{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-flair--ner-english-fast","slug":"flair--ner-english-fast","name":"ner-english-fast","type":"model","url":"https://huggingface.co/flair/ner-english-fast","page_url":"https://unfragile.ai/flair--ner-english-fast","categories":["model-training"],"tags":["flair","pytorch","token-classification","sequence-tagger-model","en","dataset:conll2003","region:us"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-flair--ner-english-fast__cap_0","uri":"capability://data.processing.analysis.fast.english.named.entity.recognition.via.token.classification","name":"fast english named entity recognition via token classification","description":"Performs sequence-level token classification to identify and label named entities (persons, organizations, locations, miscellaneous) in English text using a lightweight Flair-based PyTorch model. The model uses a BiLSTM-CRF architecture trained on the CoNLL-2003 dataset, optimized for inference speed through parameter reduction and quantization-friendly design. Outputs token-level predictions with entity type labels and confidence scores, enabling downstream entity extraction pipelines without requiring external NER services.","intents":["Extract person names, company names, and locations from unstructured English text for knowledge graph construction","Identify named entities in documents for information retrieval and document indexing systems","Build entity-aware search or recommendation systems that understand semantic entity relationships","Preprocess text for downstream NLP tasks that require entity-level semantic understanding","Run NER inference locally without cloud API dependencies for privacy-sensitive applications"],"best_for":["Teams building information extraction pipelines with strict latency requirements (<100ms per document)","Developers deploying NER on edge devices or resource-constrained environments","Organizations requiring on-premise NER without third-party API dependencies","Researchers prototyping entity-aware NLP systems with open-source tooling"],"limitations":["Trained exclusively on CoNLL-2003 English dataset — performance degrades significantly on domain-specific text (biomedical, legal, financial entities) not represented in training data","Fixed entity tagset (PER, ORG, LOC, MISC) — cannot be extended to custom entity types without retraining","No built-in handling of nested or overlapping entities — outputs flat, non-overlapping entity spans only","Inference latency scales linearly with document length — processing 10,000+ token documents may exceed real-time requirements on CPU-only hardware","No confidence calibration — raw model scores may not reflect true prediction uncertainty, requiring post-hoc calibration for high-stakes applications"],"requires":["PyTorch 1.9+ (CPU or CUDA-compatible GPU)","Flair library 0.11+ (pip install flair)","Python 3.7+","~500MB disk space for model weights download","Hugging Face transformers library 4.0+ (transitive dependency)"],"input_types":["raw text (string)","pre-tokenized sequences (list of tokens)","sentences (Flair Sentence objects with optional pre-computed embeddings)"],"output_types":["structured entity spans with token indices and entity type labels","confidence scores per entity (0.0-1.0)","BIO/BIOES tag sequences at token level","JSON-serializable entity dictionaries with text, type, and span metadata"],"categories":["data-processing-analysis","sequence-labeling","information-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-flair--ner-english-fast__cap_1","uri":"capability://data.processing.analysis.batch.entity.extraction.with.streaming.inference","name":"batch entity extraction with streaming inference","description":"Processes multiple documents or sentences in parallel batches through the token classifier, leveraging PyTorch's batching and Flair's streaming API to amortize model loading overhead and maximize GPU utilization. Supports variable-length sequences within a batch through dynamic padding, enabling efficient processing of heterogeneous document collections without manual sequence length management. Returns entity predictions for all documents in a single forward pass, reducing per-document latency overhead.","intents":["Process large document collections (1000s of documents) for bulk entity extraction with minimal latency overhead","Build real-time entity extraction APIs that handle concurrent requests by batching inference across multiple users","Implement streaming NER pipelines that continuously process document feeds (e.g., news articles, social media) with bounded memory usage","Optimize GPU utilization when deploying NER in production by batching heterogeneous document lengths"],"best_for":["Data engineering teams building ETL pipelines for entity extraction at scale (100K+ documents)","ML engineers optimizing inference throughput in production serving environments","Researchers processing large corpora for linguistic analysis or dataset creation"],"limitations":["Batch size tuning is hardware-dependent — optimal batch size varies from 8-256 depending on GPU memory and sequence lengths, requiring empirical profiling","Dynamic padding adds ~5-15% overhead per batch due to mask computation and variable tensor shapes","No built-in distributed inference — batching is single-GPU/CPU only; multi-GPU scaling requires external orchestration (Ray, Spark)","Memory usage grows quadratically with batch size and sequence length — OOM errors possible with large batches of long documents without careful resource management"],"requires":["PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU batching supported but slower)","Flair 0.11+ with batch processing utilities","Sufficient GPU memory (minimum 2GB for batch_size=32 with 512-token sequences)","Optional: Ray or Spark for distributed batch processing across multiple machines"],"input_types":["list of raw text strings","list of Flair Sentence objects","document batches with variable lengths (1-1000+ tokens per document)"],"output_types":["list of entity predictions per document","aggregated entity statistics (counts, type distributions)","streaming results (generator yielding entities as batches complete)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-flair--ner-english-fast__cap_2","uri":"capability://data.processing.analysis.multi.layer.contextual.entity.disambiguation.via.stacked.embeddings","name":"multi-layer contextual entity disambiguation via stacked embeddings","description":"Leverages Flair's stacked embedding architecture combining character-level CNNs, word embeddings (GloVe/FastText), and optional contextual embeddings (ELMo/BERT) to generate rich token representations that disambiguate entities based on surrounding context. The model learns to weight and combine these embedding layers during training, enabling it to resolve ambiguous entity references (e.g., 'Washington' as person vs. location) through contextual signals. Embeddings are computed once per document and cached, reducing redundant computation across multiple forward passes.","intents":["Disambiguate entity references in context-dependent scenarios (e.g., 'Apple Inc.' vs. 'apple fruit') using multi-layer semantic representations","Improve entity recognition accuracy on out-of-vocabulary or rare entities through character-level morphological understanding","Leverage pre-trained contextual knowledge (ELMo/BERT) to enhance entity predictions without full model retraining"],"best_for":["Applications requiring high entity recognition accuracy on diverse text domains (news, social media, technical documentation)","Teams with computational budget for embedding computation but needing faster inference than full transformer models"],"limitations":["Embedding computation adds 50-200ms overhead per document before token classification, dominating latency for short documents (<50 tokens)","Stacked embeddings require significant memory (500MB-2GB depending on embedding layers) — not suitable for extremely memory-constrained environments","Contextual embeddings (ELMo) are context-window limited (~256 tokens) — longer documents require sliding window approaches with potential boundary artifacts","No built-in embedding caching across documents — repeated entity contexts are re-embedded, wasting computation in high-repetition scenarios"],"requires":["PyTorch 1.9+","Flair 0.11+ with embedding utilities","Pre-trained embedding files (GloVe, FastText, ELMo) — ~500MB-2GB disk space","Optional: Hugging Face transformers for BERT contextual embeddings"],"input_types":["raw text strings with surrounding context","Flair Sentence objects with pre-computed embeddings"],"output_types":["entity predictions with contextual confidence scores","embedding vectors for downstream analysis or visualization"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-flair--ner-english-fast__cap_3","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":"Enables transfer learning by loading pre-trained weights and retraining the model on custom-labeled datasets with domain-specific entity types (e.g., biomedical entities: GENE, PROTEIN, DISEASE). The training pipeline uses Flair's corpus management and trainer API to handle annotation format conversion (CoNLL-BIO, CONLL-U), automatic hyperparameter scheduling, and early stopping based on validation metrics. Supports both full model retraining and parameter-efficient fine-tuning (LoRA-style adapters in newer Flair versions).","intents":["Adapt the pre-trained English NER model to recognize domain-specific entities (medical, legal, financial) using labeled in-domain data","Build custom NER models for specialized vocabularies without training from scratch, reducing data requirements and training time","Evaluate transfer learning effectiveness by comparing fine-tuned vs. from-scratch models on domain-specific benchmarks"],"best_for":["Domain experts with 500-5000 labeled examples seeking to build specialized NER systems","Teams migrating from rule-based entity extraction to learned models with minimal annotation overhead","Researchers studying transfer learning and domain adaptation in sequence labeling"],"limitations":["Requires manually annotated training data in BIO/BIOES format — no weak supervision or distant labeling support built-in","Fine-tuning on small datasets (<500 examples) risks overfitting — requires careful regularization (dropout, early stopping) and validation set curation","Entity type mismatch between source (CoNLL-2003: PER/ORG/LOC/MISC) and target domains requires manual label mapping or retraining from scratch","Training time scales with dataset size and sequence length — fine-tuning on 10K+ documents may require hours on CPU, minutes on GPU","No built-in active learning or annotation prioritization — requires external tools to identify high-value examples for labeling"],"requires":["PyTorch 1.9+ with CUDA 11.0+ for GPU training (CPU training extremely slow for >1000 examples)","Flair 0.11+ with trainer and corpus utilities","Annotated training data in CoNLL-BIO or CONLL-U format (minimum 100-500 examples for meaningful fine-tuning)","Validation and test sets (10-20% of training data) for hyperparameter tuning