{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-google-bert--bert-base-chinese","slug":"google-bert--bert-base-chinese","name":"bert-base-chinese","type":"model","url":"https://huggingface.co/google-bert/bert-base-chinese","page_url":"https://unfragile.ai/google-bert--bert-base-chinese","categories":["research-search"],"tags":["transformers","pytorch","tf","jax","safetensors","bert","fill-mask","zh","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-chinese__cap_0","uri":"capability://text.generation.language.masked.token.prediction.for.chinese.text","name":"masked-token-prediction-for-chinese-text","description":"Predicts masked tokens in Chinese text using a 12-layer transformer encoder trained on Chinese Wikipedia and other corpora. The model uses bidirectional context via masked self-attention to infer [MASK] tokens, outputting probability distributions over the 21,128-token Chinese vocabulary. Architecture employs 768-dimensional embeddings with 12 attention heads, enabling contextual understanding of Chinese morphology and syntax without language-specific preprocessing.","intents":["Fill in missing or corrupted Chinese characters in documents for data cleaning","Generate candidate tokens for Chinese text augmentation and paraphrasing tasks","Evaluate semantic coherence of Chinese sentences by scoring mask-filling plausibility","Build Chinese language understanding features for downstream NLP applications"],"best_for":["NLP teams building Chinese text processing pipelines","Researchers fine-tuning on Chinese-specific downstream tasks (NER, sentiment analysis, QA)","Data engineers cleaning or augmenting Chinese corpora at scale"],"limitations":["Trained on 2018-era Chinese text; may not capture recent slang, neologisms, or domain-specific terminology","Single-token masking only — cannot predict multi-token spans or complex phrase structures","No built-in handling for traditional vs simplified Chinese variants; vocabulary is simplified-Chinese-dominant","Inference latency ~50-200ms per sequence on CPU; requires GPU for batch processing >32 sequences","Maximum sequence length 512 tokens; longer documents require sliding-window or truncation strategies"],"requires":["Python 3.6+","transformers library (HuggingFace) version 2.3.0 or later","PyTorch 1.0+ or TensorFlow 2.0+ or JAX (model supports all three frameworks via safetensors format)","4GB+ RAM for model loading (12-layer, 110M parameters); 8GB+ recommended for batch inference","HuggingFace model hub access or local model weights (~440MB)"],"input_types":["raw Chinese text strings","tokenized sequences with [MASK] tokens inserted at target positions","batch sequences as PyTorch tensors or TensorFlow datasets"],"output_types":["probability distributions over vocabulary (shape: [batch_size, seq_length, vocab_size])","top-k predicted token IDs with confidence scores","logits for downstream fine-tuning or ensemble methods"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-chinese__cap_1","uri":"capability://data.processing.analysis.chinese.text.representation.encoding","name":"chinese-text-representation-encoding","description":"Encodes Chinese text into dense 768-dimensional contextual embeddings via the BERT encoder's hidden states. Each token receives a context-aware representation computed through 12 stacked transformer layers with bidirectional self-attention, capturing semantic and syntactic information about Chinese morphology, word boundaries, and phrase structure. Embeddings can be extracted from any layer (typically final layer or averaged across layers) for downstream tasks.","intents":["Convert Chinese text to fixed-size vectors for semantic similarity search and clustering","Extract contextual embeddings for Chinese sentence classification, sentiment analysis, or intent detection","Build feature representations for Chinese information retrieval or recommendation systems","Generate embeddings for Chinese text-to-text matching in paraphrase detection or duplicate detection"],"best_for":["ML engineers building semantic search or clustering systems for Chinese documents","Teams implementing Chinese text classification or intent recognition in chatbots","Researchers evaluating Chinese language understanding via embedding-based probing tasks"],"limitations":["Embeddings are token-level; sentence/document embeddings require pooling strategy (mean, CLS token, or learned aggregation) which may lose fine-grained information","Context window limited to 512 tokens; longer documents require chunking or hierarchical encoding strategies","Embeddings are not language-agnostic; mixing Chinese and English in same sequence may degrade quality due to vocabulary mismatch","No built-in normalization; cosine similarity requires manual L2 normalization for consistent distance metrics"],"requires":["Python 3.6+","transformers library 2.3.0+","PyTorch 1.0+ or TensorFlow 2.0+ or JAX","2GB+ RAM for model inference","tokenizer compatible with BERT (WordPiece tokenizer for Chinese)"],"input_types":["raw Chinese text strings","pre-tokenized sequences as token IDs","batched sequences as PyTorch tensors or NumPy arrays"],"output_types":["dense vectors (shape: [batch_size, seq_length, 768])","pooled sentence embeddings (shape: [batch_size, 