{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl","slug":"davlan--bert-base-multilingual-cased-ner-hrl","name":"bert-base-multilingual-cased-ner-hrl","type":"model","url":"https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl","page_url":"https://unfragile.ai/davlan--bert-base-multilingual-cased-ner-hrl","categories":["model-training"],"tags":["transformers","pytorch","tf","onnx","safetensors","bert","token-classification","license:afl-3.0","endpoints_compatible","deploy:azure","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_0","uri":"capability://data.processing.analysis.multilingual.named.entity.recognition.with.token.level.classification","name":"multilingual named entity recognition with token-level classification","description":"Performs token-level sequence labeling across 10+ languages using a fine-tuned BERT-base-multilingual-cased backbone. The model applies subword tokenization via WordPiece, processes sequences through 12 transformer layers with 768-dimensional embeddings, and outputs BIO/BIOES tags (Person, Organization, Location, Miscellaneous) for each token. Handles variable-length sequences up to 512 tokens with attention masking for padding tokens.","intents":["extract named entities from multilingual text documents without language-specific preprocessing","identify person names, organizations, and locations across documents in mixed-language corpora","build NER pipelines that work across 10+ languages with a single model checkpoint","integrate entity extraction into document processing workflows without maintaining separate language-specific models"],"best_for":["NLP teams building multilingual information extraction systems","developers creating document processing pipelines for non-English corpora","researchers prototyping cross-lingual NER without language-specific fine-tuning","production systems requiring low-latency entity extraction across diverse language inputs"],"limitations":["Performance degrades on languages underrepresented in training data (e.g., low-resource African languages show ~5-10% F1 drop vs high-resource languages)","512-token sequence limit requires document chunking for longer texts, risking entity boundary splits","Subword tokenization can fragment rare entity names, requiring post-processing to reconstruct token-level predictions to span-level entities","No domain adaptation without fine-tuning — performance on specialized domains (medical, legal) not guaranteed","Cased model is sensitive to capitalization; all-lowercase or all-uppercase text may degrade accuracy by 3-7%"],"requires":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ OR TensorFlow 2.4+","minimum 2GB GPU VRAM for batch inference (CPU inference supported but ~10x slower)","HuggingFace model hub access or local model checkpoint (~440MB disk space)"],"input_types":["raw text strings (UTF-8 encoded)","pre-tokenized sequences (list of strings)","text with existing whitespace/punctuation"],"output_types":["token-level BIO tags (B-PER, I-PER, B-ORG, I-ORG, B-LOC, I-LOC, B-MISC, I-MISC, O)","confidence scores per token (softmax probabilities over tag classes)","span-level entities (reconstructed from token predictions with start/end character offsets)"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_1","uri":"capability://data.processing.analysis.batch.token.classification.with.attention.visualization","name":"batch token classification with attention visualization","description":"Processes multiple documents in parallel through the transformer stack with dynamic batching, returning per-token logits and attention weights from all 12 layers. Supports variable-length sequences within a batch via padding and attention masking, enabling inspection of which input tokens influenced each prediction through attention head visualization.","intents":["process large document collections efficiently by batching variable-length texts","debug NER predictions by visualizing which tokens the model attended to when making entity decisions","extract attention patterns for linguistic analysis or model interpretability studies","implement confidence-based filtering by thresholding softmax probabilities across batch predictions"],"best_for":["data engineers building high-throughput NER pipelines processing 1000s of documents","ML researchers analyzing transformer attention patterns for entity recognition","teams requiring explainability in NER predictions for compliance or debugging","production systems needing per-token confidence scores for downstream filtering"],"limitations":["Attention visualization adds ~15-20% computational overhead; not suitable for