{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"doccano","slug":"doccano","name":"Doccano","type":"repo","url":"https://github.com/doccano/doccano","page_url":"https://unfragile.ai/doccano","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"doccano__cap_0","uri":"capability://data.processing.analysis.multi.task.text.annotation.with.project.scoped.label.schemas","name":"multi-task text annotation with project-scoped label schemas","description":"Enables creation of annotation projects supporting text classification, sequence labeling (NER), and sequence-to-sequence tasks through a unified project management interface. Each project defines its own label taxonomy and annotation type, with the backend Django REST API enforcing schema validation and persisting annotations to SQLite or PostgreSQL. The Vue.js frontend renders task-specific annotation interfaces dynamically based on project configuration, allowing teams to switch between annotation paradigms within the same deployment.","intents":["I need to create a labeled dataset for NER, text classification, and summarization tasks in a single platform without managing separate tools","I want to define custom label sets per project and enforce consistent annotation schema across my team","I need to support multiple annotation types (text classification, sequence labeling, seq2seq) with a unified UI"],"best_for":["ML teams building NLP datasets with mixed task types","researchers prototyping multiple annotation paradigms","organizations standardizing on a single annotation platform"],"limitations":["No hierarchical label support — labels are flat per project, limiting complex taxonomies","Annotation type is immutable after project creation — requires project recreation to switch tasks","No built-in inter-annotator agreement metrics — requires external analysis of exported annotations"],"requires":["Python 3.8+ (backend)","Node.js 14+ (frontend)","PostgreSQL 10+ or SQLite 3.x","Django 3.2+ and Django REST Framework"],"input_types":["plain text documents","JSON/JSONL with text fields","CSV with text columns"],"output_types":["JSON with annotation metadata","JSONL for streaming exports","CSV with label columns","CoNLL format for sequence labeling"],"categories":["data-processing-analysis","annotation-labeling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_1","uri":"capability://automation.workflow.collaborative.team.annotation.with.role.based.access.control","name":"collaborative team annotation with role-based access control","description":"Implements multi-user annotation workflows through Django's authentication system with role-based access control (RBAC) at the project level. Users are assigned roles (admin, annotator, viewer) with granular permissions enforced in the REST API layer before data access. The backend tracks annotation ownership, supports concurrent editing without locking, and maintains audit trails of who annotated what. The Vue.js frontend respects role permissions in the UI, hiding actions unavailable to the current user's role.","intents":["I need multiple team members to annotate the same dataset simultaneously without conflicts","I want to control who can create projects, modify labels, and export data through role-based permissions","I need to track which annotator created each annotation for quality control and inter-annotator agreement analysis"],"best_for":["distributed teams annotating large datasets","organizations with strict data governance requirements","projects requiring audit trails for compliance"],"limitations":["No optimistic locking — concurrent edits to the same annotation can overwrite without warning","RBAC is project-scoped only; no organization-level or resource-level permissions","No built-in conflict resolution for simultaneous annotations of the same example","Role permissions are static — no custom role creation, only predefined admin/annotator/viewer roles"],"requires":["Django authentication backend (default: database, supports LDAP/OAuth via extensions)","PostgreSQL recommended for concurrent write handling (SQLite has locking limitations)","User accounts pre-created by admin or via registration endpoint"],"input_types":["user credentials (username/password or OAuth tokens)","project IDs for role assignment"],"output_types":["JWT or session tokens","role-filtered API responses","audit logs with user/timestamp metadata"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_10","uri":"capability://automation.workflow.docker.containerization.with.environment.based.configuration","name":"docker containerization with environment-based configuration","description":"Provides Docker Compose configuration for single-command deployment of Doccano with all dependencies (Django backend, Vue.js frontend, PostgreSQL, Redis). Environment variables control database connection, secret keys, allowed hosts, and feature flags. The Dockerfile uses multi-stage builds to minimize image size. Supports both development (with hot-reload) and production (with gunicorn) configurations. Pre-built images are published to Docker Hub, eliminating build time.","intents":["I want to deploy Doccano quickly without managing dependencies manually","I need to run Doccano in a containerized environment (Kubernetes, Docker Swarm, cloud platforms)","I want to customize deployment configuration (database, ports, features) via environment variables"],"best_for":["DevOps engineers deploying Doccano to production","teams using Kubernetes or container orchestration","organizations requiring reproducible deployments"],"limitations":["Docker Compose is single-host only; Kubernetes requires additional manifests","No health checks configured in Compose file; requires manual addition for production","Persistent volumes are not configured; data is lost if containers are removed","No automatic backups; requires external backup strategy for PostgreSQL"],"requires":["Docker 20.10+","Docker Compose 1.29+","4GB RAM minimum","10GB