{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-flair","slug":"pypi-flair","name":"flair","type":"repo","url":"https://github.com/flairNLP/flair","page_url":"https://unfragile.ai/pypi-flair","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-flair__cap_0","uri":"capability://data.processing.analysis.contextual.string.embeddings.generation","name":"contextual-string-embeddings-generation","description":"Generates contextualized word and document embeddings using Flair's proprietary contextual string embedding approach, which combines bidirectional language models to produce position-aware vector representations that capture semantic meaning based on surrounding context. Unlike static embeddings, these are computed dynamically per token position, enabling the same word to have different representations depending on its usage context in a sentence.","intents":["Generate contextual embeddings for downstream NLP tasks without training custom models","Combine multiple embedding sources (word embeddings, transformer embeddings, Flair embeddings) into unified representations","Leverage pre-trained contextual embeddings for transfer learning across NLP tasks"],"best_for":["NLP practitioners needing strong baseline embeddings without extensive training","Researchers experimenting with embedding combinations for domain-specific tasks","Teams building production NLP pipelines requiring pre-computed contextual representations"],"limitations":["Contextual embeddings are computationally expensive to generate at inference time compared to static embeddings","Embedding dimensionality can be high when combining multiple sources, increasing memory footprint","Pre-trained models are language-specific; cross-lingual embeddings require separate models"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained embedding models (auto-downloaded on first use)"],"input_types":["raw text strings","Sentence objects with tokenized text"],"output_types":["PyTorch tensors (embedding vectors)","numpy arrays","attached to Token and Sentence objects as embedding attributes"],"categories":["data-processing-analysis","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_1","uri":"capability://data.processing.analysis.sequence.tagging.with.neural.networks","name":"sequence-tagging-with-neural-networks","description":"Trains and applies sequence tagging models (SequenceTagger) using PyTorch-based neural architectures that combine embeddings, recurrent layers (LSTM/GRU), and CRF decoders to predict token-level labels for tasks like NER, POS tagging, and chunking. The framework handles the full pipeline: tokenization, embedding lookup, forward pass through the neural network, and CRF decoding to ensure valid label sequences.","intents":["Train custom NER models on domain-specific datasets with minimal boilerplate","Apply pre-trained sequence tagging models to extract entities, POS tags, or chunks from text","Evaluate sequence tagging models using standard metrics (F1, precision, recall) with built-in evaluation harness"],"best_for":["NLP teams building production NER/POS systems without deep ML expertise","Researchers experimenting with sequence tagging architectures and hyperparameters","Domain practitioners (biomedical, legal, finance) needing to adapt pre-trained models to specialized text"],"limitations":["SequenceTagger assumes token-level predictions; nested or overlapping entities require post-processing","CRF decoder adds ~50-100ms latency per sentence during inference due to dynamic programming","Training requires GPU for reasonable throughput; CPU training is prohibitively slow for large datasets","No built-in support for multi-token entities without explicit BIO/BIOES tagging scheme"],"requires":["Python 3.7+","PyTorch 1.9+","Annotated training data in CoNLL or Flair format","GPU recommended (CUDA 11.0+ or Apple Silicon)"],"input_types":["raw text strings","pre-tokenized Sentence objects","CoNLL-formatted files","Flair-formatted datasets"],"output_types":["predicted labels attached to Token objects","confidence scores per label","evaluation metrics (F1, precision, recall, per-class scores)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_10","uri":"capability://data.processing.analysis.dataset.loading.and.preprocessing","name":"dataset-loading-and-preprocessing","description":"Provides utilities for loading, preprocessing, and managing NLP datasets in multiple formats (CoNLL, Flair format, CSV, JSON) with automatic handling of train/validation/test splits, label encoding, and data augmentation. The framework includes dataset classes for common NLP tasks (NER, POS tagging, text classification) that handle data loading, tokenization, and label mapping, reducing boilerplate code for dataset preparation.","intents":["Load annotated NLP datasets from standard formats (CoNLL, Flair) without custom parsing code","Preprocess and normalize text data (lowercasing, special character handling, tokenization) for NLP tasks","Create train/validation/test splits and handle class imbalance in classification