real-time single-token inference","Batch size limited by GPU VRAM — typical max batch size 32-64 on 8GB GPU, requiring careful memory management","Attention weights from intermediate layers don't directly explain final predictions (attention is not explanation); requires additional probing to correlate with output logits","No built-in batching optimization for sequences of vastly different lengths (e.g., 50 tokens vs 500 tokens in same batch wastes padding computation)"],"requires":["GPU with minimum 4GB VRAM for batch_size >= 16","transformers library with attention_output=True support (4.10+)","PyTorch or TensorFlow with autograd/tape enabled for gradient computation if fine-tuning"],"input_types":["list of text strings (variable length)","pre-tokenized sequences with token IDs","attention mask tensors (optional, auto-generated if not provided)"],"output_types":["logits tensor (batch_size, seq_length, num_tags)","attention weights tensor (batch_size, num_heads, seq_length, seq_length)","predicted tags per token with confidence scores"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_2","uri":"capability://data.processing.analysis.cross.lingual.entity.recognition.with.language.agnostic.embeddings","name":"cross-lingual entity recognition with language-agnostic embeddings","description":"Leverages BERT-base-multilingual-cased's shared vocabulary and embedding space across 104 languages to recognize entities in any language without language detection or model switching. The model encodes all languages into the same 768-dimensional space, allowing entities in one language to activate similar attention patterns as semantically equivalent entities in other languages.","intents":["extract entities from documents containing code-switched or mixed-language text without preprocessing","apply a single NER model to multilingual corpora without building language-specific pipelines","recognize entities in low-resource languages by leveraging transfer learning from high-resource languages","build language-agnostic entity extraction for international applications without language routing logic"],"best_for":["global companies processing documents in 10+ languages with unified infrastructure","NLP teams supporting low-resource languages (Swahili, Tagalog, Vietnamese) without dedicated models","applications handling code-switched text (e.g., Hinglish, Spanglish) without explicit language detection","research projects studying cross-lingual transfer in sequence labeling tasks"],"limitations":["Performance varies significantly by language — high-resource languages (English, German, French) achieve 90+ F1, while low-resource languages may drop to 70-80% F1","No language-specific fine-tuning means model cannot leverage language-particular morphological or syntactic patterns","Shared vocabulary means rare words in low-resource languages may be heavily subword-tokenized, reducing entity recognition accuracy","Code-switching performance untested; model may struggle with rapid language alternation within single sentences"],"requires":["Python 3.7+","transformers library 4.0+","UTF-8 text encoding support","no language detection library required (unlike language-specific pipelines)"],"input_types":["text in any of 104 supported languages","code-switched text (mixed languages)","text with non-Latin scripts (Arabic, Chinese, Cyrillic, Devanagari, etc.)"],"output_types":["BIO tags language-agnostic (same tag set regardless of input language)","confidence scores per token","language-independent entity spans"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_3","uri":"capability://code.generation.editing.fine.tuning.and.domain.adaptation.for.specialized.entity.types","name":"fine-tuning and domain adaptation for specialized entity types","description":"Supports transfer learning by unfreezing transformer layers and training on domain-specific annotated data (e.g., medical, legal, financial entities). Uses standard PyTorch/TensorFlow training loops with cross-entropy loss over token-level predictions, allowing practitioners to adapt the pre-trained weights to custom entity schemas (e.g., DRUG, DISEASE, SYMPTOM instead of generic PER/ORG/LOC).","intents":["adapt the model to recognize domain-specific entities (medical diagnoses, legal entities, financial instruments) with minimal labeled data","extend the model to custom entity types beyond the 4 pre-trained classes (PER, ORG, LOC, MISC)","improve accuracy on specialized text (clinical notes, contracts, earnings reports) through domain-specific fine-tuning","build production NER systems for vertical-specific applications without training from