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Uses subword tokenization via BPE to handle OOV words, then aggregates predictions back to word-level entities. Trained on CoNLL2003 dataset with standard BIO tagging scheme.","intents":["Extract person names, organizations, locations, and miscellaneous entities from unstructured English text","Build NER pipelines that integrate with downstream NLP tasks like relation extraction or knowledge graph construction","Evaluate entity recognition performance on English corpora with standard CoNLL2003 metrics","Deploy production NER systems with pre-trained weights avoiding expensive fine-tuning"],"best_for":["NLP engineers building information extraction pipelines for English documents","Teams needing out-of-the-box entity recognition without domain-specific fine-tuning","Researchers benchmarking against CoNLL2003-trained baselines","Developers integrating NER into multi-stage NLP workflows (e.g., entity linking, relation extraction)"],"limitations":["English-only — no multilingual support despite RoBERTa's multilingual variants being available","Fixed to CoNLL2003 entity schema (4 entity types + O tag) — cannot recognize custom entity types without fine-tuning","Subword tokenization can fragment rare entities across multiple tokens, requiring post-processing to reconstruct word-level spans","No confidence scores or uncertainty quantification per token — only hard predictions","Inference latency ~50-100ms per sentence on CPU, requires GPU for batch processing >32 samples","Context window limited to 512 tokens (RoBERTa max) — longer documents must be chunked with potential entity boundary loss"],"requires":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","4GB+ RAM for model loading (8GB+ recommended for batch inference)","HuggingFace model hub access or local model weights"],"input_types":["raw text (string)","pre-tokenized text (list of tokens)","batched text sequences"],"output_types":["token-level labels (BIO tags per token)","entity spans (character offsets + entity type)","logits (raw classification scores per token per class)"],"categories":["data-processing-analysis","nlp-information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jean-baptiste--roberta-large-ner-english__cap_1","uri":"capability://automation.workflow.multi.format.model.export.and.inference.optimization","name":"multi-format model export and inference optimization","description":"Supports export to ONNX, SafeTensors, and native PyTorch/TensorFlow formats, enabling deployment across heterogeneous inference environments (edge devices, cloud APIs, mobile). ONNX export enables quantization and graph optimization; SafeTensors format provides faster loading and better security than pickle-based PyTorch checkpoints. Integrates with HuggingFace Inference Endpoints for serverless deployment.","intents":["Deploy the NER model to production inference APIs without managing GPU infrastructure","Optimize model size and latency for edge deployment or resource-constrained environments","Integrate NER into polyglot ML stacks using ONNX Runtime (C++, Java, Node.js)","Ensure reproducible model loading and avoid pickle deserialization vulnerabilities"],"best_for":["MLOps teams deploying models across cloud providers (Azure, AWS, GCP)","Edge ML engineers targeting mobile or IoT devices with ONNX Runtime","Organizations requiring model security and auditability (SafeTensors avoids arbitrary code execution)","Polyglot teams using non-Python inference stacks (C++, Java, Node.js backends)"],"limitations":["ONNX export requires manual opset version management — not all RoBERTa operations map to all ONNX opsets","SafeTensors format is newer — some legacy inference frameworks don't support it yet","Quantization (int8, fp16) requires additional post-export optimization steps; no built-in quantized checkpoint","HuggingFace Inference Endpoints incur per-request costs (~$0.0001-0.001 per inference depending on tier)","ONNX Runtime inference may be 10-20% slower than native PyTorch on GPU due to operator fusion limitations"],"requires":["transformers library (>=4.20.0 for SafeTensors support)","onnx (>=1.12.0) and onnxruntime (>=1.13.0) for ONNX export/inference","PyTorch or TensorFlow depending on target format","HuggingFace account for Inference Endpoints (optional)"],"input_types":["model checkpoint (PyTorch .pt or TensorFlow .h5)","HuggingFace model identifier string"],"output_types":["ONNX model (.onnx file)","SafeTensors checkpoint (.safetensors file)","PyTorch state dict (.pt file)","TensorFlow SavedModel directory"],"categories":["automation-workflow","model-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jean-baptiste--roberta-large-ner-english__cap_2","uri":"capability://automation.workflow.batch.inference.with.dynamic.batching.and.padding.optimization","name":"batch inference with dynamic batching and padding optimization","description":"Processes multiple text sequences in parallel through the RoBERTa encoder, automatically padding variable-length inputs to the longest sequence in the batch and masking padding tokens to prevent attention leakage. Uses attention masks and token type IDs to handle mixed-length batches efficiently. Supports both eager execution and graph-mode optimization for throughput maximization.","intents":["Process large document collections (100s-1000s of texts) with 5-10x throughput improvement vs single-sample inference","Minimize GPU memory fragmentation and latency variance in production inference pipelines","Implement efficient data loading loops that saturate GPU compute without manual batching logic","Benchmark NER performance