{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"flair","slug":"flair","name":"Flair","type":"repo","url":"https://github.com/flairNLP/flair","page_url":"https://unfragile.ai/flair","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"flair__cap_0","uri":"capability://data.processing.analysis.contextual.string.embeddings.with.bidirectional.language.models","name":"contextual string embeddings with bidirectional language models","description":"Generates contextualized word and document embeddings by stacking forward and backward language models (flair embeddings), capturing semantic meaning based on surrounding context rather than static word vectors. This approach combines character-level CNN encoders with LSTM layers to produce embeddings that adapt to polysemy and word sense variation, enabling superior performance on downstream NLP tasks compared to static embeddings.","intents":["I need embeddings that understand word meaning in context for my NER or classification model","I want to combine multiple embedding types (contextual + transformer + static) in a single pipeline","I need to reduce dimensionality of embeddings while preserving semantic information for faster inference"],"best_for":["NLP practitioners building sequence tagging or classification models","researchers experimenting with embedding combinations for domain-specific tasks","teams with GPU access seeking state-of-the-art embedding quality without massive transformer models"],"limitations":["Contextual embeddings require forward pass through language models for every input, adding ~50-200ms latency per sentence","Pre-trained flair embeddings are language-specific (primarily English, German, multilingual variants); custom language support requires retraining","Memory footprint increases significantly when stacking multiple embedding types; GPU memory can become bottleneck with large batch sizes"],"requires":["Python 3.7+","PyTorch 1.6+","GPU recommended for inference speed (CPU inference ~10x slower)","Pre-trained model weights (~100-500MB per embedding type)"],"input_types":["raw text strings","tokenized text (list of tokens)","Sentence objects (Flair's native data structure)"],"output_types":["dense float vectors (embedding dimension typically 4096 for stacked embeddings)","PyTorch tensors compatible with downstream models"],"categories":["data-processing-analysis","embeddings"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_1","uri":"capability://data.processing.analysis.sequence.tagging.with.bilstm.crf.architecture.for.token.level.classification","name":"sequence tagging with bilstm-crf architecture for token-level classification","description":"Implements a SequenceTagger model combining BiLSTM (bidirectional LSTM) layers with Conditional Random Fields (CRF) for structured prediction on token sequences. The architecture processes embedded tokens through bidirectional recurrent layers to capture long-range dependencies, then applies CRF decoding to enforce valid tag sequences and output globally optimal predictions rather than independent token classifications.","intents":["I need to train a named entity recognition model on my custom dataset with state-of-the-art accuracy","I want to perform part-of-speech tagging or chunking with structured output constraints","I need to fine-tune a pre-trained sequence tagger on domain-specific text (biomedical, legal, social media)"],"best_for":["NLP teams building production NER systems for information extraction","researchers experimenting with sequence labeling architectures","practitioners with labeled token-level datasets (IOB/IOBES format)"],"limitations":["CRF decoding adds computational overhead during inference; prediction speed ~100-500 tokens/second on CPU depending on model size","Requires token-level annotations in IOB/IOBES format; no built-in support for partial or weak supervision","Performance degrades significantly on out-of-domain text; domain adaptation requires retraining or fine-tuning","BiLSTM-CRF is less effective than transformer-based taggers (BERT-CRF) on very large datasets (>100K sentences)"],"requires":["Python 3.7+","PyTorch 1.6+","Labeled dataset in Flair's Corpus format (IOB/IOBES tags)","GPU recommended for training (CPU training ~20x slower)"],"input_types":["Sentence objects with Token-level embeddings","raw text (automatically tokenized via Flair's splitter)","pre-tokenized sequences"],"output_types":["Sentence objects with predicted tags attached to each Token","structured predictions with confidence scores per tag","evaluation metrics (precision, recall, F1 per entity type)"],"categories":["data-processing-analysis","sequence-tagging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_10","uri":"capability://data.processing.analysis.model.evaluation.with.task.specific.metrics.and.detailed.error.analysis","name":"model