scratch"],"best_for":["domain experts building NER for healthcare, legal, or finance with 500-5000 labeled examples","teams needing custom entity schemas beyond generic PER/ORG/LOC taxonomy","practitioners with limited labeled data who want to leverage pre-trained multilingual knowledge","companies building vertical-specific NLP products (medical records processing, contract analysis)"],"limitations":["Requires 500+ labeled examples per entity type for stable fine-tuning; fewer examples risk overfitting","Fine-tuning on GPU takes 10-60 minutes depending on dataset size and learning rate; no built-in hyperparameter optimization","Catastrophic forgetting risk — aggressive fine-tuning can degrade performance on original entity types (PER, ORG, LOC)","No built-in active learning or data augmentation; practitioners must manually curate training data","Custom entity schemas require retraining; no zero-shot entity type adaptation"],"requires":["Python 3.7+","PyTorch 1.9+ or TensorFlow 2.4+","GPU with 4GB+ VRAM for fine-tuning","annotated training data in BIO/BIOES format (500+ examples recommended)","transformers library with Trainer API (4.0+) or custom training loop"],"input_types":["annotated text in BIO/BIOES format (token-tag pairs)","custom entity type definitions","training hyperparameters (learning rate, batch size, epochs)"],"output_types":["fine-tuned model checkpoint with custom entity types","training metrics (loss, F1, precision, recall per entity type)","predictions on custom entity types"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_4","uri":"capability://automation.workflow.onnx.and.tensorflow.export.for.production.deployment","name":"onnx and tensorflow export for production deployment","description":"Exports the PyTorch BERT model to ONNX and TensorFlow SavedModel formats for deployment in heterogeneous production environments. ONNX export converts transformer operations to standardized graph format compatible with ONNX Runtime (C++, Java, .NET), while TensorFlow export enables deployment on TensorFlow Serving, TensorFlow Lite (mobile), or TensorFlow.js (browser). Maintains numerical equivalence within 1e-5 precision across formats.","intents":["deploy NER models in production environments without PyTorch dependency (e.g., Java backends, C++ services)","run inference on mobile devices or browsers using TensorFlow Lite or TensorFlow.js","integrate with existing TensorFlow Serving infrastructure for scalable serving","reduce model size and latency through ONNX Runtime optimization and quantization"],"best_for":["DevOps teams deploying models to Java/C++ microservices without Python runtime","mobile developers building on-device NER for iOS/Android applications","teams using TensorFlow Serving for model serving infrastructure","practitioners optimizing inference latency through ONNX Runtime or quantization"],"limitations":["ONNX export requires onnx and onnx-simplifier libraries; export process adds 2-5 minutes overhead","TensorFlow Lite conversion requires additional quantization step; full-precision TFLite model is 440MB (too large for most mobile apps)","Numerical precision differences between PyTorch and ONNX/TF can cause 0.5-1% F1 variance on edge cases","No built-in support for dynamic batch sizes in ONNX; requires separate model variants for different batch sizes","TensorFlow.js version requires additional optimization (pruning, quantization) to run in browser without 5-10s load time"],"requires":["PyTorch 1.9+ for ONNX export","onnx >= 1.10, onnx-simplifier >= 0.4 for ONNX export","TensorFlow 2.4+ for TensorFlow export","ONNX Runtime 1.8+ for ONNX inference","TensorFlow Serving 2.4+ for production serving (optional)"],"input_types":["PyTorch model checkpoint (.pt, .pth)","model configuration (config.json)"],"output_types":["ONNX model (.onnx)","TensorFlow SavedModel (directory with saved_model.pb + variables/)","TensorFlow Lite model (.tflite, quantized or full-precision)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-davlan--bert-base-multilingual-cased-ner-hrl__cap_5","uri":"capability://automation.workflow.inference.optimization.through.quantization.and.pruning","name":"inference optimization through quantization and pruning","description":"Supports post-training quantization (INT8, FP16) and structured pruning to reduce model size and inference latency without retraining. INT8 quantization reduces model from 440MB to 110MB and speeds up inference by 2-4x on CPU through reduced memory bandwidth and faster integer operations. FP16 quantization provides 2x speedup on GPUs with minimal accuracy loss (<0.5% F1 drop).","intents":["reduce model size from 