on standard evaluation sets with minimal inference time"],"best_for":["Data engineers processing document corpora for entity extraction at scale","ML engineers optimizing inference cost and latency in production systems","Researchers evaluating model performance on benchmark datasets","Teams building real-time NER APIs that need predictable p99 latency"],"limitations":["Padding overhead increases with batch size heterogeneity — highly variable-length batches waste compute on padding tokens","Dynamic batching requires buffering requests, adding 10-50ms latency vs single-sample inference","Memory usage scales linearly with batch size — batch_size=64 requires ~8GB VRAM on GPU","No built-in bucketing by sequence length — users must implement custom batching for optimal efficiency","Attention mask computation adds ~5% overhead per batch"],"requires":["transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","GPU with >=4GB VRAM for batch_size>=16 (CPU inference possible but 10-50x slower)","DataLoader or equivalent batching framework"],"input_types":["list of text strings (variable length)","pre-tokenized sequences (list of token lists)","PyTorch DataLoader or TensorFlow Dataset"],"output_types":["batched token-level labels (shape: [batch_size, seq_length])","batched logits (shape: [batch_size, seq_length, num_classes])","batched entity spans with confidence scores"],"categories":["automation-workflow","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jean-baptiste--roberta-large-ner-english__cap_3","uri":"capability://code.generation.editing.fine.tuning.on.custom.entity.schemas.and.domain.specific.corpora","name":"fine-tuning on custom entity schemas and domain-specific corpora","description":"Enables transfer learning by unfreezing the RoBERTa encoder and training the classification head (and optionally encoder layers) on custom labeled datasets with different entity types. Uses standard supervised learning with cross-entropy loss over token-level predictions. Supports gradient accumulation, mixed precision training, and learning rate scheduling for efficient fine-tuning on limited labeled data.","intents":["Adapt the pre-trained model to recognize domain-specific entities (e.g., medical terms, product names, legal entities) not in CoNLL2003","Improve performance on non-English or non-news text by fine-tuning on domain corpora","Build custom entity recognition systems with minimal labeled data (100s-1000s of examples)","Implement active learning pipelines where the model is iteratively retrained on newly labeled examples"],"best_for":["NLP engineers building domain-specific NER systems (biomedical, legal, financial, e-commerce)","Teams with proprietary labeled datasets wanting to leverage pre-trained weights","Researchers exploring transfer learning and domain adaptation for sequence labeling","Startups building vertical AI products with custom entity recognition requirements"],"limitations":["Requires manually annotated training data in BIO or BIOES format — no weak supervision or distant labeling built-in","Fine-tuning on small datasets (<500 examples) risks overfitting — requires careful regularization (dropout, early stopping, weight decay)","Changing entity schema requires retraining the classification head — no zero-shot entity type adaptation","Fine-tuning on GPU requires 8GB+ VRAM; CPU fine-tuning is impractical (>1 hour per epoch for 1000 examples)","No built-in handling of class imbalance — rare entity types may be under-recognized without weighted loss"],"requires":["Python 3.7+","transformers (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","Labeled training data in BIO/BIOES format (CoNLL format or equivalent)","GPU with >=8GB VRAM for efficient fine-tuning","torch or tensorflow training utilities (Trainer, fit API)"],"input_types":["annotated text corpus (BIO-tagged sentences)","CoNLL-format files","custom Python datasets with token and label lists"],"output_types":["fine-tuned model checkpoint (PyTorch or TensorFlow)","training metrics (loss, F1, precision, recall per entity type)","evaluation results on validation set"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jean-baptiste--roberta-large-ner-english__cap_4","uri":"capability://data.processing.analysis.entity.span.extraction.with.character.level.offset.mapping","name":"entity span extraction with character-level offset mapping","description":"Converts token-level BIO predictions back to word-level entity spans with precise character offsets in the original text. Handles subword tokenization artifacts (BPE fragments) by merging adjacent subword tokens and mapping back to character positions. Produces structured output with entity type, text, and start/end character indices for downstream processing.","intents":["Extract entity spans as structured data (text, type, character offsets) for knowledge graph construction or database ingestion","Highlight entities in UI applications with precise character positions for text annotation","Feed entity spans to downstream NLP tasks (linking, relation extraction) that require exact text boundaries","Evaluate entity recognition against gold-standard annotations using standard metrics (precision, recall, F1)"],"best_for":["Information extraction engineers building end-to-end pipelines from text to structured data","UI/UX developers building text annotation or entity highlighting interfaces","Data scientists evaluating NER model performance with standard metrics","Knowledge graph engineers extracting entities for graph construction"],"limitations":["Subword tokenization can create ambiguous entity boundaries — BPE may split entities across