evaluation with task-specific metrics and detailed error analysis","description":"Computes comprehensive evaluation metrics for different NLP tasks including precision, recall, F1-score per class, and task-specific metrics (entity-level F1 for NER, accuracy for classification). The evaluation system provides detailed error analysis including confusion matrices, per-class performance breakdowns, and prediction confidence distributions, enabling practitioners to understand model behavior and identify failure modes.","intents":["I need to evaluate my NER model with entity-level metrics (not token-level)","I want to analyze which entity types or classes my model struggles with","I need to generate evaluation reports with per-class metrics and confusion matrices"],"best_for":["NLP practitioners evaluating model performance on test sets","researchers conducting benchmark evaluations with standard metrics","teams debugging model failures and understanding error patterns"],"limitations":["Entity-level evaluation for NER requires exact span and type match; partial matches are not credited","No support for soft metrics (e.g., token overlap F1); only hard matching","Evaluation metrics are computed in-memory; large test sets can consume significant memory","No built-in support for cross-validation or statistical significance testing"],"requires":["Python 3.7+","model predictions and gold annotations","task-specific label definitions"],"input_types":["predicted Sentence objects with predictions","gold Sentence objects with annotations","task type (NER, classification, etc.)"],"output_types":["precision, recall, F1 per class","macro and micro-averaged metrics","confusion matrices","per-class performance breakdowns"],"categories":["data-processing-analysis","evaluation-metrics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_11","uri":"capability://data.processing.analysis.biomedical.nlp.with.domain.specific.embeddings.and.pre.trained.models","name":"biomedical nlp with domain-specific embeddings and pre-trained models","description":"Provides biomedical-specific embeddings and pre-trained models for NER, relation extraction, and text classification on biomedical literature. The biomedical models are trained on PubMed abstracts and biomedical corpora, with embeddings that capture domain-specific terminology and entity types (proteins, genes, diseases, chemicals). This enables practitioners to apply state-of-the-art biomedical NLP without extensive domain-specific training data.","intents":["I need to extract biomedical entities (proteins, genes, diseases) from PubMed abstracts","I want to identify relations between biomedical entities (protein-protein interactions, drug-disease associations)","I need to classify biomedical documents by topic or relevance"],"best_for":["biomedical researchers and NLP practitioners working with scientific literature","teams building biomedical information extraction systems","practitioners with limited biomedical training data seeking pre-trained models"],"limitations":["Pre-trained biomedical models are optimized for PubMed abstracts; performance on other biomedical text (clinical notes, patents) may degrade","Biomedical entity types are fixed (proteins, genes, diseases, chemicals); custom entity types require retraining","Domain shift between PubMed and other biomedical sources (clinical text, patents) reduces accuracy","No support for multi-lingual biomedical NLP; models are English-only"],"requires":["Python 3.7+","PyTorch 1.6+","pre-trained biomedical models (auto-downloaded from Flair model hub)","biomedical text input (PubMed abstracts, scientific papers, etc.)"],"input_types":["raw biomedical text (PubMed abstracts, scientific papers)","Sentence objects with biomedical text"],"output_types":["biomedical entity predictions (proteins, genes, diseases, chemicals)","relation predictions between biomedical entities","document classification predictions"],"categories":["data-processing-analysis","domain-specific-nlp"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_12","uri":"capability://data.processing.analysis.language.model.training.and.fine.tuning.for.custom.embeddings","name":"language model training and fine-tuning for custom embeddings","description":"Enables training custom contextual embeddings (flair embeddings) from scratch or fine-tuning pre-trained embeddings on domain-specific text. The language model training uses forward and backward LSTM-based language models with character-level CNN encoders, optimized for predicting next/previous tokens. This approach allows practitioners to create domain-specific embeddings without requiring massive transformer models, enabling better performance on specialized domains with limited data.","intents":["I want to train custom contextual embeddings on my domain-specific text (medical, legal, social media)","I