and evaluation","Python 3.7+"],"input_types":["annotated text files in CoNLL-BIO format (token \\t tag per line, blank lines between sentences)","CONLL-U formatted data with NER annotations in MISC column","Flair Corpus objects with train/dev/test splits"],"output_types":["fine-tuned model checkpoint (PyTorch .pt file)","training metrics (precision, recall, F1 per entity type)","evaluation reports with per-class performance and error analysis"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-flair--ner-english-fast__cap_4","uri":"capability://data.processing.analysis.entity.span.extraction.with.confidence.based.filtering","name":"entity span extraction with confidence-based filtering","description":"Extracts entity spans from token-level predictions by decoding the CRF output layer, which produces optimal tag sequences respecting BIO constraints (e.g., preventing invalid transitions like I-PER → I-ORG). Confidence scores are computed from the CRF's Viterbi path probabilities, enabling downstream filtering by confidence threshold to trade recall for precision. Supports multiple decoding strategies (greedy, beam search) and post-processing rules (entity merging, span boundary correction).","intents":["Extract entity mentions with associated confidence scores for downstream ranking or filtering in information retrieval systems","Build entity extraction pipelines that balance precision and recall by filtering low-confidence predictions","Identify uncertain entity predictions for human review or active learning annotation prioritization"],"best_for":["Production systems requiring confidence-based entity filtering to reduce false positives","Information extraction pipelines where entity quality directly impacts downstream task performance","Active learning systems that prioritize uncertain predictions for human annotation"],"limitations":["CRF confidence scores are not well-calibrated — raw probabilities may not reflect true prediction uncertainty, requiring empirical calibration on validation data","Confidence filtering introduces precision-recall tradeoff — no principled way to select optimal threshold without labeled validation data","Beam search decoding adds 20-50% latency overhead compared to greedy Viterbi decoding","Post-processing rules (entity merging, boundary correction) are heuristic and may introduce errors for edge cases (nested entities, entity abbreviations)"],"requires":["PyTorch 1.9+","Flair 0.11+ with CRF decoding utilities","Optional: validation data for confidence calibration"],"input_types":["token-level predictions from the NER model","CRF output scores (log probabilities per tag)"],"output_types":["entity spans with text, type, character offsets, and confidence scores","filtered entity lists based on confidence threshold","precision-recall curves for threshold selection"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["PyTorch 1.9+ (CPU or CUDA-compatible GPU)","Flair library 0.11+ (pip install flair)","Python 3.7+","~500MB disk space for model weights download","Hugging Face transformers library 4.0+ (transitive dependency)","PyTorch 1.9+ with CUDA 11.0+ for GPU acceleration (CPU batching supported but slower)","Flair 0.11+ with batch processing utilities","Sufficient GPU memory (minimum 2GB for batch_size=32 with 512-token sequences)","Optional: Ray or Spark for distributed batch processing across multiple machines","PyTorch 1.9+"],"failure_modes":["Trained exclusively on CoNLL-2003 English dataset — performance degrades significantly on domain-specific text (biomedical, legal, financial entities) not represented in training data","Fixed entity tagset (PER, ORG, LOC, MISC) — cannot be extended to custom entity types without retraining","No built-in handling of nested or overlapping entities — outputs flat, non-overlapping entity spans only","Inference latency scales linearly with document length — processing 10,000+ token documents may exceed real-time requirements on CPU-only hardware","No confidence calibration — raw model scores may not reflect true prediction uncertainty, requiring post-hoc calibration for high-stakes applications","Batch size tuning is hardware-dependent — optimal batch size varies from 8-256 depending on GPU memory and sequence lengths, requiring empirical profiling","Dynamic padding adds ~5-15% overhead per batch due to mask computation and variable tensor shapes","No built-in distributed inference — batching is single-GPU/CPU only; multi-GPU scaling requires external orchestration (Ray, Spark)","Memory usage grows quadratically with batch size and sequence length — OOM errors possible with large batches of long documents without careful resource management","Embedding computation adds 50-200ms overhead per document before token classification, dominating latency for short documents (<50 tokens)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.6154576526751855,"quality":0.2,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.9,"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":419623,"model_likes":26}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=flair--ner-english-fast","compare_url":"https://unfragile.ai/compare?artifact=flair--ner-english-fast"}},"signature":"4zbpnhoG4TUePGsdCoNbpHG14QX1MEAnAQ0XQKsy8yUEvp65A4f9Pt61Z9vPJbXtwKvXJ8txnJdw40gIJV4jBw==","signedAt":"2026-06-16T21:06:14.728Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/flair--ner-english-fast","artifact":"https://unfragile.ai/flair--ner-english-fast","verify":"https://unfragile.ai/api/v1/verify?slug=flair--ner-english-fast","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"}}