{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-google-bert--bert-large-uncased","slug":"google-bert--bert-large-uncased","name":"bert-large-uncased","type":"model","url":"https://huggingface.co/google-bert/bert-large-uncased","page_url":"https://unfragile.ai/google-bert--bert-large-uncased","categories":["research-search"],"tags":["transformers","pytorch","tf","jax","rust","safetensors","bert","fill-mask","en","dataset:bookcorpus","dataset:wikipedia","arxiv:1810.04805","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-google-bert--bert-large-uncased__cap_0","uri":"capability://text.generation.language.masked.language.model.token.prediction.via.bidirectional.transformer.attention","name":"masked language model token prediction via bidirectional transformer attention","description":"Predicts masked tokens in text sequences using a 24-layer bidirectional transformer architecture trained on 110M parameters. The model processes entire input sequences simultaneously through multi-head self-attention (16 heads, 1024 hidden dimensions), enabling context-aware predictions that consider both left and right context. Implements WordPiece tokenization with a 30,522-token vocabulary and absolute position embeddings, allowing it to disambiguate token predictions based on syntactic and semantic context from the full sequence.","intents":["I need to predict what word should fill a [MASK] token in a sentence for data augmentation or text completion tasks","I want to generate multiple plausible token candidates ranked by confidence for a masked position","I need to extract contextual embeddings for downstream NLP tasks like classification or semantic similarity"],"best_for":["NLP researchers and practitioners building text understanding pipelines","Teams implementing data augmentation for low-resource language tasks","Developers creating semantic search or text similarity systems via embedding extraction"],"limitations":["Maximum sequence length of 512 tokens — longer documents require chunking or truncation","Uncased variant loses capitalization information, reducing effectiveness for proper noun disambiguation","Prediction quality degrades with multiple consecutive masked tokens (>3-4 masks per sequence)","No native support for non-English languages despite multilingual BERT variants existing","Inference latency ~50-100ms per sequence on CPU, requires GPU for batch processing >32 sequences"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ or JAX (framework-specific)","transformers library 4.0+","4GB+ RAM for model weights (3.4GB for full model in fp32)","Optional: CUDA 11.0+ for GPU acceleration"],"input_types":["raw text strings with [MASK] tokens","tokenized sequences (token IDs)","attention masks for variable-length batches"],"output_types":["logits (raw prediction scores) for all 30,522 vocabulary tokens","probability distributions over vocabulary","top-k token predictions with confidence scores","contextual embeddings (hidden states from any layer)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_1","uri":"capability://data.processing.analysis.contextual.embedding.extraction.for.semantic.representation","name":"contextual embedding extraction for semantic representation","description":"Extracts dense vector representations (embeddings) from any layer of the transformer stack, capturing semantic and syntactic information about tokens and sequences. The model produces 1024-dimensional embeddings per token by passing inputs through the full 24-layer transformer, with each layer progressively refining representations through attention mechanisms. Supports extraction from intermediate layers (e.g., layer 12 for lighter-weight embeddings) or the final layer for maximum semantic richness, enabling downstream tasks like clustering, similarity matching, or feature engineering.","intents":["I need dense vector representations of text for semantic similarity or clustering tasks","I want to extract sentence-level embeddings by pooling token representations for document-level tasks","I need to compare semantic similarity between two text passages using cosine distance in embedding space"],"best_for":["ML engineers building semantic search or recommendation systems","Data scientists performing text clustering or dimensionality reduction","Teams implementing retrieval-augmented generation (RAG) with vector databases"],"limitations":["Embeddings are 1024-dimensional, requiring dimensionality reduction for efficient storage in vector databases (adds ~5-10ms latency)","No built-in pooling strategy — requires manual mean/max pooling or [CLS] token extraction for sentence embeddings","Embeddings are not normalized by default, requiring L2 normalization for cosine similarity calculations","Context window of 512 tokens limits representation quality for long