440MB to <150MB for deployment on resource-constrained devices","accelerate CPU inference by 2-4x through INT8 quantization for latency-sensitive applications","optimize GPU inference for batch processing by using FP16 mixed precision","enable on-device inference on mobile/edge devices with limited memory and compute"],"best_for":["teams deploying NER on edge devices (IoT, mobile) with memory constraints","practitioners optimizing inference latency for real-time applications","companies reducing cloud inference costs through faster CPU-based serving","mobile developers building on-device NER without cloud dependency"],"limitations":["INT8 quantization requires calibration on representative data; poor calibration data can cause 2-5% F1 degradation","Quantized models lose interpretability — attention weights become integer-quantized, making visualization less informative","Quantization is post-training; cannot be applied during fine-tuning (quantization-aware training not supported)","Pruning requires retraining to recover accuracy; unstructured pruning adds complexity to deployment","FP16 quantization on older GPUs (pre-Volta) may not provide speedup due to lack of native FP16 support"],"requires":["PyTorch 1.9+ with quantization support","TensorFlow 2.4+ with TensorFlow Lite quantization tools","calibration dataset (100-1000 representative examples) for INT8 quantization","ONNX Runtime 1.8+ with quantization support for ONNX models"],"input_types":["pre-trained model checkpoint","calibration dataset (unlabeled text)","quantization configuration (bit-width, calibration method)"],"output_types":["quantized model checkpoint (INT8 or FP16)","quantization metrics (accuracy loss, speedup measurements)","deployment-ready model for ONNX Runtime or TensorFlow Lite"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","transformers library 4.0+","PyTorch 1.9+ OR TensorFlow 2.4+","minimum 2GB GPU VRAM for batch inference (CPU inference supported but ~10x slower)","HuggingFace model hub access or local model checkpoint (~440MB disk space)","GPU with minimum 4GB VRAM for batch_size >= 16","transformers library with attention_output=True support (4.10+)","PyTorch or TensorFlow with autograd/tape enabled for gradient computation if fine-tuning","UTF-8 text encoding support","no language detection library required (unlike language-specific pipelines)"],"failure_modes":["Performance degrades on languages underrepresented in training data (e.g., low-resource African languages show ~5-10% F1 drop vs high-resource languages)","512-token sequence limit requires document chunking for longer texts, risking entity boundary splits","Subword tokenization can fragment rare entity names, requiring post-processing to reconstruct token-level predictions to span-level entities","No domain adaptation without fine-tuning — performance on specialized domains (medical, legal) not guaranteed","Cased model is sensitive to capitalization; all-lowercase or all-uppercase text may degrade accuracy by 3-7%","Attention visualization adds ~15-20% computational overhead; not suitable for real-time single-token inference","Batch size limited by GPU VRAM — typical max batch size 32-64 on 8GB GPU, requiring careful memory management","Attention weights from intermediate layers don't directly explain final predictions (attention is not explanation); requires additional probing to correlate with output logits","No built-in batching optimization for sequences of vastly different lengths (e.g., 50 tokens vs 500 tokens in same batch wastes padding computation)","Performance varies significantly by language — high-resource languages (English, German, French) achieve 90+ F1, while low-resource languages may drop to 70-80% F1","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6140275032018259,"quality":0.37,"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":"2026-05-03T14:23:01.785Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":287100,"model_likes":82}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=davlan--bert-base-multilingual-cased-ner-hrl","compare_url":"https://unfragile.ai/compare?artifact=davlan--bert-base-multilingual-cased-ner-hrl"}},"signature":"xAPa1Yex/6huHXsSh3NrhD4BiXI8GqMhaocluPlJ3ABlhs4r5qXgC89vTv38OYRi0hKvdpueef1YEA+GR8CLBw==","signedAt":"2026-06-22T01:57:51.801Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/davlan--bert-base-multilingual-cased-ner-hrl","artifact":"https://unfragile.ai/davlan--bert-base-multilingual-cased-ner-hrl","verify":"https://unfragile.ai/api/v1/verify?slug=davlan--bert-base-multilingual-cased-ner-hrl","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"}}