need to fine-tune pre-trained embeddings on a new language or domain","I want to create lightweight embeddings for deployment on resource-constrained devices"],"best_for":["practitioners working with specialized domains (medical, legal, social media) with limited labeled data","researchers exploring embedding architectures and language model training","teams seeking lightweight embeddings for edge deployment"],"limitations":["Language model training requires large amounts of unlabeled text (millions of tokens) for good embeddings","Training is computationally expensive; requires GPU and significant time (days to weeks for large corpora)","Character-level CNN encoders have limited context window; cannot capture very long-range dependencies","No built-in support for multi-lingual embeddings; requires separate training per language"],"requires":["Python 3.7+","PyTorch 1.6+","large unlabeled text corpus (millions of tokens recommended)","GPU with 8GB+ VRAM for practical training"],"input_types":["raw text files (one sentence per line)","pre-tokenized text","domain-specific corpora"],"output_types":["trained language model weights","contextual embeddings for downstream tasks","forward and backward language model components"],"categories":["data-processing-analysis","language-model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_13","uri":"capability://data.processing.analysis.sentence.and.token.level.data.structures.with.annotation.management","name":"sentence and token-level data structures with annotation management","description":"Provides core data structures (Sentence, Token, Label, Span) that represent text and annotations in a unified format. Sentence objects contain Token objects with embeddings and predictions, Label objects store classification labels with confidence scores, and Span objects represent entity mentions with types and confidence. These structures enable seamless integration between text processing, embedding, and prediction components throughout Flair's pipeline.","intents":["I need a unified data structure to represent text with multiple types of annotations (entities, labels, relations)","I want to attach embeddings and predictions to tokens and sentences without manual bookkeeping","I need to serialize and deserialize annotated text for data pipelines"],"best_for":["NLP practitioners building custom pipelines with Flair components","researchers implementing new NLP tasks using Flair's data structures","teams integrating Flair with other NLP tools and frameworks"],"limitations":["Data structures are optimized for in-memory processing; no built-in persistence or serialization to databases","Embedding storage in Token objects can consume significant memory for large corpora; no lazy loading","Limited support for complex annotation types (overlapping entities, nested structures); assumes flat annotation hierarchy","No built-in versioning or change tracking for annotations"],"requires":["Python 3.7+","Flair library"],"input_types":["raw text strings","tokenized text","annotations (entities, labels, relations)"],"output_types":["Sentence objects with Token, Label, and Span objects","serialized annotations (JSON, CoNLL format)","embeddings and predictions attached to data structures"],"categories":["data-processing-analysis","data-structures"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_2","uri":"capability://data.processing.analysis.text.classification.with.document.level.embeddings.and.feed.forward.networks","name":"text classification with document-level embeddings and feed-forward networks","description":"Performs document-level text classification by aggregating token embeddings into a single document representation (via pooling or attention mechanisms), then passing through feed-forward neural networks with optional multi-layer architecture. The TextClassifier model supports both single-label and multi-label classification, with configurable loss functions (cross-entropy for single-label, binary cross-entropy for multi-label) and automatic handling of class imbalance through weighted sampling.","intents":["I need to classify documents into predefined categories (sentiment, topic, intent)","I want to train a multi-label classifier where documents can belong to multiple categories simultaneously","I need to fine-tune a pre-trained text classifier on domain-specific data with minimal code"],"best_for":["teams building sentiment analysis or topic classification systems","practitioners with labeled document datasets (single or multi-label)","researchers prototyping text classification architectures quickly"],"limitations":["Document-level pooling (mean/max) loses fine-grained token information; attention-based pooling adds computational cost","No built-in support for hierarchical classification or label dependencies; requires custom loss functions for