datasets"],"best_for":["NLP practitioners building models on standard datasets (CoNLL, SemEval, etc.)","Teams migrating datasets from other frameworks (spaCy, HuggingFace) to Flair","Researchers experimenting with different dataset splits and preprocessing strategies"],"limitations":["Limited support for custom dataset formats; requires manual conversion to Flair format","No built-in support for streaming large datasets; entire dataset must fit in memory","Data augmentation is limited to basic techniques (token replacement, sentence shuffling); advanced augmentation requires custom code","No built-in support for handling imbalanced datasets (class weighting, oversampling); requires manual configuration"],"requires":["Python 3.7+","Annotated dataset in supported format (CoNLL, Flair, CSV, JSON)","Sufficient memory to load entire dataset"],"input_types":["CoNLL-formatted files","Flair-formatted datasets","CSV/JSON files with text and label columns","raw text files"],"output_types":["Flair Corpus objects with train/validation/test splits","preprocessed Sentence and Token objects","label mappings and statistics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_11","uri":"capability://automation.workflow.model.training.with.hyperparameter.tuning","name":"model-training-with-hyperparameter-tuning","description":"Provides a unified training framework for all Flair models with built-in support for hyperparameter tuning, learning rate scheduling, gradient clipping, early stopping, and checkpoint management. The trainer handles batch creation, loss computation, backpropagation, and validation, abstracting away PyTorch boilerplate. Supports both grid search and random search for hyperparameter optimization, with automatic tracking of best models and training metrics.","intents":["Train NLP models with minimal boilerplate code and automatic hyperparameter management","Perform hyperparameter tuning to optimize model performance on validation sets","Monitor training progress with automatic logging and checkpoint management"],"best_for":["NLP practitioners building models without deep PyTorch expertise","Teams needing rapid model development with automatic hyperparameter tuning","Researchers experimenting with different model architectures and training strategies"],"limitations":["Hyperparameter search is limited to grid/random search; no Bayesian optimization or advanced search strategies","Training is single-GPU only; distributed training across multiple GPUs requires custom setup","No built-in support for mixed precision training or gradient accumulation for large batch sizes","Early stopping is based on validation loss; no support for custom stopping criteria"],"requires":["Python 3.7+","PyTorch 1.9+","GPU recommended (CUDA 11.0+ or Apple Silicon)","Annotated training dataset"],"input_types":["Flair Corpus objects with train/validation/test splits","model configuration (architecture, hyperparameters)","training hyperparameters (learning rate, batch size, epochs)"],"output_types":["trained model checkpoints","training metrics (loss, accuracy, F1)","best model based on validation performance","hyperparameter search results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_12","uri":"capability://data.processing.analysis.model.evaluation.with.standard.metrics","name":"model-evaluation-with-standard-metrics","description":"Computes standard NLP evaluation metrics (F1, precision, recall, accuracy, confusion matrix) for all task types (sequence tagging, text classification, relation extraction) with support for per-class metrics, macro/micro averaging, and task-specific evaluation protocols. The evaluation framework handles label encoding, metric computation, and result reporting, providing detailed performance breakdowns for model analysis and debugging.","intents":["Evaluate trained models on test sets using standard NLP metrics","Compare model performance across different architectures and hyperparameters","Analyze per-class performance to identify weak points and guide model improvements"],"best_for":["NLP practitioners evaluating model performance on standard benchmarks","Researchers comparing different model architectures and training strategies","Teams tracking model performance across development iterations"],"limitations":["Evaluation metrics are limited to standard NLP metrics; custom metrics require manual computation","No built-in support for cross-validation; requires manual dataset splitting","Evaluation is single-threaded; large test sets can be slow to evaluate","No built-in support for statistical significance testing or confidence intervals"],"requires":["Python 3.7+","Trained Flair model","Test dataset with gold labels"],"input_types":["trained model","test dataset with gold labels","predictions from model inference"],"output_types":["F1, precision, recall scores","per-class metrics","confusion matrix","macro/micro averaged metrics"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_13","uri":"capability://data.processing.analysis.sentence.segmentation.and.tokenization","name":"sentence-segmentation-and-tokenization","description":"Provides