multiple tokens, requiring heuristics to merge","Character offset mapping assumes consistent tokenization between training and inference — custom tokenizers may produce misaligned offsets","No built-in handling of overlapping entities — BIO scheme assumes non-overlapping entity spans","Whitespace handling varies across tokenizers — offset calculation may be off by 1-2 characters with unusual whitespace","No confidence scores per entity span — only hard predictions from token-level logits"],"requires":["transformers library (>=4.0.0)","tokenizer from the model (AutoTokenizer)","original text string (required for character offset mapping)","BIO-tagged token predictions"],"input_types":["token-level BIO labels (list of strings)","original text (string)","token-to-character mapping (from tokenizer)"],"output_types":["entity spans (list of dicts with 'text', 'type', 'start', 'end' keys)","structured JSON with entity metadata","CoNLL-format output for evaluation"],"categories":["data-processing-analysis","text-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-jean-baptiste--roberta-large-ner-english__cap_5","uri":"capability://data.processing.analysis.evaluation.against.standard.ner.benchmarks.with.seqeval.metrics","name":"evaluation against standard ner benchmarks with seqeval metrics","description":"Computes standard sequence labeling metrics (precision, recall, F1) at both token and entity span levels using the seqeval library. Handles BIO tag scheme validation, merges adjacent tags of the same type, and reports per-entity-type performance. Supports both strict matching (exact span boundaries) and partial matching (overlapping spans).","intents":["Benchmark model performance on CoNLL2003 test set or custom evaluation corpora","Compare fine-tuned models against baseline to measure domain adaptation gains","Identify entity types with poor performance for targeted improvement","Report standardized metrics for research papers or model cards"],"best_for":["Researchers publishing NER results with standard metrics","ML engineers validating model improvements during fine-tuning","Teams comparing multiple NER approaches on the same evaluation set","Model card authors documenting performance characteristics"],"limitations":["seqeval only supports BIO/BIOES schemes — other tagging schemes (IOBES, BILOU) require custom metric code","Metrics assume non-overlapping entities — cannot evaluate multi-label entity recognition","No confidence thresholding — all predictions treated equally regardless of model confidence","Span-level metrics are strict (exact boundary match required) — no partial credit for near-miss predictions","Per-entity-type metrics can be noisy with imbalanced datasets (rare entity types have high variance)"],"requires":["seqeval library (>=1.2.2)","predictions and gold labels in BIO format","transformers library (optional, for integration with Trainer)"],"input_types":["predicted BIO labels (list of lists)","gold BIO labels (list of lists)","optional: entity type list for per-type reporting"],"output_types":["overall F1, precision, recall (float)","per-entity-type metrics (dict)","confusion matrix (optional)","detailed error analysis (optional)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","transformers library (>=4.0.0)","PyTorch (>=1.9.0) or TensorFlow (>=2.4.0)","4GB+ RAM for model loading (8GB+ recommended for batch inference)","HuggingFace model hub access or local model weights","transformers library (>=4.20.0 for SafeTensors support)","onnx (>=1.12.0) and onnxruntime (>=1.13.0) for ONNX export/inference","PyTorch or TensorFlow depending on target format","HuggingFace account for Inference Endpoints (optional)","GPU with >=4GB VRAM for batch_size>=16 (CPU inference possible but 10-50x slower)"],"failure_modes":["English-only — no multilingual support despite RoBERTa's multilingual variants being available","Fixed to CoNLL2003 entity schema (4 entity types + O tag) — cannot recognize custom entity types without fine-tuning","Subword tokenization can fragment rare entities across multiple tokens, requiring post-processing to reconstruct word-level spans","No confidence scores or uncertainty quantification per token — only hard predictions","Inference latency ~50-100ms per sentence on CPU, requires GPU for batch processing >32 samples","Context window limited to 512 tokens (RoBERTa max) — longer documents must be chunked with potential entity boundary loss","ONNX export requires manual opset version management — not all RoBERTa operations map to all ONNX opsets","SafeTensors format is newer — some legacy inference frameworks don't support it yet","Quantization (int8, fp16) requires additional post-export optimization steps; no built-in quantized checkpoint","HuggingFace Inference Endpoints incur per-request costs (~$0.0001-0.001 per inference depending on tier)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6197017976001208,"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":315178,"model_likes":79}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=jean-baptiste--roberta-large-ner-english","compare_url":"https://unfragile.ai/compare?artifact=jean-baptiste--roberta-large-ner-english"}},"signature":"3r7/pExm3iItyf16jBkt3vJR1mr3iETzyqv1UZ3/b7uyvDXWHLAniEwDjZvS0EJPwRBRXoom1rRrPOXOIJJvCg==","signedAt":"2026-06-20T13:01:58.501Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/jean-baptiste--roberta-large-ner-english","artifact":"https://unfragile.ai/jean-baptiste--roberta-large-ner-english","verify":"https://unfragile.ai/api/v1/verify?slug=jean-baptiste--roberta-large-ner-english","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"}}