documents","Uncased preprocessing removes case information, reducing effectiveness for case-sensitive similarity tasks"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ RAM for model loading","Vector database or similarity computation library (e.g., faiss, annoy, or numpy for small-scale use)"],"input_types":["raw text strings","tokenized sequences with token IDs","attention masks for variable-length batches"],"output_types":["token-level embeddings (sequence_length × 1024)","pooled sentence embeddings (1 × 1024)","layer-specific embeddings from intermediate transformer layers"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_2","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.padding.and.attention.masking","name":"batch inference with dynamic padding and attention masking","description":"Processes variable-length text sequences in batches with automatic padding and attention masking to prevent the model from attending to padding tokens. 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Attention masks are automatically generated to zero out gradients and attention weights for padding positions, ensuring predictions are unaffected by artificial padding tokens.","intents":["I need to process multiple text sequences of different lengths efficiently without padding all to 512 tokens","I want to run inference on large datasets with minimal memory overhead using batching","I need to ensure padding tokens don't influence model predictions or embeddings"],"best_for":["Data engineers processing large text corpora for embedding extraction","ML practitioners optimizing inference latency and memory usage","Teams deploying BERT in production with variable-length input streams"],"limitations":["Dynamic padding adds tokenization overhead (~5-10ms per batch) compared to fixed-size padding","Batch size is limited by GPU memory (typically 32-128 on consumer GPUs, 256-512 on A100s)","Attention masking computation adds ~2-5% overhead per forward pass","No built-in support for distributed inference across multiple GPUs without external frameworks (e.g., Hugging Face Accelerate)","Padding strategy doesn't optimize for hardware-specific tensor operations (e.g., NVIDIA TensorRT requires fixed shapes)"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","GPU with CUDA 11.0+ for batch inference >64 sequences (CPU inference viable for small batches)","Optional: Hugging Face Accelerate for distributed inference"],"input_types":["list of text strings with variable lengths","pre-tokenized sequences","attention masks (optional, auto-generated)"],"output_types":["batched logits (batch_size × sequence_length × 30522)","batched embeddings (batch_size × sequence_length × 1024)","attention weights from intermediate layers"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_3","uri":"capability://tool.use.integration.multi.framework.model.export.and.inference.pytorch.tensorflow.jax.rust","name":"multi-framework model export and inference (pytorch, tensorflow, jax, rust)","description":"Provides pre-trained weights compatible with PyTorch, TensorFlow, JAX, and Rust ecosystems through the transformers library's unified model interface. The model can be loaded and executed in any framework without manual weight conversion, with automatic architecture mapping between frameworks. Supports SafeTensors format for secure, efficient weight loading with built-in integrity verification, and enables framework-specific optimizations (e.g., TensorFlow's graph mode, JAX's JIT compilation, Rust's WASM deployment).","intents":["I need to use BERT in a TensorFlow/Keras pipeline without retraining or manual weight conversion","I want to deploy BERT in a Rust application or WebAssembly environment for edge inference","I need to leverage JAX's JIT compilation and automatic differentiation for custom training or fine-tuning workflows"],"best_for":["Teams with heterogeneous ML stacks (PyTorch for research, TensorFlow for production)","Developers building edge inference applications in Rust or WebAssembly","Researchers using JAX for advanced optimization or custom training loops"],"limitations":["Framework-specific optimizations require separate code paths (e.g., TensorFlow graph mode vs eager execution)","JAX implementation requires manual batching and JIT compilation setup, adding complexity","Rust bindings are community-maintained and lag behind PyTorch/TensorFlow in feature parity","SafeTensors format is newer and not all legacy tools support it (requires transformers 4.30+)","Cross-framework weight conversion may introduce numerical precision differences (fp32 vs fp16 handling)"],"requires":["Python 3.7+ (for model loading and conversion)","PyTorch 1.9+ OR TensorFlow 2.4+ OR JAX 0.3+ (framework-specific)","transformers library 4.0+","For Rust: rust-bert crate or manual ONNX export","For