structured labels","Performance on very long documents (>512 tokens) degrades due to information loss in pooling; requires document splitting strategies","Class imbalance handling via weighted sampling is basic; no support for advanced techniques (focal loss, OHEM)"],"requires":["Python 3.7+","PyTorch 1.6+","Labeled dataset with document-level labels (single or multi-label format)","GPU optional but recommended for training"],"input_types":["Sentence objects (documents treated as single sentences or concatenated sentences)","raw text strings","pre-embedded document vectors"],"output_types":["predicted class labels with confidence scores","probability distributions over classes","evaluation metrics (accuracy, F1, precision, recall per class)"],"categories":["data-processing-analysis","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_3","uri":"capability://data.processing.analysis.relation.extraction.with.pairwise.classification.and.entity.aware.embeddings","name":"relation extraction with pairwise classification and entity-aware embeddings","description":"Extracts relations between entities by treating relation extraction as a pairwise classification problem: for each pair of entities in a sentence, the model predicts whether a relation exists and its type. The RelationExtractor uses entity-aware embeddings that concatenate token embeddings with entity type information, enabling the model to distinguish between different entity types and their interactions while maintaining awareness of entity boundaries through special markers.","intents":["I need to extract structured relations (e.g., person-organization affiliations) from text","I want to identify relation types between pre-identified entities in documents","I need to build a knowledge graph extraction pipeline from unstructured text"],"best_for":["information extraction teams building knowledge graph systems","practitioners with entity-annotated datasets and relation labels","researchers working on biomedical or domain-specific relation extraction"],"limitations":["Requires pre-identified entities; cannot extract relations from raw text without upstream NER","Pairwise classification scales quadratically with entity count; sentences with >50 entities become computationally expensive","No built-in support for overlapping relations or complex relation structures; assumes one relation per entity pair","Performance depends heavily on entity recognition quality; cascading errors from NER significantly impact relation extraction accuracy"],"requires":["Python 3.7+","PyTorch 1.6+","Pre-trained NER model or gold entity annotations","Labeled dataset with entity pairs and relation types (IOB format for entities + relation labels)"],"input_types":["Sentence objects with entity annotations (from NER or manual annotation)","entity pairs with context","raw text with pre-identified entity spans"],"output_types":["predicted relations with confidence scores","relation type labels per entity pair","structured output (triples: entity1-relation-entity2)"],"categories":["data-processing-analysis","relation-extraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_4","uri":"capability://data.processing.analysis.entity.linking.with.candidate.generation.and.disambiguation","name":"entity linking with candidate generation and disambiguation","description":"Links named entities in text to entries in a knowledge base (e.g., Wikipedia) through a two-stage pipeline: candidate generation identifies potential knowledge base entries for each entity mention, then disambiguation ranks candidates using entity context embeddings and knowledge base information. The EntityLinker uses mention embeddings combined with entity type constraints to select the most likely knowledge base entry, supporting both exact matching and fuzzy matching strategies.","intents":["I need to link entity mentions in text to Wikipedia or custom knowledge bases","I want to disambiguate entity mentions that refer to multiple possible entities (e.g., 'Washington' → city vs. person)","I need to enrich extracted entities with knowledge base information (descriptions, types, relations)"],"best_for":["information extraction teams building knowledge graph systems with entity disambiguation","practitioners building question-answering systems requiring entity grounding","researchers working on entity linking benchmarks and evaluation"],"limitations":["Requires pre-built knowledge base with embeddings; no built-in support for dynamic knowledge base updates","Candidate generation via string matching is brittle for misspellings or non-standard entity names; requires fuzzy matching configuration","Disambiguation accuracy depends on entity context quality; short contexts or ambiguous mentions reduce performance","Knowledge base coverage is critical; out-of-vocabulary entities cannot be linked and require