768])","attention weights for interpretability (shape: [batch_size, num_heads, seq_length, seq_length])"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-chinese__cap_2","uri":"capability://code.generation.editing.fine.tuning.on.downstream.chinese.nlp.tasks","name":"fine-tuning-on-downstream-chinese-nlp-tasks","description":"Enables transfer learning by adding task-specific heads (classification layers, sequence tagging heads, or QA heads) on top of frozen or unfrozen BERT encoder layers. The model supports efficient fine-tuning via parameter-efficient methods (LoRA, adapter modules) or full fine-tuning, with gradient computation through all 12 transformer layers. Training leverages standard PyTorch/TensorFlow optimizers (Adam, AdamW) with learning rate warmup and weight decay for stable convergence on Chinese downstream tasks.","intents":["Fine-tune BERT for Chinese text classification tasks (sentiment analysis, topic classification, intent detection)","Adapt BERT for Chinese sequence labeling (NER, POS tagging, chunking) via token-level classification heads","Train BERT for Chinese question-answering systems with span extraction heads","Implement Chinese semantic similarity or paraphrase detection via sentence-pair classification"],"best_for":["ML teams with labeled Chinese datasets (100+ examples) building production NLP systems","Researchers conducting Chinese NLP experiments with limited computational budgets","Companies deploying Chinese-specific models without access to large-scale unlabeled data"],"limitations":["Requires labeled training data; performance degrades significantly with <100 examples per class","Full fine-tuning requires 8GB+ GPU VRAM; parameter-efficient methods (LoRA) reduce to 2-4GB but add complexity","Overfitting risk on small datasets; requires careful regularization (dropout, weight decay, early stopping)","Fine-tuning time varies: 1-10 hours on single GPU for typical datasets (1K-10K examples)","No built-in handling for class imbalance or domain shift; requires custom loss weighting or data augmentation"],"requires":["Python 3.6+","transformers library 2.3.0+","PyTorch 1.0+ or TensorFlow 2.0+ with training support","GPU with 8GB+ VRAM (or CPU for small datasets with reduced batch size)","labeled Chinese dataset in standard format (CSV, JSON, or HuggingFace datasets)","training script or framework (HuggingFace Trainer, PyTorch Lightning, or custom training loop)"],"input_types":["labeled Chinese text examples with task-specific annotations (labels, spans, pairs)","validation and test sets in same format","optional: unlabeled data for data augmentation or semi-supervised learning"],"output_types":["fine-tuned model weights saved as PyTorch checkpoints or safetensors","evaluation metrics (accuracy, F1, precision/recall for classification; F1 for NER)","predictions on test set in task-specific format (class labels, token tags, spans)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-chinese__cap_3","uri":"capability://automation.workflow.multi.framework.model.export.and.deployment","name":"multi-framework-model-export-and-deployment","description":"Exports trained or pretrained BERT weights to multiple deep learning frameworks (PyTorch, TensorFlow, JAX) via unified safetensors format, enabling deployment across diverse inference environments. Model weights are stored in framework-agnostic safetensors binary format (~440MB), with automatic conversion to framework-specific formats (PyTorch .pt, TensorFlow SavedModel, JAX pytree) during loading. Supports ONNX export for optimized inference on CPUs and edge devices.","intents":["Deploy BERT to production systems using different frameworks (PyTorch for research, TensorFlow for serving)","Export model to ONNX for inference optimization on CPUs, mobile devices, or specialized hardware","Integrate BERT into multi-framework ML pipelines without reimplementation","Ensure reproducibility and portability across development, testing, and production environments"],"best_for":["ML ops teams managing heterogeneous inference infrastructure (PyTorch + TensorFlow + ONNX)","Organizations deploying models across cloud (Azure, AWS, GCP) and edge devices","Researchers sharing models across teams using different frameworks"],"limitations":["ONNX export requires additional conversion step and may lose some dynamic control flow features","Framework-specific optimizations (TensorFlow XLA, PyTorch TorchScript) require separate compilation","Safetensors format is read-only during inference; no in-place weight updates without reloading","Cross-framework numerical precision may vary slightly (float32 vs float16); requires validation","JAX export requires jax library and may not support all dynamic features of PyTorch/TensorFlow versions"],"requires":["Python 3.6+","transformers library 2.3.0+","PyTorch 1.0+ OR TensorFlow 2.0+ OR JAX (depending on target framework)","safetensors library for efficient weight loading","ONNX Runtime (optional, for ONNX inference)","~1GB disk space for model weights in each framework format"],"input_types":["pretrained BERT model from HuggingFace hub or local checkpoint","framework specification (pytorch, tensorflow, jax, onnx)","optional: quantization config (int8, float16) for optimized export"],"output_types":["framework-specific