WebAssembly: ONNX Runtime Web or Hugging Face.js"],"input_types":["framework-agnostic text inputs (converted to framework-specific tensors internally)","pre-tokenized token IDs","attention masks"],"output_types":["framework-specific tensors (torch.Tensor, tf.Tensor, jax.Array, etc.)","logits and embeddings in native framework format"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_4","uri":"capability://code.generation.editing.fine.tuning.on.downstream.nlp.tasks.with.transfer.learning","name":"fine-tuning on downstream nlp tasks with transfer learning","description":"Enables task-specific fine-tuning by adding lightweight task heads (classification, token classification, question-answering) on top of frozen or partially-frozen BERT layers. The model uses transfer learning to adapt pretrained representations to downstream tasks with minimal labeled data (typically 100-1000 examples), leveraging the rich linguistic knowledge from pretraining on BookCorpus + Wikipedia. Supports parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) or adapter modules to reduce trainable parameters from 110M to 0.1-1M while maintaining performance.","intents":["I need to adapt BERT to a custom text classification task with limited labeled data","I want to fine-tune BERT for named entity recognition or token-level tagging without retraining from scratch","I need to reduce fine-tuning memory and compute costs using parameter-efficient methods like LoRA"],"best_for":["ML practitioners with domain-specific NLP tasks and limited labeled data (100-10K examples)","Teams optimizing fine-tuning costs and latency for production deployment","Researchers exploring transfer learning and domain adaptation in NLP"],"limitations":["Fine-tuning requires labeled data — performance degrades significantly with <50 examples per class","Task-specific heads must be manually designed and integrated (no automatic architecture inference)","LoRA/adapter fine-tuning adds inference latency (~5-10%) due to additional matrix multiplications","Catastrophic forgetting risk if learning rate is too high — requires careful hyperparameter tuning","No built-in support for multi-task fine-tuning or continual learning scenarios","Fine-tuning on GPU requires 8-16GB VRAM for batch size 16-32 with full model training"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","transformers library 4.0+","Labeled dataset (minimum 50-100 examples per class for reasonable performance)","GPU with 8GB+ VRAM for efficient fine-tuning (CPU fine-tuning viable for small datasets)","Optional: peft library for LoRA/adapter fine-tuning"],"input_types":["labeled text examples with task-specific annotations (labels, entity tags, answer spans)","task-specific input formats (single text for classification, text pairs for similarity)"],"output_types":["fine-tuned model weights (3.4GB for full model, 10-50MB for LoRA adapters)","task-specific predictions (class labels, entity tags, answer spans)"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_5","uri":"capability://text.generation.language.multilingual.and.cross.lingual.transfer.via.language.agnostic.representations","name":"multilingual and cross-lingual transfer via language-agnostic representations","description":"While the base model is English-only (uncased), the architecture and pretraining approach enable transfer to other languages through fine-tuning or use of multilingual BERT variants (mBERT, XLM-RoBERTa). The bidirectional transformer architecture and WordPiece tokenization are language-agnostic, allowing the learned attention patterns and layer representations to generalize across languages when fine-tuned on non-English data. Zero-shot cross-lingual transfer is possible by fine-tuning on one language and evaluating on another, leveraging shared embedding spaces.","intents":["I need to adapt BERT to non-English languages by fine-tuning on language-specific data","I want to perform zero-shot cross-lingual transfer by fine-tuning on English and evaluating on other languages","I need to understand how BERT's architecture enables language transfer without explicit multilingual pretraining"],"best_for":["NLP practitioners working with non-English languages who want to leverage English pretraining","Researchers studying cross-lingual transfer and language-agnostic representations","Teams building multilingual systems with limited non-English labeled data"],"limitations":["English-only pretraining limits zero-shot performance on distant languages (e.g., English→Chinese)","WordPiece tokenization is English-optimized, resulting in subword fragmentation for morphologically rich languages","No built-in support for language-specific preprocessing (e.g., stemming, diacritics handling)","Cross-lingual transfer performance degrades with linguistic distance (Germanic