utilities for splitting raw text into sentences and tokenizing sentences into tokens using rule-based and neural approaches. The framework includes built-in sentence splitters for multiple languages and custom tokenization strategies (whitespace, Penn Treebank, SentencePiece), handling edge cases like abbreviations, URLs, and special characters. Integrates with Flair's Sentence and Token data structures for downstream NLP tasks.","intents":["Split raw text documents into sentences for sentence-level NLP tasks","Tokenize sentences into words for token-level analysis and tagging","Handle language-specific tokenization rules (e.g., German compound words, Chinese character segmentation)"],"best_for":["NLP practitioners building text processing pipelines from raw documents","Teams handling multilingual text requiring language-specific tokenization","Researchers experimenting with different tokenization strategies"],"limitations":["Rule-based sentence splitting fails on edge cases (abbreviations, URLs, special formatting)","Tokenization is language-specific; cross-lingual tokenization requires language detection","No built-in support for subword tokenization (BPE, SentencePiece); requires integration with external tokenizers","Sentence splitting on noisy text (social media, OCR output) is unreliable without preprocessing"],"requires":["Python 3.7+","Raw text input","Language specification for language-specific tokenization"],"input_types":["raw text strings","text files","document collections"],"output_types":["Sentence objects with tokenized text","Token objects with character offsets","sentence and token boundaries"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_2","uri":"capability://text.generation.language.text.classification.with.document.embeddings","name":"text-classification-with-document-embeddings","description":"Implements document-level text classification using a two-stage pipeline: (1) compute document embeddings by aggregating token embeddings (mean pooling, attention-based, or learned aggregation), and (2) pass the document embedding through a classification head (linear layer + softmax) to predict document-level labels. Supports both single-label and multi-label classification with configurable loss functions and label smoothing.","intents":["Train sentiment analysis models on text documents without manual feature engineering","Apply pre-trained text classifiers for sentiment, topic, or intent detection","Fine-tune document classifiers on custom datasets using transfer learning from pre-trained embeddings"],"best_for":["Teams building sentiment analysis or topic classification systems","Practitioners needing quick baselines for text classification without deep learning expertise","Researchers experimenting with embedding aggregation strategies for document representation"],"limitations":["Document-level aggregation (mean pooling) loses word-order information; attention-based aggregation adds computational overhead","Classification head is shallow (single linear layer); complex decision boundaries require custom model extensions","No built-in support for hierarchical or multi-level classification without custom label encoding","Inference latency scales with document length due to per-token embedding computation"],"requires":["Python 3.7+","PyTorch 1.9+","Labeled training data with document-level labels","Pre-computed or pre-trained embeddings"],"input_types":["raw text strings","Sentence objects with document-level labels","CSV/JSON files with text and label columns"],"output_types":["predicted class labels","confidence scores per class","evaluation metrics (accuracy, F1, confusion matrix)"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_3","uri":"capability://data.processing.analysis.relation.extraction.with.entity.context","name":"relation-extraction-with-entity-context","description":"Extracts semantic relations between entity pairs using a neural model that encodes entity context and relative positions within sentences. The RelationExtractor processes token embeddings, applies attention mechanisms to focus on entity spans and their surrounding context, and predicts relation types between entity pairs. Supports both supervised training on annotated relation datasets and inference on new text with pre-trained models.","intents":["Extract structured relations (e.g., person-organization, drug-disease) from biomedical or domain-specific text","Train custom relation extraction models on domain-specific datasets with minimal preprocessing","Evaluate relation extraction models using standard metrics (F1, precision, recall) with built-in evaluation"],"best_for":["Biomedical NLP teams extracting drug-disease or protein-interaction relations","Knowledge graph construction pipelines requiring relation extraction from unstructured text","Domain practitioners (legal, finance) needing to extract structured relations from documents"],"limitations":["Requires pre-identified entity