model files (PyTorch .pt, TensorFlow SavedModel, JAX pytree)","ONNX model (.onnx) for cross-platform inference","safetensors weights file for framework-agnostic storage"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-google-bert--bert-base-chinese__cap_4","uri":"capability://automation.workflow.batch.inference.with.dynamic.padding","name":"batch-inference-with-dynamic-padding","description":"Processes multiple Chinese text sequences in parallel using dynamic padding to minimize computational waste. The model groups sequences by length, pads to the longest sequence in each batch, and applies attention masks to ignore padding tokens during computation. Batching is handled transparently via HuggingFace pipeline API or manual batching with DataLoader, enabling efficient GPU utilization for throughput-critical applications.","intents":["Process large volumes of Chinese text (1000s-millions of documents) efficiently for batch classification or embedding extraction","Reduce per-sequence inference latency by amortizing model loading and GPU setup costs across batches","Build scalable Chinese NLP pipelines for data processing, content moderation, or search indexing","Optimize inference cost in cloud environments where GPU time is billed per batch"],"best_for":["Data engineers processing large Chinese corpora for ETL or feature extraction","ML teams building batch inference pipelines for daily/weekly model scoring","Cost-conscious organizations optimizing cloud inference budgets"],"limitations":["Dynamic padding adds ~5-10% overhead for length computation and mask generation","Memory usage scales with batch size and max sequence length; OOM errors require batch size reduction","Latency benefits diminish for very small batches (<4 sequences) or highly variable sequence lengths","No built-in distributed batching across multiple GPUs; requires manual data parallelism setup","Attention masks prevent true parallelization of variable-length sequences; all sequences padded to max length in batch"],"requires":["Python 3.6+","transformers library 2.3.0+","PyTorch 1.0+ or TensorFlow 2.0+ with DataLoader support","GPU with 4GB+ VRAM for batch size >32; CPU inference possible but 10-50x slower","HuggingFace datasets library (optional, for efficient data loading)"],"input_types":["list of Chinese text strings","pre-tokenized sequences as token ID lists","PyTorch DataLoader or TensorFlow tf.data.Dataset with batched examples"],"output_types":["batched predictions (shape: [batch_size, num_classes] for classification)","batched embeddings (shape: [batch_size, 768] for sentence-level)","batched logits or probabilities for downstream processing"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":47,"verified":false,"data_access_risk":"low","permissions":["Python 3.6+","transformers library (HuggingFace) version 2.3.0 or later","PyTorch 1.0+ or TensorFlow 2.0+ or JAX (model supports all three frameworks via safetensors format)","4GB+ RAM for model loading (12-layer, 110M parameters); 8GB+ recommended for batch inference","HuggingFace model hub access or local model weights (~440MB)","transformers library 2.3.0+","PyTorch 1.0+ or TensorFlow 2.0+ or JAX","2GB+ RAM for model inference","tokenizer compatible with BERT (WordPiece tokenizer for Chinese)","PyTorch 1.0+ or TensorFlow 2.0+ with training support"],"failure_modes":["Trained on 2018-era Chinese text; may not capture recent slang, neologisms, or domain-specific terminology","Single-token masking only — cannot predict multi-token spans or complex phrase structures","No built-in handling for traditional vs simplified Chinese variants; vocabulary is simplified-Chinese-dominant","Inference latency ~50-200ms per sequence on CPU; requires GPU for batch processing >32 sequences","Maximum sequence length 512 tokens; longer documents require sliding-window or truncation strategies","Embeddings are token-level; sentence/document embeddings require pooling strategy (mean, CLS token, or learned aggregation) which may lose fine-grained information","Context window limited to 512 tokens; longer documents require chunking or hierarchical encoding strategies","Embeddings are not language-agnostic; mixing Chinese and English in same sequence may degrade quality due to vocabulary mismatch","No built-in normalization; cosine similarity requires manual L2 normalization for consistent distance metrics","Requires labeled training data; performance degrades significantly with <100 examples per class","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7717315949785654,"quality":0.2,"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":1140112,"model_likes":1417}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=google-bert--bert-base-chinese","compare_url":"https://unfragile.ai/compare?artifact=google-bert--bert-base-chinese"}},"signature":"jxACna1y9oiBGJBHyq4UUGxoJ1mutMGVk/RjVvg2goDoSmS98o1r7B85E0XDMs1itbVXs+LSMlXnGAVeJl4zCQ==","signedAt":"2026-06-22T17:43:21.483Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/google-bert--bert-base-chinese","artifact":"https://unfragile.ai/google-bert--bert-base-chinese","verify":"https://unfragile.ai/api/v1/verify?slug=google-bert--bert-base-chinese","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"}}