{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-google-bert--bert-base-uncased","slug":"google-bert--bert-base-uncased","name":"bert-base-uncased","type":"model","url":"https://huggingface.co/google-bert/bert-base-uncased","page_url":"https://unfragile.ai/google-bert--bert-base-uncased","categories":["model-training"],"tags":["transformers","pytorch","tf","jax","rust","coreml","onnx","safetensors","bert","fill-mask","exbert","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-base-uncased__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 sequences using a 12-layer bidirectional transformer encoder trained on 110M parameters. The model processes input text through WordPiece tokenization, learns contextual embeddings from both left and right context simultaneously, and outputs probability distributions over the 30,522-token vocabulary for each [MASK] position. Uses absolute positional embeddings and segment embeddings to encode sequence structure and sentence boundaries.","intents":["I need to fill in missing words in a sentence given surrounding context","I want to generate candidate tokens for a specific position in text","I need to understand what words are semantically plausible at a given location","I want to use a pre-trained model for downstream NLP tasks via fine-tuning"],"best_for":["NLP researchers prototyping language understanding tasks","teams building semantic search or entity linking systems","developers fine-tuning models for domain-specific text classification or NER","builders creating text augmentation or data cleaning pipelines"],"limitations":["Requires explicit [MASK] tokens in input — cannot predict arbitrary positions without modification","Fixed 512-token sequence length due to positional embedding design","Uncased variant loses capitalization information, reducing performance on tasks where case matters (named entities, acronyms)","Bidirectional context means it cannot be used for autoregressive generation without architectural changes","Trained on 2019 data (BookCorpus + Wikipedia) — lacks knowledge of recent events, terminology, or cultural references"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+ or JAX 0.2.0+","Transformers library 4.0+","Minimum 512MB RAM for inference, 2GB+ for fine-tuning","HuggingFace Hub access or local model weights (~440MB)"],"input_types":["raw text strings with [MASK] tokens","tokenized input_ids (integers 0-30521)","attention masks (binary tensors indicating valid tokens)","token_type_ids (segment embeddings for sentence pairs)"],"output_types":["logits tensor (batch_size, sequence_length, 30522)","probability distributions over vocabulary per masked position","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-google-bert--bert-base-uncased__cap_1","uri":"capability://data.processing.analysis.semantic.text.representation.via.contextual.embeddings","name":"semantic text representation via contextual embeddings","description":"Generates dense vector representations (768-dimensional) for input text by extracting hidden states from the final transformer layer or pooled [CLS] token. Each token receives a context-dependent embedding that captures semantic and syntactic information learned during pre-training on 3.3B tokens. Embeddings can be used for downstream tasks like semantic similarity, clustering, or as input features for classifiers without fine-tuning.","intents":["I need to convert text into fixed-size vectors for similarity comparison","I want to cluster documents or sentences based on semantic meaning","I need features for a text classification model without training from scratch","I want to find semantically similar passages in a corpus"],"best_for":["teams building semantic search or recommendation systems","researchers comparing text similarity across domains","developers creating document clustering pipelines","builders implementing zero-shot or few-shot learning with embeddings"],"limitations":["768-dimensional vectors require significant memory for large-scale similarity search (use quantization or approximate nearest neighbor indices)","Embeddings are task-agnostic — may not capture domain-specific semantics without fine-tuning","No built-in normalization — cosine similarity requires manual L2 normalization","Sequence length capped at 512 tokens — longer documents must be chunked or truncated","Uncased model conflates 'Apple' (company) and 'apple' (fruit) in embeddings"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","GPU recommended for batch processing (CPU inference ~50-100ms per sequence)","768MB+ RAM for model weights"],"input_types":["raw text strings (auto-tokenized)","pre-tokenized input_ids","attention masks for variable-length sequences"],"output_types":["token-level embeddings (sequence_length, 768)","sentence-level embeddings via [CLS] pooling (768,)","mean-pooled embeddings across tokens (768,)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_2","uri":"capability://tool.use.integration.multi.format.model.export.and.cross.framework.compatibility","name":"multi-format model export and cross-framework compatibility","description":"Supports export to 6+ serialization formats (PyTorch, TensorFlow, JAX, ONNX, CoreML, SafeTensors) enabling deployment across diverse inference engines and hardware targets. The model can be loaded and converted via HuggingFace Transformers library, which handles format-specific optimizations (e.g., ONNX quantization, CoreML neural network graph compilation). SafeTensors format provides faster loading and improved security compared to pickle-based PyTorch checkpoints.","intents":["I need to deploy this model on mobile devices using CoreML","I want to run inference on edge devices with ONNX Runtime","I need to use this model in a JAX-based research pipeline","I want to load the model safely without executing arbitrary code"],"best_for":["teams deploying models across