fallback strategies"],"requires":["Python 3.7+","PyTorch 1.6+","Pre-trained entity linker model or custom knowledge base with embeddings","Entity annotations (from NER or manual annotation) as input"],"input_types":["Sentence objects with entity annotations","entity mentions with context","knowledge base entries (name, description, embeddings)"],"output_types":["linked entity IDs (knowledge base identifiers)","confidence scores for each link","entity metadata from knowledge base (descriptions, types)"],"categories":["data-processing-analysis","entity-linking"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_5","uri":"capability://data.processing.analysis.zero.shot.learning.with.task.specific.prompts.and.label.semantics","name":"zero-shot learning with task-specific prompts and label semantics","description":"Enables zero-shot classification and tagging by leveraging label semantics and task descriptions without requiring labeled training data. The TARS (Task Aware Representation System) model uses a prompt-based approach where task descriptions and label definitions are encoded as embeddings, then compared against input text embeddings to predict labels. This approach allows the same model to handle different classification tasks by changing the prompt and label definitions without retraining.","intents":["I need to classify text into custom categories without labeled training data","I want to adapt a model to new classification tasks by providing label descriptions","I need to perform few-shot learning by providing a few examples as task descriptions"],"best_for":["practitioners with limited labeled data or rapidly changing classification schemas","teams building flexible classification systems that adapt to new categories without retraining","researchers exploring prompt-based and semantic classification approaches"],"limitations":["Zero-shot accuracy is significantly lower than supervised models; typically 10-30% lower F1 on standard benchmarks","Performance heavily depends on label description quality; vague or misleading descriptions degrade accuracy substantially","No support for complex label hierarchies or dependencies; assumes flat label spaces","Requires pre-trained embeddings with good semantic properties; performance varies across embedding types and languages"],"requires":["Python 3.7+","PyTorch 1.6+","Pre-trained TARS model or embeddings with semantic properties","task descriptions and label definitions (no labeled training data required)"],"input_types":["raw text strings","Sentence objects","task descriptions and label definitions"],"output_types":["predicted labels with confidence scores","probability distributions over labels","ranking of labels by similarity to input"],"categories":["data-processing-analysis","zero-shot-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_6","uri":"capability://data.processing.analysis.multi.task.learning.with.shared.representations.and.task.specific.heads","name":"multi-task learning with shared representations and task-specific heads","description":"Trains multiple NLP tasks simultaneously using a shared embedding and encoder layer with task-specific output heads, enabling knowledge transfer between related tasks. The multi-task architecture uses a single BiLSTM or transformer encoder that processes embeddings, then branches into separate task-specific layers (CRF for tagging, softmax for classification) that predict task-specific outputs. This approach improves generalization by leveraging task relationships and reducing overfitting on small datasets.","intents":["I want to train NER and POS tagging jointly to improve both tasks through shared representations","I need to build a multi-task model that handles both sequence tagging and text classification","I want to leverage task relationships to improve performance on low-resource tasks"],"best_for":["NLP teams with multiple related tasks and limited labeled data per task","researchers studying task relationships and transfer learning in NLP","practitioners building unified models for multiple NLP tasks"],"limitations":["Requires careful task weighting to prevent one task from dominating training; no automatic weighting strategy","Negative transfer can occur if tasks are too dissimilar; requires domain knowledge to select compatible tasks","Training complexity increases with number of tasks; convergence can be slower and less stable","Inference speed is only marginally faster than training separate models due to shared encoder overhead"],"requires":["Python 3.7+","PyTorch 1.6+","Labeled datasets for multiple related NLP tasks","GPU recommended for training multiple tasks simultaneously"],"input_types":["Sentence objects with multiple types of annotations (tags, labels)","raw text with multi-task labels"],"output_types":["predictions for all tasks (tags, labels, etc.)","task-specific