{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-dslim--distilbert-ner","slug":"dslim--distilbert-ner","name":"distilbert-NER","type":"model","url":"https://huggingface.co/dslim/distilbert-NER","page_url":"https://unfragile.ai/dslim--distilbert-ner","categories":["data-analysis"],"tags":["transformers","tensorboard","onnx","safetensors","distilbert","token-classification","en","dataset:conll2003","arxiv:1810.04805","arxiv:1910.01108","base_model:distilbert/distilbert-base-cased","base_model:quantized:distilbert/distilbert-base-cased","license:apache-2.0","endpoints_compatible","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-dslim--distilbert-ner__cap_0","uri":"capability://data.processing.analysis.token.level.named.entity.recognition.with.distilled.transformer.inference","name":"token-level named entity recognition with distilled transformer inference","description":"Performs sequence labeling on input text by tokenizing with WordPiece vocabulary, passing tokens through a 6-layer DistilBERT encoder (40% smaller than BERT-base), and classifying each token into entity categories (PER, ORG, LOC, MISC, O) using a linear classification head. Uses attention mechanisms to capture bidirectional context for each token position, enabling entity boundary detection without explicit sequence tagging rules.","intents":["extract person names, organizations, locations, and miscellaneous entities from unstructured text documents","build NER pipelines that run efficiently on CPU or edge devices without GPU requirements","integrate entity extraction into production systems with minimal latency overhead","fine-tune the model on domain-specific entity types using the HuggingFace Transformers API"],"best_for":["NLP engineers building information extraction pipelines for document processing","teams deploying entity recognition at scale with CPU-constrained infrastructure","developers prototyping multilingual or domain-specific NER without training from scratch","researchers benchmarking token classification performance on CONLL2003 and similar datasets"],"limitations":["Fixed vocabulary of ~28K tokens from DistilBERT base; out-of-vocabulary words are subword-tokenized, potentially splitting entity names across multiple tokens","Trained exclusively on CONLL2003 English dataset; performance degrades significantly on non-English text or domain-specific entities (medical, legal, financial terminology)","Maximum sequence length of 512 tokens; documents longer than ~400 words require sliding-window or truncation strategies","No built-in confidence scoring or uncertainty quantification; all predictions treated as equally confident","Token-level predictions can produce malformed entity spans (e.g., B-PER followed by B-PER without I-PER); post-processing required for clean entity extraction"],"requires":["Python 3.6+","transformers library (>=4.0.0)","torch or tensorflow backend (CPU or GPU)","input text in English language","HuggingFace model hub access or local model weights (~268MB for safetensors format)"],"input_types":["raw text strings","pre-tokenized sequences (optional)","batched text inputs for efficient inference"],"output_types":["token-level classification labels (BIO or BIOES format)","logits/probability scores per token per entity class","structured entity spans with start/end character offsets"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_1","uri":"capability://data.processing.analysis.batch.inference.with.dynamic.batching.and.padding.optimization","name":"batch inference with dynamic batching and padding optimization","description":"Accepts multiple text sequences of variable length, automatically pads shorter sequences to match the longest in the batch, and processes them through the transformer in a single forward pass using efficient tensor operations. Implements dynamic batching to minimize padding waste and reduce memory footprint compared to fixed-size batching, with support for both PyTorch and TensorFlow backends.","intents":["process hundreds or thousands of documents efficiently without sequential inference loops","reduce per-document inference latency by amortizing transformer computation across batch","minimize GPU/CPU memory usage by padding only to the longest sequence in each batch rather than a fixed maximum"],"best_for":["production systems processing document streams or bulk NER jobs","data scientists running batch inference on large corpora for analysis or dataset creation","teams optimizing inference cost and throughput in cloud environments"],"limitations":["Batch size must be tuned per hardware; too large causes OOM errors; too small wastes parallelization benefits","Padding tokens (special [PAD] token) still consume computation; highly variable sequence lengths in a batch reduce efficiency gains","No built-in distributed batching across multiple GPUs or TPUs; requires external orchestration (Ray, Spark, etc.)","Attention mask computation adds ~5-10% overhead per batch to handle variable lengths"],"requires":["transformers library with DataCollator or manual padding logic","sufficient GPU/CPU memory for largest batch size × max sequence length","PyTorch or TensorFlow installed"],"input_types":["list of text strings with variable