{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-dslim--bert-base-ner","slug":"dslim--bert-base-ner","name":"bert-base-NER","type":"model","url":"https://huggingface.co/dslim/bert-base-NER","page_url":"https://unfragile.ai/dslim--bert-base-ner","categories":["model-training"],"tags":["transformers","pytorch","tf","jax","onnx","safetensors","bert","token-classification","en","dataset:conll2003","arxiv:1810.04805","license:mit","model-index","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-dslim--bert-base-ner__cap_0","uri":"capability://data.processing.analysis.multilingual.named.entity.recognition.via.token.classification","name":"multilingual named entity recognition via token classification","description":"Performs token-level sequence labeling using a fine-tuned BERT encoder to identify and classify named entities (persons, organizations, locations, miscellaneous) within raw text. The model uses subword tokenization via WordPiece and outputs per-token probability distributions across entity classes, enabling downstream systems to extract structured entity data from unstructured text with ~90% F1 score on CoNLL2003 benchmark.","intents":["extract person names, company names, and locations from customer support tickets or documents","build a knowledge graph by identifying and classifying entities in large text corpora","preprocess raw text for downstream NLP tasks that require entity-aware context","implement entity-based search or filtering in document management systems"],"best_for":["teams building information extraction pipelines for English text","developers prototyping NER systems without training custom models","enterprises needing production-grade entity recognition with MIT licensing"],"limitations":["English-only model — no native support for non-English languages despite BERT's multilingual capability","Trained on CoNLL2003 dataset (news domain) — performance degrades on out-of-domain text like social media or technical documentation","Token-level predictions require post-processing to reconstruct entity spans from subword tokens, adding complexity","No confidence thresholding built-in — all tokens receive predictions regardless of model certainty","Batch inference latency ~50-100ms per 128-token sequence on CPU, requiring optimization for real-time applications"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) OR TensorFlow (>=2.4.0) OR JAX backend","512 tokens max sequence length (BERT base limitation)","~440MB disk space for model weights (safetensors format)"],"input_types":["raw text strings","pre-tokenized sequences (with attention masks)","batched text up to 512 subword tokens per sequence"],"output_types":["token-level logits (shape: [batch_size, sequence_length, num_classes])","predicted class indices per token (0=O, 1=B-PER, 2=I-PER, 3=B-ORG, 4=I-ORG, 5=B-LOC, 6=I-LOC, 7=B-MISC, 8=I-MISC)","confidence scores (softmax probabilities) for each token-class pair"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_1","uri":"capability://tool.use.integration.cross.framework.model.inference.with.automatic.backend.selection","name":"cross-framework model inference with automatic backend selection","description":"Abstracts away framework-specific inference code by providing a unified HuggingFace transformers API that automatically selects optimal backend (PyTorch, TensorFlow, JAX, or ONNX) based on installed dependencies and hardware availability. The model weights are stored in safetensors format, enabling secure deserialization without arbitrary code execution and fast loading via memory-mapped I/O.","intents":["deploy the same model across heterogeneous infrastructure (PyTorch on GPU servers, TensorFlow on TPU, ONNX on edge devices)","avoid framework lock-in by switching backends without retraining or model conversion","load model weights safely without vulnerability to pickle-based code injection attacks","optimize inference latency by selecting the fastest backend for target hardware"],"best_for":["MLOps teams managing multi-framework deployment pipelines","organizations with mixed infrastructure (GPU, TPU, edge devices)","security-conscious teams requiring safe model deserialization"],"limitations":["Backend selection is automatic but not always optimal — may select PyTorch on a TPU-only system if TensorFlow is not installed","ONNX export requires additional conversion step and may lose some model features (e.g., custom attention patterns)","JAX backend requires jax and jaxlib installation, adding ~500MB dependency overhead","Safetensors format is newer — some legacy tools may not support it without explicit conversion"],"requires":["transformers library (>=4.20.0 for safetensors support)","At least one of: PyTorch (>=1.9.0), TensorFlow (>=2.4.0), JAX (>=0.3.0), or ONNX Runtime (>=1.10.0)","Internet access for initial model download (~440MB) or pre-cached model directory"],"input_types":["model identifier string ('dslim/bert-base-NER')","local filesystem path to model directory","HuggingFace Hub model card URL"],"output_types":["transformers.PreTrainedModel instance (framework-agnostic wrapper)","inference results in framework-native format (torch.Tensor, tf.Tensor, jnp.ndarray, or numpy.ndarray)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__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 multiple text sequences of varying lengths in a single forward pass by automatically padding shorter sequences to the longest in the batch and generating attention masks to prevent the model from attending to padding tokens. This reduces per-sequence overhead and enables GPU batching efficiency while maintaining correctness of token-level predictions.","intents":["process large document collections efficiently by batching variable-length texts","reduce inference latency by 3-5x compared to processing sequences one-at-a-time","handle real-world text where sequence lengths vary (titles vs. paragraphs)","maximize GPU utilization by filling batch dimensions without wasting compute on padding"],"best_for":["batch processing pipelines for document analysis or content moderation","high-throughput