spans; does not jointly extract entities and relations","Relation extraction accuracy degrades significantly when entity boundaries are incorrect","No built-in support for relations spanning multiple sentences or document-level relations","Training requires substantial annotated data; few-shot relation extraction not natively supported"],"requires":["Python 3.7+","PyTorch 1.9+","Annotated training data with entity spans and relation labels","Pre-trained NER model or gold entity annotations"],"input_types":["Sentence objects with pre-identified entity spans","CoNLL or custom relation annotation formats","entity pairs with context windows"],"output_types":["predicted relation types between entity pairs","confidence scores per relation","evaluation metrics (F1, precision, recall, per-relation-type scores)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_4","uri":"capability://data.processing.analysis.entity.linking.to.knowledge.bases","name":"entity-linking-to-knowledge-bases","description":"Links named entities in text to entries in external knowledge bases (e.g., Wikipedia, Wikidata, domain-specific KBs) using a neural disambiguation model that scores candidate entities based on entity context and mention similarity. The EntityLinker combines mention embeddings with entity embeddings and applies a learned scoring function to rank candidates, enabling both zero-shot linking (using pre-trained embeddings) and supervised fine-tuning on annotated linking datasets.","intents":["Link person, organization, and location mentions to Wikipedia or Wikidata entries","Resolve entity ambiguity (e.g., 'Apple' as company vs. fruit) using contextual information","Build knowledge graph augmentation pipelines that enrich text with structured entity identifiers"],"best_for":["Knowledge graph construction teams needing to link text mentions to KB entries","Information extraction pipelines requiring entity disambiguation and normalization","Biomedical NLP teams linking gene/protein mentions to NCBI or UniProt identifiers"],"limitations":["Requires pre-computed entity embeddings for all KB entries; large KBs (Wikipedia) require substantial storage","Linking accuracy depends heavily on NER quality; incorrect entity boundaries propagate to linking errors","No built-in support for linking to dynamic or frequently-updated knowledge bases","Inference latency scales with KB size due to candidate scoring; approximate nearest-neighbor search needed for large KBs"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-identified entity mentions (from NER)","Knowledge base with entity embeddings or entity descriptions"],"input_types":["Sentence objects with pre-identified entity spans","entity mention text and context","knowledge base entity identifiers and embeddings"],"output_types":["linked entity KB identifiers","confidence scores per candidate","evaluation metrics (accuracy, MRR, recall@K)"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_5","uri":"capability://planning.reasoning.zero.shot.learning.with.task.descriptions","name":"zero-shot-learning-with-task-descriptions","description":"Enables zero-shot NLP task adaptation using the TARS (Task Aware Representation System) model, which encodes task descriptions and input text into a shared embedding space, allowing the model to predict labels for unseen tasks without task-specific training. The approach concatenates task descriptions with input text, encodes them jointly, and applies a learned scoring function to rank candidate labels, enabling rapid task adaptation with minimal or no labeled examples.","intents":["Adapt NLP models to new classification tasks without collecting labeled training data","Perform few-shot learning by providing task descriptions and a handful of examples","Rapidly prototype NLP systems for emerging tasks or domains without extensive annotation"],"best_for":["Startups and teams with limited labeling budgets needing rapid task adaptation","Researchers exploring zero-shot and few-shot NLP capabilities","Production systems requiring quick adaptation to new classification tasks"],"limitations":["Zero-shot accuracy is significantly lower than supervised baselines; task descriptions must be well-crafted","Performance degrades with task descriptions that are too generic or domain-mismatched","Limited to classification tasks; sequence tagging and structured prediction not supported in zero-shot mode","Requires careful prompt engineering; task description quality directly impacts accuracy"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained TARS model (auto-downloaded)","Task descriptions (natural language descriptions of labels)"],"input_types":["raw text strings","task descriptions (label names or natural language descriptions)","optional few-shot examples (1-5 labeled examples per class)"],"output_types":["predicted class labels","confidence scores per class","evaluation metrics on test sets"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_6","uri":"capability://planning.reasoning.multi.task.learning.with.shared.representations","name":"multi-task-learning-with-shared-representations","description":"Trains