heterogeneous hardware (mobile, edge, cloud)","researchers working in JAX or other non-PyTorch frameworks","security-conscious teams avoiding pickle deserialization vulnerabilities","builders optimizing inference latency with ONNX Runtime or TensorRT"],"limitations":["Format conversion may introduce numerical precision differences (especially with quantization)","ONNX export requires additional dependencies (onnx, onnxruntime) not included by default","CoreML export limited to inference — no training or fine-tuning support","JAX version requires jax and jaxlib installation, adding ~500MB to environment","SafeTensors format is newer — some older tools may not support it yet"],"requires":["Transformers library 4.0+","Base framework (PyTorch 1.9+, TensorFlow 2.4+, or JAX 0.2.0+)","Optional: onnx, onnxruntime for ONNX export","Optional: coremltools for CoreML export","Optional: safetensors library for SafeTensors format"],"input_types":["HuggingFace model identifier (string)","local checkpoint directory","pre-loaded model object"],"output_types":["PyTorch .pt or .pth checkpoint","TensorFlow SavedModel directory","ONNX .onnx graph file","CoreML .mlmodel bundle","JAX pytree","SafeTensors .safetensors file"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_3","uri":"capability://code.generation.editing.fine.tuning.and.task.specific.adaptation.via.transfer.learning","name":"fine-tuning and task-specific adaptation via transfer learning","description":"Enables efficient adaptation to downstream tasks (text classification, NER, QA) by freezing pre-trained transformer weights and training a task-specific head (linear layer) on labeled data. The model provides pre-computed contextual embeddings as input to the head, reducing training time and data requirements compared to training from scratch. Supports gradient accumulation, mixed precision training, and distributed fine-tuning via HuggingFace Trainer API.","intents":["I want to adapt this model to classify emails as spam/not-spam with 500 labeled examples","I need to fine-tune for named entity recognition in biomedical text","I want to build a sentiment classifier without training a model from scratch","I need to adapt the model to a new domain with limited labeled data"],"best_for":["teams with limited labeled data (100-10k examples) for specific tasks","researchers prototyping task-specific models quickly","developers building domain-specific classifiers (legal, medical, financial)","builders optimizing training cost and time for production models"],"limitations":["Fine-tuning on very small datasets (<100 examples) risks overfitting — requires careful regularization","Task-specific head architecture must be manually designed for non-standard tasks","Pre-trained weights are frozen by default — full fine-tuning requires more compute and data","Requires labeled data — cannot adapt to unsupervised tasks without modification","Uncased variant may hurt performance on tasks where capitalization is informative (e.g., named entity recognition)"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Labeled dataset in standard format (CSV, JSON, or HuggingFace Dataset)","GPU with 4GB+ VRAM for batch size 8-16 (8GB+ recommended for larger batches)","HuggingFace Trainer or custom training loop"],"input_types":["labeled text examples with task-specific labels","pre-tokenized input_ids with attention masks","dataset in HuggingFace Dataset format"],"output_types":["fine-tuned model checkpoint","task-specific predictions (class labels, confidence scores)","training metrics (loss, accuracy, F1)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_4","uri":"capability://data.processing.analysis.tokenization.with.wordpiece.vocabulary.and.subword.decomposition","name":"tokenization with wordpiece vocabulary and subword decomposition","description":"Converts raw text into token IDs using a 30,522-token WordPiece vocabulary learned from BookCorpus and Wikipedia. The tokenizer performs lowercasing (uncased variant), whitespace splitting, and greedy longest-match subword segmentation, enabling the model to handle out-of-vocabulary words by decomposing them into known subword units. Special tokens ([CLS], [SEP], [MASK], [UNK]) are prepended/appended for task-specific formatting.","intents":["I need to convert raw text into token IDs compatible with BERT","I want to handle out-of-vocabulary words by breaking them into subwords","I need to add special tokens for classification or masking tasks","I want to tokenize text while preserving attention masks for variable-length sequences"],"best_for":["developers building NLP pipelines that require BERT-compatible tokenization","researchers analyzing tokenization behavior and vocabulary coverage","teams working with multilingual or domain-specific text requiring custom tokenizers","builders implementing batch processing with variable-length sequences"],"limitations":["Uncased tokenization loses capitalization information — cannot distinguish 'US' (country) from 'us' (pronoun)","WordPiece vocabulary is fixed — cannot add custom tokens without retraining","Greedy longest-match tokenization may not be optimal for all languages or domains","30,522 vocabulary size is relatively small — rare technical terms may be split into many subwords","No built-in support for character-level or byte-pair encoding alternatives"],"requires":["Transformers library 4.0+","Python 3.6+","Pre-trained tokenizer weights (~230KB)"],"input_types":["raw text strings","lists of text sequences","text pairs (for sentence classification tasks)"],"output_types":["input_ids (token IDs, integers 0-30521)","attention_mask (binary tensor indicating valid tokens)","token_type_ids (segment embeddings for sentence pairs)","tokens (human-readable token strings)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_5","uri":"capability://planning.reasoning.zero.shot.and.few.shot.learning.via.embedding.similarity","name":"zero-shot