inference services handling concurrent requests","teams optimizing inference cost on cloud GPU infrastructure"],"limitations":["Padding adds computational overhead for short sequences in long batches — if batch contains one 512-token sequence and nine 50-token sequences, all nine are padded to 512, wasting ~80% compute","Attention masks prevent but do not eliminate padding token processing — the model still computes embeddings for padding tokens before masking attention","Dynamic padding requires CPU-side sequence length tracking, adding ~5-10ms overhead per batch on CPU","Maximum batch size limited by GPU memory — typical batch size 8-32 for 12GB VRAM, vs. 64+ for smaller models"],"requires":["transformers library with DataCollatorWithPadding utility (>=4.0.0)","PyTorch DataLoader or equivalent batching mechanism","GPU with >=8GB VRAM for batch_size >= 16 at sequence_length=512"],"input_types":["list of text strings with variable lengths","pre-tokenized sequences with token_ids and attention_mask tensors"],"output_types":["batched logits tensor (shape: [batch_size, max_sequence_length, num_classes])","attention_mask tensor indicating valid vs. padding tokens"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_3","uri":"capability://data.processing.analysis.entity.span.reconstruction.from.subword.tokens","name":"entity span reconstruction from subword tokens","description":"Converts token-level predictions from the BERT model (which operates on WordPiece subword tokens) back into character-level entity spans in the original text. This involves tracking subword boundaries (tokens starting with '##'), merging predictions across subword fragments, and mapping token positions back to character offsets in the source text.","intents":["extract entity text and boundaries from model predictions for downstream applications","build entity-aware search indexes by mapping predicted entities to original document positions","generate structured output (JSON with entity name, type, and character offsets) from raw predictions","enable entity linking by matching reconstructed spans to knowledge bases"],"best_for":["information extraction pipelines that need human-readable entity locations","document annotation systems requiring character-level entity boundaries","teams building entity-aware search or knowledge graph construction"],"limitations":["Subword merging requires careful handling of '##' prefix tokens — incorrect merging produces malformed entity text","Character offset mapping is fragile when original text contains special characters or whitespace normalization — requires exact tokenizer configuration match","Overlapping entity predictions (e.g., nested entities) are not handled — model outputs flat BIO tags without hierarchy","Entity boundary errors propagate downstream — if model predicts B-PER in middle of a word, reconstructed span will be incorrect"],"requires":["transformers.AutoTokenizer for the same BERT model to ensure token-to-character mapping consistency","Original raw text (not pre-tokenized) to enable character offset calculation","Post-processing script or library (e.g., seqeval) to convert BIO tags to spans"],"input_types":["token-level class predictions (BIO tag indices or class names)","token_ids from BERT tokenizer","original raw text string"],"output_types":["list of entity dictionaries with keys: {entity_text, entity_type, start_char, end_char}","structured JSON with entity annotations"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_4","uri":"capability://automation.workflow.multi.backend.model.deployment.via.huggingface.endpoints.and.cloud.platforms","name":"multi-backend model deployment via huggingface endpoints and cloud platforms","description":"Integrates with HuggingFace Inference Endpoints and major cloud providers (Azure, AWS, GCP) to enable serverless or containerized deployment without manual infrastructure setup. The model is registered in the HuggingFace Model Hub with endpoint-compatible metadata, allowing one-click deployment to managed inference services with automatic scaling, monitoring, and API generation.","intents":["deploy NER model as a REST API without writing inference code or managing containers","scale inference automatically based on request volume using managed services","integrate NER into production applications via simple HTTP requests","monitor model performance and latency through cloud provider dashboards"],"best_for":["teams without ML infrastructure expertise seeking quick production deployment","organizations already using HuggingFace Endpoints or Azure ML for other models","startups needing cost-effective inference without dedicated GPU servers"],"limitations":["HuggingFace Endpoints pricing is per-hour regardless of usage — not cost-effective for low-traffic applications","Cold start latency ~2-5 seconds on first request after scaling down, unsuitable for real-time applications","API rate limits and request size limits (typically 1MB payload) may constrain high-throughput use cases","Vendor lock-in — switching from HuggingFace Endpoints to self-hosted requires rewriting client code","No fine-tuning or custom preprocessing in the managed endpoint — must be handled client-side"],"requires":["HuggingFace account with API token","Endpoint subscription (paid tier for production, free tier for testing)","HTTP client library (requests, curl, etc.)","Network access to HuggingFace API endpoints (may require firewall rules)"],"input_types":["JSON payload with 'inputs' field containing text string or list of strings","HTTP POST request to endpoint URL"],"output_types":["JSON response with token-level predictions and entity classifications","HTTP status codes and error messages"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_5","uri":"capability://code.generation.editing.fine.tuning.and.domain.adaptation.for.custom.entity.types","name":"fine-tuning and domain adaptation for custom entity types","description":"Provides a pre-trained BERT encoder that can be efficiently fine-tuned on custom NER datasets with different entity types (e.g., medical entities, product names) using