neural models on multiple NLP tasks simultaneously using shared embedding and encoder layers, with task-specific output heads that predict labels for different tasks. The multi-task learning framework enables knowledge transfer between related tasks (e.g., NER and POS tagging), improving generalization and reducing overfitting on small datasets. Supports flexible task weighting, task-specific loss functions, and joint optimization across tasks.","intents":["Train models on multiple related NLP tasks to improve generalization and reduce overfitting","Leverage auxiliary tasks (e.g., POS tagging) to improve performance on primary tasks (e.g., NER)","Build multi-task NLP systems that predict multiple annotations (entities, POS tags, chunks) in a single forward pass"],"best_for":["Teams with limited labeled data for individual tasks but abundant multi-task annotations","Researchers studying transfer learning and task relationships in NLP","Production systems requiring multiple NLP predictions (NER + POS + chunking) with shared computation"],"limitations":["Task selection and weighting require careful tuning; poor task combinations can hurt performance","Training is more complex than single-task learning; hyperparameter search space is larger","Negative transfer can occur if tasks are poorly aligned or have conflicting objectives","Inference latency is higher than single-task models due to multiple output heads"],"requires":["Python 3.7+","PyTorch 1.9+","Annotated training data for multiple related tasks","Task definitions and label sets for each task"],"input_types":["Sentence objects with multiple label types (entities, POS tags, chunks, etc.)","multi-task annotated datasets"],"output_types":["predicted labels for all tasks attached to Token/Sentence objects","per-task evaluation metrics","joint evaluation across tasks"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_7","uri":"capability://data.processing.analysis.language.model.pretraining.and.fine.tuning","name":"language-model-pretraining-and-fine-tuning","description":"Provides tools for pretraining and fine-tuning language models (character-level and word-level) using masked language modeling and next-sentence prediction objectives. The framework supports training on large text corpora, saving intermediate checkpoints, and fine-tuning on downstream NLP tasks. Integrates with Flair's embedding system to use pre-trained language models as contextual embeddings for other tasks.","intents":["Pretrain domain-specific language models on specialized corpora (biomedical, legal, financial text)","Fine-tune pre-trained language models on downstream NLP tasks with minimal additional training","Generate contextual embeddings from custom language models for transfer learning"],"best_for":["Teams with large domain-specific text corpora needing specialized language models","Researchers studying language model pretraining and transfer learning","Organizations requiring domain-adapted embeddings for downstream NLP tasks"],"limitations":["Pretraining is computationally expensive; requires GPU clusters for reasonable training time","Character-level models are slower to train and infer than word-level models","No built-in support for distributed training across multiple GPUs/nodes; requires custom setup","Fine-tuning on small datasets can lead to catastrophic forgetting of pre-trained knowledge"],"requires":["Python 3.7+","PyTorch 1.9+","Large text corpus (millions of documents for meaningful pretraining)","GPU with sufficient VRAM (16GB+ recommended)"],"input_types":["raw text files or datasets","pre-tokenized text","downstream task datasets for fine-tuning"],"output_types":["pre-trained language model checkpoints","contextual embeddings from fine-tuned models","downstream task predictions"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_8","uri":"capability://data.processing.analysis.transformer.model.integration.and.fine.tuning","name":"transformer-model-integration-and-fine-tuning","description":"Integrates pre-trained transformer models (BERT, RoBERTa, DistilBERT, etc.) from HuggingFace as embedding sources and enables fine-tuning of transformer layers for downstream NLP tasks. The integration handles tokenization, subword token aggregation, and gradient flow through transformer layers, allowing users to leverage transformer representations without writing custom PyTorch code. Supports both frozen embeddings (feature extraction) and end-to-end fine-tuning.","intents":["Use pre-trained transformers as embeddings for Flair NLP tasks without custom integration code","Fine-tune transformer models on domain-specific NLP tasks with Flair's training framework","Combine transformer embeddings with other embedding sources (Flair contextual, GloVe) for ensemble representations"],"best_for":["Teams wanting to leverage transformer models without deep PyTorch expertise","Practitioners needing to fine-tune transformers on specific NLP tasks","Researchers