and few-shot learning via embedding similarity","description":"Enables classification of unseen classes by computing embedding similarity between input text and class descriptions without fine-tuning. The model generates embeddings for both the input and candidate class labels, then ranks classes by cosine similarity. This approach leverages the model's pre-trained semantic understanding to generalize to new tasks with minimal or no labeled examples.","intents":["I want to classify text into categories I haven't seen during training","I need to build a classifier with only 5-10 labeled examples per class","I want to add new categories to my classifier without retraining","I need to evaluate model performance on out-of-distribution text"],"best_for":["teams with limited labeled data for new classification tasks","researchers evaluating transfer learning and generalization","builders prototyping classifiers before investing in data labeling","developers building dynamic classification systems with evolving categories"],"limitations":["Performance degrades significantly on domain-specific tasks (medical, legal) without fine-tuning","Requires manually crafted class descriptions — poor descriptions lead to poor predictions","Embedding similarity is sensitive to text length — longer inputs may dominate similarity scores","No learned task-specific projections — generic embeddings may not capture task-relevant features","Computational cost scales with number of classes (must compute embeddings for all candidates)"],"requires":["PyTorch or TensorFlow for embedding computation","Transformers library 4.0+","Scikit-learn or similar library for cosine similarity computation","GPU optional but recommended for batch embedding computation"],"input_types":["input text to classify","list of class label descriptions (strings)"],"output_types":["predicted class label (string)","similarity scores for each class (floats 0-1)","ranked list of classes by confidence"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_6","uri":"capability://automation.workflow.batch.inference.with.dynamic.sequence.length.handling","name":"batch inference with dynamic sequence length handling","description":"Processes multiple text sequences of varying lengths in a single forward pass by padding shorter sequences to the longest sequence in the batch and using attention masks to ignore padding tokens. The model computes embeddings and predictions for all sequences simultaneously, reducing per-sequence overhead and enabling efficient GPU utilization. Supports configurable batch sizes and automatic device placement (CPU/GPU).","intents":["I need to process 1000 documents efficiently without running inference 1000 times","I want to handle variable-length sequences without manual padding","I need to maximize GPU throughput for inference on a large corpus","I want to measure inference latency and throughput for production deployment"],"best_for":["teams processing large document collections (1k-1M documents)","builders optimizing inference latency and throughput for production","researchers benchmarking model performance at scale","developers building batch processing pipelines for embeddings or predictions"],"limitations":["Padding overhead increases with sequence length variance — batches with mixed lengths waste computation","Memory usage scales with batch size and max sequence length — large batches may cause OOM errors","Attention mask computation adds ~5-10% overhead compared to fixed-length sequences","No built-in batching strategy optimization — developers must manually tune batch size","Inference latency per sequence decreases with batch size but absolute latency increases (throughput vs latency trade-off)"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","GPU with 4GB+ VRAM for batch size 32-64 (8GB+ for larger batches)","DataLoader or similar batching utility"],"input_types":["list of text sequences (variable length)","pre-tokenized input_ids with attention masks","HuggingFace Dataset with batching support"],"output_types":["batched logits or embeddings","batched predictions with confidence scores","inference time metrics (latency, throughput)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_7","uri":"capability://automation.workflow.model.quantization.and.compression.for.edge.deployment","name":"model quantization and compression for edge deployment","description":"Reduces model size and inference latency by converting 32-bit floating-point weights to 8-bit integers (INT8) or lower precision formats (FP16, BFLOAT16) using post-training quantization or quantization-aware training. Quantized models maintain 95%+ accuracy on most tasks while reducing model size by 4x (440MB → 110MB) and inference latency by 2-4x. Supports ONNX quantization, TensorFlow Lite, and PyTorch quantization APIs.","intents":["I need to deploy this model on mobile devices with limited storage","I want to reduce inference latency for real-time applications","I need to run inference on edge devices with limited memory","I want to optimize inference cost by reducing GPU memory usage"],"best_for":["teams deploying models on mobile or edge devices","builders optimizing inference latency for real-time applications","developers reducing deployment costs by fitting more models on limited hardware","researchers studying accuracy-efficiency trade-offs in neural networks"],"limitations":["Quantization introduces numerical precision loss — accuracy drops 1-5% on some tasks","INT8 quantization requires calibration on representative data — poor calibration degrades accuracy","Not all operations support quantization — some layers may remain in FP32, limiting speedup","Quantized models are less portable — require specific inference engines (ONNX Runtime, TensorFlow Lite, etc.)","Fine-tuning quantized models is more complex than fine-tuning