transfer learning. The model's learned language representations transfer to new domains, requiring only 100-1000 labeled examples to achieve good performance compared to training from scratch which needs 10,000+ examples.","intents":["adapt the model to recognize domain-specific entities (medical terms, legal entities, product SKUs) with minimal labeled data","reduce annotation effort by leveraging pre-trained representations instead of training from scratch","build custom NER models for specialized domains without ML expertise","improve performance on out-of-domain text by fine-tuning on domain-specific corpora"],"best_for":["teams with domain-specific NER needs and small labeled datasets (100-5000 examples)","organizations migrating from rule-based systems to ML-based NER","researchers exploring transfer learning for low-resource NER tasks"],"limitations":["Fine-tuning requires GPU (training on CPU is prohibitively slow — ~1 hour per epoch vs. 2 minutes on GPU)","Overfitting risk with small datasets (<500 examples) — requires careful regularization (dropout, early stopping, weight decay)","Entity type mismatch — if custom dataset has different entity types than CoNLL2003, the model must be retrained from scratch on the new label set","Catastrophic forgetting — fine-tuning on new domain may degrade performance on original CoNLL2003 entities","Hyperparameter tuning required — learning rate, batch size, and epochs must be adjusted per dataset"],"requires":["PyTorch or TensorFlow (>=2.4.0)","transformers library (>=4.0.0) with Trainer API","GPU with >=12GB VRAM for batch_size >= 8","Labeled dataset in BIO or IOB2 format with custom entity types","Python 3.7+ with datasets library for data loading"],"input_types":["labeled text sequences in CoNLL format or HuggingFace datasets format","custom entity type definitions (e.g., ['O', 'B-DRUG', 'I-DRUG', 'B-SYMPTOM', 'I-SYMPTOM'])"],"output_types":["fine-tuned model checkpoint saved to disk","evaluation metrics (precision, recall, F1) on validation set"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_6","uri":"capability://data.processing.analysis.confidence.scoring.and.uncertainty.quantification.for.predictions","name":"confidence scoring and uncertainty quantification for predictions","description":"Outputs softmax probability distributions over entity classes for each token, enabling downstream systems to filter low-confidence predictions, rank entities by confidence, or implement confidence-based thresholding. The model does not provide calibrated uncertainty estimates (e.g., Bayesian confidence intervals), but raw softmax scores can be used as a proxy for prediction confidence.","intents":["filter out low-confidence entity predictions to reduce false positives in production systems","rank extracted entities by confidence for prioritization in downstream tasks","implement confidence-based quality gates (e.g., only use predictions with >0.9 confidence)","detect out-of-distribution inputs by identifying sequences with uniformly low confidence across all tokens"],"best_for":["production systems requiring precision over recall (e.g., legal document processing)","applications with human-in-the-loop workflows where low-confidence predictions are reviewed by humans","quality assurance pipelines that need to flag uncertain predictions for manual inspection"],"limitations":["Softmax scores are not calibrated — a 0.9 confidence score does not mean 90% probability of correctness; actual accuracy may be 70-80%","No built-in uncertainty quantification — cannot distinguish between 'model is confident but wrong' and 'model is uncertain'","Confidence scores are per-token, not per-entity — entity-level confidence requires aggregating token scores, which is non-trivial","Adversarial examples can produce high-confidence wrong predictions — softmax scores do not detect out-of-distribution inputs reliably","No confidence calibration post-processing built-in — requires separate temperature scaling or Platt scaling to align scores with accuracy"],"requires":["Model output logits or softmax probabilities (available via transformers pipeline with output_scores=True)","Post-processing logic to aggregate token-level scores to entity-level confidence"],"input_types":["token-level logits from model forward pass (shape: [batch_size, sequence_length, num_classes])"],"output_types":["softmax probability distributions (shape: [batch_size, sequence_length, num_classes])","per-token confidence scores (max probability across classes)","per-entity confidence (aggregated from constituent tokens)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--bert-base-ner__cap_7","uri":"capability://automation.workflow.onnx.export.for.edge.deployment.and.inference.optimization","name":"onnx export for edge deployment and inference optimization","description":"Supports export to ONNX (Open Neural Network Exchange) format, enabling deployment on edge devices, mobile platforms, and specialized inference hardware (e.g., NVIDIA Jetson, Intel Neural Compute Stick) without PyTorch or TensorFlow dependencies. ONNX models are typically 2-5x faster and 50% smaller than PyTorch checkpoints due to graph optimization and quantization support.","intents":["deploy NER to edge devices (mobile phones, IoT devices) with minimal latency and memory footprint","optimize inference latency by 2-5x using ONNX Runtime's graph optimizations","reduce model size by 50% for deployment on resource-constrained devices","enable inference on specialized hardware (TPU, NPU) via ONNX Runtime backends"],"best_for":["mobile and edge computing teams requiring on-device NER without cloud dependencies","IoT and embedded systems with limited compute and memory (e.g., Raspberry Pi, Jetson Nano)","organizations with strict data privacy requirements preventing cloud inference"],"limitations":["ONNX export requires additional conversion step and may lose model features (e.g., custom attention patterns, dynamic shapes)","ONNX Runtime inference requires separate installation and configuration — not as straightforward as 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