disk space for images and data"],"input_types":[".env file with environment variables","docker-compose.yml"],"output_types":["running Doccano service on localhost:8000"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_11","uri":"capability://automation.workflow.project.cloning.and.template.reuse.for.rapid.project.setup","name":"project cloning and template reuse for rapid project setup","description":"Allows administrators to clone existing projects (including label schema, annotation guidelines, and UI configuration) to create new projects without manual reconfiguration. Cloning copies project metadata but not annotations, enabling rapid setup of similar projects. Supports exporting project configuration as a template file and importing it into other Doccano instances. Templates are JSON files containing label definitions, UI settings, and guidelines.","intents":["I want to create a new annotation project with the same labels and settings as an existing one","I need to share project configurations across multiple Doccano instances or teams","I want to standardize annotation setup across my organization"],"best_for":["organizations running multiple similar annotation projects","teams sharing project templates across instances","projects with standardized label schemas"],"limitations":["Cloning does not copy annotations — requires separate export/import if data reuse is needed","Templates are not versioned; no history of configuration changes","No validation of imported templates — invalid JSON can corrupt project configuration","No merge functionality — cannot combine templates from multiple sources"],"requires":["project admin role","Django admin or API access"],"input_types":["source project ID (for cloning)","template JSON file (for import)"],"output_types":["new project with cloned configuration","template JSON file (for export)"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_12","uri":"capability://text.generation.language.multi.language.support.with.unicode.text.handling.and.rtl.language.rendering","name":"multi-language support with unicode text handling and rtl language rendering","description":"Supports annotation in multiple languages including right-to-left (RTL) languages (Arabic, Hebrew, Persian) with proper Unicode text handling and bidirectional text rendering. The frontend uses CSS flexbox with direction properties to render RTL text correctly, while the backend stores all text as UTF-8 without language-specific processing. Language selection is per-project, affecting UI language and text rendering direction.","intents":["I need to annotate documents in non-English languages (Arabic, Chinese, etc.)","I want to support RTL languages without manual text reversal","I need the UI to display in multiple languages for international teams"],"best_for":["international teams working with multilingual datasets","organizations building NLP models for non-English languages","researchers working with low-resource languages"],"limitations":["Language selection is per-project — cannot mix languages in a single project","UI translations are community-contributed — not all languages are fully translated","No language-specific tokenization — sequence labeling uses character-based boundaries for non-Latin scripts","No support for code-switching — documents mixing multiple languages may have rendering issues"],"requires":["UTF-8 encoding for all text","Modern web browser with Unicode support","Language selection during project creation"],"input_types":["UTF-8 encoded text in any language","language code (en, ar, zh, etc.)"],"output_types":["UTF-8 encoded annotations","language-specific export formats"],"categories":["text-generation-language","internationalization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_2","uri":"capability://data.processing.analysis.asynchronous.data.import.with.format.auto.detection.and.validation","name":"asynchronous data import with format auto-detection and validation","description":"Processes bulk data imports through a Celery task queue that handles CSV, JSON, JSONL, and other formats without blocking the web interface. The backend detects file format, validates against project schema (ensuring required text fields exist), and creates Example records in batches. Large imports are chunked to avoid memory exhaustion, with progress tracking via Celery task IDs. Failed rows are logged separately, allowing users to retry or inspect errors without re-importing successful records.","intents":["I need to import thousands of documents without waiting for the upload to complete or blocking other users","I want automatic format detection so I don't have to specify CSV vs JSON manually","I need to validate that imported data matches my project's schema before creating examples"],"best_for":["teams importing large datasets (>10k documents)","workflows where data comes from multiple sources in different formats","projects requiring data validation before annotation starts"],"limitations":["No streaming parsing — entire file is loaded into memory before processing (problematic for >1GB files)","Format detection is heuristic-based; ambiguous files may be misclassified","No deduplication — importing the same file twice creates duplicate examples","Celery requires Redis or RabbitMQ broker; SQLite-only deployments cannot use async imports"],"requires":["Celery 5.0+","Redis 5.0+ or RabbitMQ 3.8+ (message broker)","Python 3.8+","pandas library for CSV/Excel parsing"],"input_types":["CSV files (with text column)","JSON files (array of objects)","JSONL files (newline-delimited JSON)","Excel files (.xlsx)"],"output_types":["Example records in database","import status JSON with success/failure counts","error log with row numbers and validation failures"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_3","uri":"capability://data.processing.analysis.structured.data.export.with.format.conversion.and.filtering","name":"structured data export with format