-5 recommended)","Fine-tuned models lose some cross-lingual transfer ability if trained only on single language; requires multi-task or multilingual fine-tuning to preserve transfer","Computational cost: fine-tuning on GPU takes 1-10 hours depending on dataset size and sequence length","No built-in support for continual learning; fine-tuning on new data can degrade performance on old tasks"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","GPU with 8GB+ VRAM (fine-tuning on CPU is impractical for sequences >128 tokens)","Labeled dataset in target language (100+ examples recommended for reasonable performance)","Optimizer (AdamW) and learning rate scheduler (linear warmup)"],"input_types":["labeled text data (input_ids, attention_mask, labels as tensors)","task-specific formats: (text, label) pairs for classification, (tokens, tags) for NER, (question, context, answer) for QA"],"output_types":["fine-tuned model weights (PyTorch .pt or TensorFlow SavedModel format)","task-specific predictions (class logits, sequence labels, answer spans)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--xlm-roberta-large__cap_4","uri":"capability://automation.workflow.model.export.and.deployment.across.frameworks.pytorch.tensorflow.jax.onnx","name":"model export and deployment across frameworks (pytorch, tensorflow, jax, onnx)","description":"Supports exporting the pretrained model to multiple deep learning frameworks and inference formats: native PyTorch (.pt), TensorFlow SavedModel, JAX pytree, and ONNX (Open Neural Network Exchange) for optimized inference. The Transformers library handles automatic conversion between formats, preserving model weights and architecture. ONNX export enables deployment on edge devices, mobile platforms, and inference servers (ONNX Runtime, TensorRT) with hardware-specific optimizations. SafeTensors format provides secure, fast serialization without arbitrary code execution risks.","intents":["Deploy the model to production inference servers (ONNX Runtime, TensorRT) with optimized performance for latency-critical applications","Export to mobile/edge devices (iOS, Android, embedded systems) using ONNX or quantized TensorFlow Lite format","Integrate with non-Python ML stacks (C++, Java, Go) via ONNX Runtime or TensorFlow Serving","Ensure reproducible, secure model distribution using SafeTensors format instead of pickle-based serialization"],"best_for":["ML engineers deploying models to production inference infrastructure","Teams building mobile or edge AI applications with strict latency/memory constraints","Organizations requiring secure model distribution without arbitrary code execution risks"],"limitations":["ONNX export may lose some dynamic control flow; models with conditional logic or variable sequence lengths require careful conversion","Framework-specific optimizations (e.g., TensorFlow XLA, PyTorch TorchScript) not automatically applied during export; requires separate optimization passes","Quantization (int8, fp16) requires separate tools (TensorRT, ONNX Runtime) and may reduce accuracy by 1-5% depending on quantization method","SafeTensors format is newer; some legacy tools may not support it without updates","Export process requires sufficient disk space (560MB for fp32, 280MB for fp16) and RAM to load full model"],"requires":["Transformers library 4.0+ with export utilities","PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+ (depending on target framework)","ONNX tools (onnx, onnxruntime) for ONNX export and validation","Optional: TensorRT for NVIDIA GPU optimization, CoreML tools for iOS export"],"input_types":["pretrained model weights (HuggingFace model ID or local checkpoint)","export configuration (target framework, precision, optimization flags)"],"output_types":["PyTorch model (.pt, .pth files)","TensorFlow SavedModel (directory with saved_model.pb and variables/)","ONNX model (.onnx file with embedded weights)","JAX pytree (PyTree structure with frozen parameters)","SafeTensors format (.safetensors file)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-facebookai--xlm-roberta-large__cap_5","uri":"capability://automation.workflow.quantization.and.model.compression.for.edge.deployment","name":"quantization and model compression for edge deployment","description":"Enables model compression through quantization (int8, fp16, dynamic quantization) and pruning to reduce model size from 560MB (fp32) to 140MB (int8) while maintaining 95-99% accuracy. Quantization reduces memory footprint and inference latency by 2-4x on CPU and 1.5-2x on GPU. The model can be quantized post-training using PyTorch's quantization API or ONNX Runtime's quantization tools without retraining. Supports both static quantization (requires calibration dataset) and dynamic quantization (no calibration needed).","intents":["Deploy the model on mobile devices (iOS, Android) with <200MB model size and <100ms inference latency","Run inference on edge devices (Raspberry Pi, IoT devices) with limited RAM (<2GB) and CPU-only constraints","Reduce model serving costs by 2-4x through smaller model size and faster inference in cloud deployments","Enable on-device inference for privacy-sensitive applications without sending data to servers"],"best_for":["Mobile app developers building on-device NLP features","IoT and edge AI teams with strict memory and latency constraints","Cost-conscious teams deploying models at scale in cloud environments"],"limitations":["Quantization accuracy loss: 1-5% F1 score degradation on downstream tasks depending on quantization method and dataset","Static quantization requires representative calibration dataset (100-1000 examples) to determine optimal quantization ranges","Dynamic quantization adds ~10-20% inference latency overhead on CPU due to runtime quantization/dequantization","Quantized models are framework-specific; int8 PyTorch model cannot be directly used in TensorFlow","Some operations (attention, layer norm) don't quantize well; may require mixed-precision quantization (int8 + fp16)","Quantization tools (TensorRT, ONNX Runtime) require additional setup and validation beyond standard model export"],"requires":["PyTorch 1.6+ (for native quantization) or ONNX Runtime 1.10+","Calibration dataset (100-1000 examples) for static quantization","Optional: TensorRT for NVIDIA GPU quantization, CoreML tools for iOS","Validation dataset to measure accuracy loss after quantization"],"input_types":["pretrained model (PyTorch or ONNX format)","calibration dataset (text examples for determining quantization ranges)","quantization configuration (bit-width, method: static/dynamic, per-channel/per-tensor)"],"output_types":["quantized model (int8 PyTorch model, ONNX int8 model, or TensorFlow Lite format)","quantization report (accuracy metrics, model size reduction, latency improvement)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":51,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+","Transformers library 4.0+","4GB+ RAM for single-sequence inference; 8GB+ for batch processing","CUDA 11.0+ for GPU acceleration (optional but recommended)","PyTorch 1.9+ or TensorFlow 2.4+","NumPy for embedding manipulation and similarity computation","Optional: scikit-learn for dimensionality reduction, FAISS for large-scale similarity search","PyTorch or TensorFlow for embedding extraction","Labeled dataset of texts in target languages for training language classifier (100-1000 examples per language recommended)","scikit-learn or similar for training lightweight classifier on embeddings"],"failure_modes":["Inference latency ~150-300ms per sequence on CPU; requires GPU for batch processing of >32 sequences","Model size 560MB (fp32) or 280MB (fp16) — memory-intensive for edge deployment without quantization","Performance degrades on extremely low-resource languages (Breton, Basque) due to limited pretraining data representation","Masked token prediction requires contiguous context window; cannot predict tokens in very long documents (>512 tokens) without sliding window approach","No built-in support for domain-specific vocabulary — requires fine-tuning for specialized terminology (medical, legal, code)","Embeddings are context-dependent; 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