full-precision models"],"requires":["PyTorch 1.8+ (for torch.quantization) or TensorFlow 2.4+","ONNX Runtime or TensorFlow Lite for inference","Calibration dataset (100-1000 representative examples)","Optional: quantization-aware training framework (e.g., PyTorch QAT)"],"input_types":["full-precision model checkpoint","calibration dataset for INT8 quantization","quantization configuration (bit-width, scheme)"],"output_types":["quantized model checkpoint (INT8, FP16, or BFLOAT16)","quantization statistics (scale factors, zero points)","accuracy metrics on validation set"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_8","uri":"capability://planning.reasoning.attention.visualization.and.interpretability.analysis","name":"attention visualization and interpretability analysis","description":"Extracts and visualizes attention weights from the 12 transformer layers to understand which input tokens the model attends to when making predictions. Attention patterns reveal linguistic phenomena (e.g., attention to related words, long-range dependencies) and can identify potential biases or failure modes. Supports layer-wise and head-wise attention visualization via BertViz or custom analysis tools.","intents":["I want to understand why the model made a specific prediction","I need to debug model failures by analyzing attention patterns","I want to visualize which tokens the model considers important","I need to detect potential biases or spurious correlations in the model"],"best_for":["researchers studying transformer interpretability and attention mechanisms","teams debugging model failures and unexpected predictions","builders validating that models learn linguistically meaningful patterns","developers creating explainability features for end-users"],"limitations":["Attention weights do not directly explain predictions — high attention does not guarantee importance","Attention visualization is qualitative — difficult to quantify or automate interpretation","12 layers × 12 heads = 144 attention matrices — overwhelming for manual analysis","Attention patterns are task-dependent — patterns learned for fill-mask may not apply to classification","No built-in tools for statistical significance testing of attention patterns"],"requires":["PyTorch or TensorFlow with model.config.output_attentions=True","BertViz library or custom visualization code","Jupyter notebook or similar interactive environment","Basic understanding of transformer architecture"],"input_types":["input text or token IDs","model forward pass with output_attentions=True"],"output_types":["attention weight matrices (batch_size, num_heads, seq_len, seq_len)","attention visualizations (heatmaps, flow diagrams)","interpretability reports (attention statistics, patterns)"],"categories":["planning-reasoning","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-uncased__cap_9","uri":"capability://data.processing.analysis.domain.adaptation.via.continued.pre.training.on.custom.corpora","name":"domain adaptation via continued pre-training on custom corpora","description":"Enables adaptation to new domains (biomedical, legal, financial) by continuing pre-training on domain-specific unlabeled text using the masked language modeling objective. The model learns domain-specific vocabulary and linguistic patterns while retaining general language knowledge from the original pre-training. Supports efficient continued pre-training via gradient accumulation and mixed-precision training.","intents":["I want to adapt BERT to biomedical text without fine-tuning on labeled data","I need to improve model performance on legal documents by pre-training on legal corpora","I want to learn domain-specific terminology and patterns from unlabeled data","I need to reduce fine-tuning data requirements by domain-adapting the model first"],"best_for":["teams with large unlabeled domain-specific corpora (1M+ documents)","researchers studying domain adaptation and transfer learning","builders optimizing downstream task performance with limited labeled data","developers creating specialized models for vertical-specific applications (healthcare, legal, finance)"],"limitations":["Requires large unlabeled corpus (1M+ documents) to be effective — small corpora may not provide sufficient signal","Continued pre-training is computationally expensive (weeks on single GPU) — requires significant compute resources","Vocabulary is fixed — cannot add domain-specific tokens without retraining from scratch","Catastrophic forgetting risk — aggressive continued pre-training may degrade performance on general tasks","No guarantee of improvement — domain adaptation may hurt performance if domain is too different from original training data"],"requires":["PyTorch 1.9+ or TensorFlow 2.4+","Transformers library 4.0+","Large unlabeled domain-specific corpus (1M+ documents, 1GB+ text)","GPU with 8GB+ VRAM (16GB+ recommended for larger batch sizes)","Compute budget for weeks of training (or distributed training setup)"],"input_types":["raw text files or dataset in HuggingFace Dataset format","pre-tokenized input_ids with attention masks"],"output_types":["domain-adapted model checkpoint","training metrics (loss, perplexity)","downstream task performance 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references","768-dimensional vectors require significant memory for large-scale similarity search (use quantization or approximate nearest neighbor indices)","Embeddings are task-agnostic — may not capture domain-specific semantics without fine-tuning","No built-in normalization — cosine similarity requires manual L2 normalization","Sequence length capped at 512 tokens — longer documents must be chunked or truncated","Uncased model conflates 'Apple' (company) and 'apple' (fruit) in embeddings","builder identity is not verified yet","no observed match outcomes 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