lengths","pre-tokenized sequences with attention masks"],"output_types":["batched logits tensor (batch_size × seq_length × num_classes)","batched token-level predictions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_2","uri":"capability://automation.workflow.onnx.export.and.cross.platform.inference.optimization","name":"onnx export and cross-platform inference optimization","description":"Exports the DistilBERT token classifier to ONNX (Open Neural Network Exchange) format, enabling inference on non-Python runtimes (C++, C#, Java, JavaScript) and hardware accelerators (ONNX Runtime, TensorRT, CoreML). Includes quantization support (int8, fp16) to reduce model size and latency by 2-4x with minimal accuracy loss, stored in safetensors format for secure model distribution.","intents":["deploy NER models to edge devices, mobile apps, or browser environments without Python/PyTorch dependencies","reduce inference latency and model size for real-time entity extraction in production systems","integrate the model into non-Python backend services (Java microservices, C++ applications, Node.js servers)"],"best_for":["mobile and edge ML engineers deploying models on resource-constrained devices","backend teams building polyglot systems with non-Python services","teams requiring sub-100ms inference latency for real-time NER","organizations with strict model distribution security requirements (safetensors prevents arbitrary code execution)"],"limitations":["ONNX export requires manual conversion; not all HuggingFace features (e.g., custom attention patterns) translate to ONNX","Quantized models (int8) may lose 1-3% F1 score on edge cases; requires validation per domain","ONNX Runtime performance varies by hardware; CPU inference on ARM (mobile) is slower than GPU inference","Safetensors format is read-only during inference; no dynamic weight updates or LoRA adapters","Cross-platform testing required; ONNX model behavior can differ slightly across runtimes (ONNX Runtime vs TensorRT vs CoreML)"],"requires":["transformers library with ONNX export utilities","onnx and onnxruntime packages","ONNX Runtime or equivalent runtime for target platform","optional: quantization tools (onnxruntime-tools or TensorRT for GPU)"],"input_types":["PyTorch or TensorFlow model checkpoint","ONNX model file (.onnx)"],"output_types":["ONNX model artifact (platform-agnostic)","quantized ONNX model (int8 or fp16)","safetensors format model weights"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.entity.types.with.transfer.learning","name":"fine-tuning on custom entity types with transfer learning","description":"Enables adaptation of the pre-trained DistilBERT encoder to domain-specific entity types (e.g., medical entities, product names, financial instruments) by replacing the classification head and training on labeled custom datasets. Uses transfer learning to retain knowledge from CONLL2003 pre-training while learning new entity patterns; supports parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) to reduce trainable parameters by 99% without accuracy loss.","intents":["adapt the model to extract domain-specific entities (medical, legal, financial) not covered by CONLL2003","fine-tune on proprietary labeled datasets without retraining from scratch","reduce fine-tuning compute cost and memory footprint using LoRA or other parameter-efficient methods"],"best_for":["NLP practitioners with labeled domain datasets (100+ examples minimum)","teams building vertical-specific NER (healthcare, legal tech, fintech)","researchers experimenting with entity type adaptation on limited compute budgets"],"limitations":["Requires labeled training data; quality and quantity of labels directly impact fine-tuned model performance","Fine-tuning on small datasets (<500 examples) risks overfitting; requires careful hyperparameter tuning and validation","LoRA reduces trainable parameters but adds inference latency (~5-10%) due to rank decomposition computations","No built-in active learning or data augmentation; manual dataset curation required","Fine-tuned model is tied to custom entity schema; cannot easily switch between entity type sets without retraining"],"requires":["Python 3.6+","transformers and torch/tensorflow","labeled dataset in BIO or BIOES format (CoNLL-style)","GPU recommended for fine-tuning (CPU training is very slow)","optional: peft library for LoRA fine-tuning"],"input_types":["labeled text sequences with token-level entity annotations","BIO/BIOES format datasets or custom annotation formats"],"output_types":["fine-tuned model checkpoint","LoRA adapter weights (if using parameter-efficient fine-tuning)","evaluation metrics (precision, recall, F1 per entity type)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_4","uri":"capability://data.processing.analysis.multilingual.entity.extraction.via.cross.lingual.transfer","name":"multilingual entity extraction via cross-lingual transfer","description":"While trained exclusively on English CONLL2003, the model can perform zero-shot entity extraction on non-English text through cross-lingual transfer learning inherent to multilingual BERT-derived architectures. Leverages shared subword vocabulary and attention patterns learned from English to