confidence scores","evaluation metrics per task"],"categories":["data-processing-analysis","multi-task-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_7","uri":"capability://data.processing.analysis.transformer.model.integration.with.pre.trained.weights.and.fine.tuning","name":"transformer model integration with pre-trained weights and fine-tuning","description":"Integrates pre-trained transformer models (BERT, RoBERTa, DistilBERT, etc.) from Hugging Face as embedding providers or task-specific models, enabling fine-tuning on downstream NLP tasks. Flair wraps transformer models through a unified TransformerWordEmbeddings interface that handles tokenization, subword token aggregation, and embedding extraction, allowing transformers to be used interchangeably with Flair's native embeddings in any downstream task architecture.","intents":["I want to use BERT embeddings in my Flair NER or classification pipeline","I need to fine-tune a pre-trained transformer on my custom NLP task","I want to experiment with different transformer models (BERT, RoBERTa, ELECTRA) without changing my task code"],"best_for":["teams leveraging pre-trained transformer models for downstream tasks","practitioners seeking state-of-the-art accuracy on standard NLP benchmarks","researchers comparing different transformer architectures on the same task"],"limitations":["Transformer inference is slow on CPU; GPU with sufficient VRAM (8GB+) required for practical use","Fine-tuning transformers requires careful hyperparameter tuning (learning rate, warmup steps); default settings often suboptimal","Subword tokenization mismatch with Flair's token-level annotations requires careful alignment; some tokens may not align perfectly","Memory footprint is large; batch sizes must be reduced compared to non-transformer models, increasing training time"],"requires":["Python 3.7+","PyTorch 1.6+","Transformers library (Hugging Face) 3.0+","GPU with 8GB+ VRAM for practical fine-tuning","Pre-trained transformer model weights (auto-downloaded from Hugging Face Hub)"],"input_types":["raw text strings","Sentence objects","tokenized text"],"output_types":["transformer embeddings (contextual vectors)","task-specific predictions (tags, labels, etc.)","fine-tuned model weights"],"categories":["data-processing-analysis","transformer-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_8","uri":"capability://data.processing.analysis.corpus.management.and.dataset.handling.with.automatic.train.test.splitting","name":"corpus management and dataset handling with automatic train-test splitting","description":"Provides a unified Corpus abstraction for managing labeled NLP datasets, handling data loading, preprocessing, and train-validation-test splitting. The Corpus class automatically manages multiple Sentence objects with their annotations, supports various input formats (CoNLL, TSV, JSON), and provides utilities for dataset statistics, class distribution analysis, and stratified splitting to ensure balanced class representation across splits.","intents":["I need to load and manage labeled NLP datasets in various formats (CoNLL, TSV, JSON)","I want to split my dataset into train-validation-test sets with proper stratification","I need to analyze dataset statistics and class distributions before training"],"best_for":["NLP practitioners managing labeled datasets for model training","researchers conducting benchmark evaluations with proper data splits","teams building data pipelines for NLP projects"],"limitations":["Limited format support; requires custom loaders for non-standard formats","No built-in data augmentation or balancing strategies; class imbalance must be handled separately","Corpus loading into memory can be slow for very large datasets (>1M sentences); no streaming support","No built-in data versioning or provenance tracking; difficult to reproduce exact dataset versions"],"requires":["Python 3.7+","labeled dataset in supported format (CoNLL, TSV, JSON, or custom loader)","sufficient RAM to load entire corpus into memory"],"input_types":["CoNLL format files (IOB/IOBES tags)","TSV/CSV files with text and labels","JSON files with structured annotations","raw Sentence objects"],"output_types":["Corpus objects with train/validation/test splits","dataset statistics (sentence count, token count, label distribution)","balanced train-test splits"],"categories":["data-processing-analysis","dataset-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__cap_9","uri":"capability://automation.workflow.model.training.with.configurable.loss.functions.and.optimization.strategies","name":"model training with configurable loss functions and optimization strategies","description":"Provides a unified training loop that handles model optimization, loss computation, and evaluation across different NLP tasks. The ModelTrainer class manages