languages >Romance >Slavic >Asian)","Requires fine-tuning on target language data for competitive performance — zero-shot transfer typically 10-20% lower accuracy"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Labeled data in target language (minimum 100-500 examples for reasonable fine-tuning)","Optional: multilingual BERT variants (mBERT, XLM-RoBERTa) for better multilingual coverage"],"input_types":["text in non-English languages","language-specific tokenization (optional, handled by WordPiece tokenizer)"],"output_types":["language-agnostic embeddings and predictions","cross-lingual similarity scores"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_6","uri":"capability://tool.use.integration.integration.with.hugging.face.hub.ecosystem.model.versioning.inference.apis.model.cards","name":"integration with hugging face hub ecosystem (model versioning, inference apis, model cards)","description":"Fully integrated with Hugging Face Hub, providing model versioning, automatic inference API endpoints, and standardized model cards with documentation. The model supports one-click deployment to Hugging Face Inference API (serverless endpoints with auto-scaling), integration with Hugging Face Spaces for interactive demos, and automatic model card generation with usage examples and benchmark results. Version control via Git-based model repositories enables reproducibility and collaborative model development.","intents":["I need to deploy BERT inference without managing servers or containers","I want to create an interactive demo or API endpoint for BERT without DevOps overhead","I need to version and track model changes, fine-tuned variants, and performance metrics"],"best_for":["Teams without DevOps infrastructure seeking quick model deployment","Researchers sharing models and results with standardized documentation","Startups prototyping NLP applications with minimal infrastructure overhead"],"limitations":["Hugging Face Inference API has rate limits (100 requests/minute on free tier) and latency (500-2000ms per request)","Vendor lock-in to Hugging Face ecosystem — models require export for deployment elsewhere","Model cards are community-maintained and may contain outdated or inaccurate information","No built-in monitoring, logging, or analytics for production inference","Cold start latency (2-5 seconds) for serverless endpoints due to model loading"],"requires":["Hugging Face account (free tier available)","transformers library 4.0+ for local inference","Optional: Hugging Face CLI for model management and deployment"],"input_types":["text inputs via REST API or Python SDK","structured JSON payloads with model parameters"],"output_types":["JSON responses with logits, embeddings, or predictions","streaming responses for long-running inference"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_7","uri":"capability://text.generation.language.question.answering.via.extractive.span.selection.from.context","name":"question-answering via extractive span selection from context","description":"Enables extractive question-answering by fine-tuning BERT to predict start and end token positions of answer spans within a given context passage. The model learns to identify which tokens in the context correspond to the answer through two classification heads (start position and end position logits), leveraging bidirectional context to disambiguate answer boundaries. This approach is efficient and interpretable compared to generative QA, as answers are directly extracted from the provided context without hallucination risk.","intents":["I need to build a QA system that extracts answers from provided documents or passages","I want to implement reading comprehension evaluation on datasets like SQuAD","I need to find relevant answer spans in long documents without generating new text"],"best_for":["Teams building document-based QA systems with reference passages","Researchers evaluating reading comprehension models on benchmarks like SQuAD","Applications requiring interpretable answers (answer spans are directly traceable to source)"],"limitations":["Extractive QA requires answer to be present in context — cannot handle questions requiring reasoning or synthesis","Performance degrades with long contexts (>512 tokens) due to sequence length limit","Requires fine-tuning on QA-specific datasets (e.g., SQuAD) — zero-shot performance is poor","Answer span boundaries may be misaligned with semantic units (e.g., partial entity names)","No support for multi-hop reasoning or questions requiring information from multiple passages"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","QA-specific fine-tuning dataset (e.g., SQuAD, MS MARCO, or custom labeled data)","GPU with 8GB+ VRAM for efficient fine-tuning"],"input_types":["context passages (text up to 512 tokens)","questions (text)","answer spans (start/end token positions for training)"],"output_types":["start and end position logits for each token","predicted answer spans with confidence scores","top-k candidate answers ranked by probability"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-large-uncased__cap_8","uri":"capability://text.generation.language.semantic.similarity.and.paraphrase.detection.via.embedding.comparison","name":"semantic similarity and paraphrase detection via embedding comparison","description":"Computes semantic similarity between text pairs by extracting embeddings and computing cosine distance in the 1024-dimensional embedding space. The model can be fine-tuned on sentence-pair datasets (e.g., STS Benchmark, MRPC) to learn similarity-aware representations, or used zero-shot by pooling token embeddings and comparing with cosine similarity. This enables paraphrase detection, duplicate detection, and semantic textual similarity tasks without explicit classification heads.","intents":["I need to detect paraphrases or duplicate text without training a classifier","I want to compute semantic similarity scores between sentence pairs for ranking or clustering","I need to identify semantically similar documents in a corpus for deduplication or recommendation"],"best_for":["Teams building deduplication or plagiarism detection systems","Practitioners implementing semantic search or document clustering","Researchers evaluating semantic textual similarity on benchmarks like STS"],"limitations":["Zero-shot similarity performance is moderate (0.60-0.70 Spearman correlation on STS) without fine-tuning","Requires manual pooling strategy (mean, max, [CLS]) — no automatic optimal pooling","Embeddings are not normalized by default, requiring L2 normalization for cosine similarity","Similarity thresholds must be manually tuned per application (no universal threshold)","Context window of 512 tokens limits representation quality for long documents"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ or TensorFlow 2.4+","Optional: sentence-transformers library for optimized similarity computation","Optional: fine-tuning dataset (e.g., STS Benchmark, MRPC) for task-specific adaptation"],"input_types":["text pairs (two sentences or passages)","single texts for embedding extraction"],"output_types":["similarity scores (0-1 cosine similarity)","embeddings for manual similarity computation","ranked lists of similar documents"],"categories":["text-generation-language","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+ or JAX (framework-specific)","transformers library 4.0+","4GB+ RAM for model weights (3.4GB for full model in fp32)","Optional: CUDA 11.0+ for GPU acceleration","PyTorch 1.9+ or TensorFlow 2.4+","4GB+ RAM for model loading","Vector database or similarity computation library (e.g., faiss, annoy, or numpy for small-scale use)","GPU with CUDA 11.0+ for batch inference >64 sequences (CPU inference viable for small batches)","Optional: Hugging Face Accelerate for distributed inference"],"failure_modes":["Maximum sequence length of 512 tokens — longer documents require chunking or truncation","Uncased variant loses capitalization information, reducing effectiveness for proper noun disambiguation","Prediction quality degrades with multiple consecutive masked tokens (>3-4 masks per sequence)","No native support for non-English languages despite multilingual BERT variants existing","Inference latency ~50-100ms per sequence on CPU, requires GPU for batch processing >32 sequences","Embeddings are 1024-dimensional, requiring dimensionality reduction for efficient storage in vector databases (adds ~5-10ms latency)","No built-in pooling strategy — requires manual mean/max pooling or [CLS] token extraction for sentence embeddings","Embeddings are not normalized by default, requiring L2 normalization for cosine similarity calculations","Context window of 512 tokens limits representation quality for long documents","Uncased preprocessing removes case information, reducing effectiveness for case-sensitive similarity tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7212967170332167,"quality":0.28,"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":1120072,"model_likes":147}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-bert--bert-large-uncased","compare_url":"https://unfragile.ai/compare?artifact=google-bert--bert-large-uncased"}},"signature":"JqcSe6S2Vf2J5tlWUXkAzsqz76Gn1xw+EodVwNJFfAH30rsGWHAAe7Ku0sVZz4L3rx0cY5EKvpPZcLbdUQudBg==","signedAt":"2026-06-19T17:00:06.427Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-bert--bert-large-uncased","artifact":"https://unfragile.ai/google-bert--bert-large-uncased","verify":"https://unfragile.ai/api/v1/verify?slug=google-bert--bert-large-uncased","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"}}