generalize to other languages, though with degraded performance (typically 10-30% lower F1 than English).","intents":["extract entities from non-English text without language-specific NER models","prototype multilingual NER pipelines before investing in language-specific fine-tuning","handle code-mixed or low-resource language text with minimal additional training"],"best_for":["teams processing multilingual corpora with limited resources for language-specific models","startups prototyping global NER before building language-specific variants","researchers studying cross-lingual transfer in token classification"],"limitations":["Performance degrades significantly on non-English text (typically 60-80% of English F1); not suitable for production multilingual NER without fine-tuning","Model is monolingual (English-only) by design; does not use multilingual BERT or XLM-RoBERTa which have explicit cross-lingual pre-training","Entity type definitions are English-centric (PER, ORG, LOC, MISC); may not align with linguistic entity boundaries in other languages","Subword tokenization assumes Latin/Germanic language structure; performance is worse on morphologically rich or non-Latin script languages (Arabic, Chinese, etc.)","No built-in language detection; requires external language identification to route text to appropriate models"],"requires":["transformers library","input text in any language (though English-trained, so best-effort only)","optional: language detection library (langdetect, textblob) for routing"],"input_types":["text in non-English languages","code-mixed text (e.g., English + Spanish)"],"output_types":["token-level entity predictions (same label set as English model)","logits for each token (lower confidence on non-English text)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_5","uri":"capability://safety.moderation.confidence.scoring.and.uncertainty.quantification.per.token","name":"confidence scoring and uncertainty quantification per token","description":"Outputs raw logits and softmax probabilities for each token's entity class prediction, enabling confidence-based filtering and uncertainty quantification. Developers can extract the maximum softmax probability per token to identify low-confidence predictions, or compute entropy across the class distribution to detect ambiguous entity boundaries. Supports post-processing strategies like confidence thresholding to filter unreliable predictions.","intents":["identify low-confidence entity predictions for manual review or rejection","compute uncertainty metrics (entropy, margin) to assess model reliability per token","implement confidence-based filtering to improve precision at the cost of recall"],"best_for":["teams building human-in-the-loop NER systems with manual review workflows","applications requiring high-precision entity extraction (legal, medical) where false positives are costly","researchers analyzing model calibration and uncertainty on token classification tasks"],"limitations":["Raw logits are not calibrated; softmax probabilities do not reflect true prediction confidence; requires temperature scaling or Platt scaling for calibration","High confidence does not guarantee correctness; model can be confidently wrong on out-of-distribution entities","No built-in uncertainty quantification methods (Bayesian, ensemble-based); requires manual implementation of advanced techniques","Confidence is per-token, not per-entity-span; entity-level confidence requires aggregation logic (e.g., minimum token confidence in span)","Threshold selection is dataset and application-specific; no principled method provided for choosing optimal thresholds"],"requires":["transformers library with output_scores=True or similar flag","post-processing logic to extract probabilities and compute metrics","optional: sklearn or scipy for calibration techniques"],"input_types":["text sequences"],"output_types":["logits tensor (batch_size × seq_length × num_classes)","softmax probabilities per token","entropy or margin scores per token"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_6","uri":"capability://automation.workflow.efficient.inference.on.cpu.and.low.resource.hardware","name":"efficient inference on cpu and low-resource hardware","description":"DistilBERT's 40% smaller size (268MB vs 440MB for BERT-base) and 6-layer architecture enable efficient inference on CPU, mobile devices, and edge hardware without GPU acceleration. Achieves ~2-3x speedup over BERT-base on CPU while maintaining 97%+ F1 score; supports quantization (int8, fp16) for additional 2-4x latency reduction and memory savings.","intents":["deploy NER models on CPU-only servers or edge devices without GPU infrastructure","reduce inference latency to <100ms per document for real-time applications","minimize model size for mobile or embedded deployment (e.g., on-device processing)"],"best_for":["teams with CPU-only infrastructure or limited GPU budgets","edge ML and mobile developers requiring on-device NER","cost-sensitive deployments where GPU instances are prohibitively expensive"],"limitations":["CPU inference is still slower than GPU (typically 50-200ms per document vs 5-20ms on GPU); not suitable for ultra-low-latency requirements","Quantization (int8) may reduce accuracy by 1-3% on edge cases; requires validation per domain","Memory footprint is still ~1GB for inference (model + runtime); not suitable for extremely memory-constrained devices (<512MB)","Multi-threaded CPU inference requires careful tuning of thread pools; suboptimal configuration can reduce throughput","No built-in batching optimization for CPU; manual batch size tuning required to balance latency and throughput"],"requires":["Python 3.6+ or ONNX Runtime for non-Python environments","CPU with AVX2 or SSE4.2 support (most modern CPUs)","optional: quantization tools for int8/fp16 optimization"],"input_types":["text sequences"],"output_types":["token-level entity predictions","logits/probabilities"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-dslim--distilbert-ner__cap_7","uri":"capability://tool.use.integration.integration.with.huggingface.transformers.pipeline.api","name":"integration with huggingface transformers pipeline api","description":"Provides a high-level Python API via HuggingFace's pipeline abstraction, enabling one-line inference without manual tokenization, tensor handling, or post-processing. The pipeline automatically handles text preprocessing, batching, and output formatting; supports both PyTorch and TensorFlow backends with automatic device selection (GPU if available, fallback to CPU).","intents":["quickly prototype NER applications without deep transformer knowledge","integrate entity extraction into Python applications with minimal boilerplate code","switch between different NER models or backends without code changes"],"best_for":["Python developers new to transformers or NLP","rapid prototyping and proof-of-concept projects","applications where inference simplicity is prioritized over fine-grained control"],"limitations":["Pipeline abstraction adds ~50-100ms overhead per inference due to automatic batching and post-processing logic","Limited customization; advanced use cases (custom attention patterns, intermediate layer extraction) require dropping down to lower-level APIs","Automatic device selection may not be optimal; GPU memory management is not fine-tuned for specific hardware","Pipeline caches model in memory; multiple pipelines with different models consume significant memory","Output format is fixed; custom output structures require manual post-processing"],"requires":["Python 3.6+","transformers library (>=4.0.0)","torch or tensorflow"],"input_types":["raw text strings","list of text strings"],"output_types":["list of dictionaries with entity, score, index, word, start, end keys","structured entity spans with character offsets"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+","transformers library (>=4.0.0)","torch or tensorflow backend (CPU or GPU)","input text in English language","HuggingFace model hub access or local model weights (~268MB for safetensors format)","transformers library with DataCollator or manual padding logic","sufficient GPU/CPU memory for largest batch size × max sequence length","PyTorch or TensorFlow installed","transformers library with ONNX export utilities","onnx and onnxruntime packages"],"failure_modes":["Fixed vocabulary of ~28K tokens from DistilBERT base; out-of-vocabulary words are subword-tokenized, potentially splitting entity names across multiple tokens","Trained exclusively on CONLL2003 English dataset; performance degrades significantly on non-English text or domain-specific entities (medical, legal, financial terminology)","Maximum sequence length of 512 tokens; documents longer than ~400 words require sliding-window or truncation strategies","No built-in confidence scoring or uncertainty quantification; all predictions treated as equally confident","Token-level predictions can produce malformed entity spans (e.g., B-PER followed by B-PER without I-PER); post-processing required for clean entity extraction","Batch size must be tuned per hardware; too large causes OOM errors; too small wastes parallelization benefits","Padding tokens (special [PAD] token) still consume computation; highly variable sequence lengths in a batch reduce efficiency gains","No built-in distributed batching across multiple GPUs or TPUs; requires external orchestration (Ray, Spark, etc.)","Attention mask computation adds ~5-10% overhead per batch to handle variable lengths","ONNX export requires manual conversion; not all HuggingFace features (e.g., custom attention patterns) translate to ONNX","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6170721270539024,"quality":0.26,"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":"2026-04-22T08:08:28.377Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":350107,"model_likes":50}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dslim--distilbert-ner","compare_url":"https://unfragile.ai/compare?artifact=dslim--distilbert-ner"}},"signature":"Nv1MrRt7b/Y270Nmba0UfsFRnorixHEL//QI0wod+YCASvON/USQhwJ0SoVVLHvPSeRpDVOI1RM7xAMFXt4YAw==","signedAt":"2026-06-21T01:10:28.968Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dslim--distilbert-ner","artifact":"https://unfragile.ai/dslim--distilbert-ner","verify":"https://unfragile.ai/api/v1/verify?slug=dslim--distilbert-ner","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"}}