training dynamics including learning rate scheduling, gradient clipping, early stopping, and checkpoint management. It supports task-specific loss functions (cross-entropy for classification, CRF loss for tagging, weighted loss for imbalanced data) and multiple optimization strategies (Adam, SGD with momentum, AdamW).","intents":["I want to train a Flair NLP model with automatic hyperparameter management and early stopping","I need to handle class imbalance during training through weighted loss functions","I want to monitor training progress and save the best model checkpoint"],"best_for":["NLP practitioners training custom models on labeled datasets","researchers experimenting with different optimization strategies","teams building production NLP systems with proper model selection"],"limitations":["Limited hyperparameter search capabilities; requires manual tuning or external hyperparameter optimization libraries","Early stopping based on single metric (e.g., validation F1); no multi-objective optimization support","No built-in learning rate warmup or advanced scheduling strategies (cosine annealing, polynomial decay)","Training monitoring is basic; no integration with experiment tracking tools (Weights & Biases, MLflow)"],"requires":["Python 3.7+","PyTorch 1.6+","labeled Corpus object","GPU recommended for practical training speed"],"input_types":["Flair model objects (SequenceTagger, TextClassifier, etc.)","Corpus with train/validation splits","training hyperparameters (learning rate, batch size, epochs)"],"output_types":["trained model weights","training curves (loss, metrics over epochs)","best model checkpoint","evaluation metrics on validation set"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"flair__headline","uri":"capability://data.processing.analysis.powerful.nlp.framework.for.text.classification.and.entity.recognition","name":"powerful nlp framework for text classification and entity recognition","description":"Flair is a powerful and user-friendly NLP framework built on PyTorch, designed for tasks like named entity recognition, sentiment analysis, and text classification with state-of-the-art accuracy.","intents":["best NLP framework","NLP framework for text classification","NLP library for named entity recognition","top tools for sentiment analysis","PyTorch-based NLP solutions"],"best_for":["researchers","practitioners","developers"],"limitations":[],"requires":["Python","PyTorch"],"input_types":["text"],"output_types":["predictions","embeddings"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"low","permissions":["Python 3.7+","PyTorch 1.6+","GPU recommended for inference speed (CPU inference ~10x slower)","Pre-trained model weights (~100-500MB per embedding type)","Labeled dataset in Flair's Corpus format (IOB/IOBES tags)","GPU recommended for training (CPU training ~20x slower)","model predictions and gold annotations","task-specific label definitions","pre-trained biomedical models (auto-downloaded from Flair model hub)","biomedical text input (PubMed abstracts, scientific papers, etc.)"],"failure_modes":["Contextual embeddings require forward pass through language models for every input, adding ~50-200ms latency per sentence","Pre-trained flair embeddings are language-specific (primarily English, German, multilingual variants); custom language support requires retraining","Memory footprint increases significantly when stacking multiple embedding types; GPU memory can become bottleneck with large batch sizes","CRF decoding adds computational overhead during inference; prediction speed ~100-500 tokens/second on CPU depending on model size","Requires token-level annotations in IOB/IOBES format; no built-in support for partial or weak supervision","Performance degrades significantly on out-of-domain text; domain adaptation requires retraining or fine-tuning","BiLSTM-CRF is less effective than transformer-based taggers (BERT-CRF) on very large datasets (>100K sentences)","Entity-level evaluation for NER requires exact span and type match; partial matches are not credited","No support for soft metrics (e.g., token overlap F1); only hard matching","Evaluation metrics are computed in-memory; large test sets can consume significant memory","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"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":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"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=flair","compare_url":"https://unfragile.ai/compare?artifact=flair"}},"signature":"HgIYNwVyBPSo1qZOwReljYaAaomxFRJSti/bcHDDrURq36FHzWfLbvYIKwuQSuh+4pmDDYQ43IBuTd8gsUF4Dg==","signedAt":"2026-06-22T11:54:36.479Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/flair","artifact":"https://unfragile.ai/flair","verify":"https://unfragile.ai/api/v1/verify?slug=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"}}