experimenting with transformer combinations and ensemble embeddings"],"limitations":["Transformer fine-tuning requires significant GPU memory; batch sizes must be small for large models","Inference latency is higher than static embeddings due to full forward pass through transformer","Subword tokenization mismatch requires careful token aggregation; some information is lost in aggregation","Fine-tuning on small datasets can lead to overfitting; requires careful regularization and early stopping"],"requires":["Python 3.7+","PyTorch 1.9+","HuggingFace transformers library (auto-installed)","GPU with 8GB+ VRAM for inference, 16GB+ for fine-tuning"],"input_types":["raw text strings","Sentence objects","HuggingFace model identifiers (e.g., 'bert-base-uncased')"],"output_types":["transformer embeddings (contextual token representations)","fine-tuned model checkpoints","downstream task predictions"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-flair__cap_9","uri":"capability://data.processing.analysis.biomedical.nlp.with.domain.specific.models","name":"biomedical-nlp-with-domain-specific-models","description":"Provides pre-trained models and datasets specifically designed for biomedical NLP tasks, including biomedical NER (genes, proteins, diseases), biomedical relation extraction, and biomedical text classification. The framework includes pre-trained embeddings on biomedical corpora (PubMed, MEDLINE) and pre-trained sequence taggers for common biomedical entity types, enabling rapid deployment of biomedical NLP systems without extensive domain-specific training.","intents":["Extract biomedical entities (genes, proteins, diseases, drugs) from scientific literature","Extract biomedical relations (protein-protein interactions, drug-disease associations) from text","Classify biomedical documents (e.g., clinical trial phases, adverse event reports) without custom training"],"best_for":["Biomedical researchers and clinicians needing NLP tools for literature mining","Pharmaceutical and healthcare companies building knowledge extraction pipelines","Bioinformatics teams integrating NLP into genomics and drug discovery workflows"],"limitations":["Pre-trained biomedical models are optimized for specific entity types; custom entity types require retraining","Biomedical text has high domain specificity; models trained on general biomedical corpora may not transfer to specialized subdomains (e.g., radiology reports)","Biomedical relation extraction is limited to common relation types; rare or novel relations require custom annotation","No built-in support for clinical note processing; requires preprocessing for clinical text normalization"],"requires":["Python 3.7+","PyTorch 1.9+","Pre-trained biomedical models (auto-downloaded)","Biomedical text input (scientific abstracts, full-text articles, clinical notes)"],"input_types":["PubMed abstracts","full-text scientific articles","clinical notes","biomedical text in raw or pre-tokenized format"],"output_types":["biomedical entity annotations (genes, proteins, diseases, drugs)","biomedical relation predictions","document-level classifications"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.9+","Pre-trained embedding models (auto-downloaded on first use)","Annotated training data in CoNLL or Flair format","GPU recommended (CUDA 11.0+ or Apple Silicon)","Annotated dataset in supported format (CoNLL, Flair, CSV, JSON)","Sufficient memory to load entire dataset","Annotated training dataset","Trained Flair model","Test dataset with gold labels"],"failure_modes":["Contextual embeddings are computationally expensive to generate at inference time compared to static embeddings","Embedding dimensionality can be high when combining multiple sources, increasing memory footprint","Pre-trained models are language-specific; cross-lingual embeddings require separate models","SequenceTagger assumes token-level predictions; nested or overlapping entities require post-processing","CRF decoder adds ~50-100ms latency per sentence during inference due to dynamic programming","Training requires GPU for reasonable throughput; CPU training is prohibitively slow for large datasets","No built-in support for multi-token entities without explicit BIO/BIOES tagging scheme","Limited support for custom dataset formats; requires manual conversion to Flair format","No built-in support for streaming large datasets; entire dataset must fit in memory","Data augmentation is limited to basic techniques (token replacement, sentence shuffling); advanced augmentation requires custom code","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-flair","compare_url":"https://unfragile.ai/compare?artifact=pypi-flair"}},"signature":"JkqnrEQ9KZnY9BIK3OTN7Gaq6n+rHRyDWDBv5Wg9s70kQ51XqyG9ovIVcRDzWxClFJKASTT7j9Jdh5Ln6YWTAw==","signedAt":"2026-06-21T05:51:36.149Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-flair","artifact":"https://unfragile.ai/pypi-flair","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-flair","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"}}