conversion and filtering","description":"Exports annotated datasets in multiple formats (JSON, JSONL, CSV, CoNLL for sequence labeling) through a Django REST endpoint that queries the database, applies user-specified filters (by label, annotator, status), and serializes annotations with metadata. Export jobs can be async for large datasets, returning a download URL. The serialization layer handles format-specific transformations: CoNLL format converts span annotations to BIO tags, CSV flattens nested structures, JSONL preserves full annotation objects.","intents":["I need to export my annotated dataset in the format required by my ML training pipeline (CoNLL, JSON, CSV)","I want to filter exports by label, annotator, or completion status without re-annotating","I need to convert between annotation formats without manual post-processing"],"best_for":["ML engineers preparing datasets for model training","teams exporting subsets of data for quality review","projects requiring format conversion between annotation tools"],"limitations":["No custom field mapping — export schema is fixed per format, cannot add/remove columns","CoNLL export assumes single-token spans; overlapping or multi-token spans may be truncated","CSV export flattens nested annotations, losing hierarchical structure","No incremental export — each export regenerates the entire dataset, inefficient for large projects"],"requires":["Django REST Framework serializers","Python 3.8+","pandas for CSV generation (optional, used for performance)"],"input_types":["project ID","filter parameters (label IDs, annotator IDs, status)","format selection (json, jsonl, csv, conll)"],"output_types":["JSON array of annotations","JSONL (one annotation per line)","CSV with columns for text, label, span position","CoNLL format (one token per line with BIO tags)"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_4","uri":"capability://tool.use.integration.auto.labeling.with.external.service.integration.and.custom.rest.templates","name":"auto-labeling with external service integration and custom rest templates","description":"Integrates with external ML services (OpenAI, Hugging Face, custom REST APIs) to pre-label examples before human annotation. Users configure auto-labeling via a template system that specifies request format, response parsing, and label mapping. The backend sends text to the external service, parses the response, and creates annotations programmatically. Supports both batch pre-labeling (all examples at once) and on-demand labeling (per-example). Failed requests are retried with exponential backoff; results are cached to avoid duplicate API calls.","intents":["I want to pre-label my dataset with a pre-trained model to reduce annotator workload","I need to integrate my custom ML service for auto-labeling without modifying Doccano code","I want to compare auto-labeling results with human annotations to measure model accuracy"],"best_for":["teams with pre-trained models or access to external APIs","projects where auto-labeling can bootstrap annotation (e.g., NER with existing models)","workflows comparing model predictions to human ground truth"],"limitations":["Template system is string-based, not strongly typed — easy to create invalid requests","No built-in confidence scoring — cannot filter low-confidence predictions","Response parsing is regex/JSON-path based, fragile for complex nested responses","No rate limiting — can overwhelm external APIs or incur high costs","Cached results are not invalidated if the external service is updated"],"requires":["External API endpoint (OpenAI, Hugging Face, custom REST service)","API credentials (stored in Django settings or environment variables)","Python 3.8+","requests library for HTTP calls"],"input_types":["auto-labeling configuration (service URL, request template, response parser)","example texts to label","label mapping (API output → project labels)"],"output_types":["annotations created in the database","confidence scores (if returned by service)","error logs for failed requests"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_5","uri":"capability://automation.workflow.example.assignment.and.sampling.strategies.for.annotation.distribution","name":"example assignment and sampling strategies for annotation distribution","description":"Distributes examples to annotators using configurable sampling strategies (sequential, random, stratified by label) to ensure balanced workload and coverage. The backend tracks assignment state (unassigned, assigned, completed) per annotator and prevents double-assignment. Supports batch assignment (assign N examples to annotator) and dynamic assignment (assign next unassigned example on-demand). The Vue.js frontend shows annotators their assigned examples in a queue, with progress tracking.","intents":["I need to distribute examples evenly across my annotation team without manual assignment","I want to ensure all labels are represented in each annotator's workload","I need to track which examples are assigned to whom and prevent duplicate annotation"],"best_for":["teams with multiple annotators needing fair workload distribution","projects requiring stratified sampling to ensure label coverage","workflows where assignment happens dynamically as annotators request work"],"limitations":["No reassignment — once assigned, examples cannot be moved to another annotator without manual intervention","Stratified sampling requires pre-computed label statistics; adding new labels requires recomputation","No priority-based assignment — cannot prioritize uncertain examples for expert annotators","Assignment is one-way; no mechanism to unassign or return examples to the pool"],"requires":["Django ORM for assignment tracking","PostgreSQL recommended for concurrent assignment without race conditions","User accounts for all annotators"],"input_types":["annotator user IDs","number of examples to assign","sampling strategy (sequential, random, stratified)"],"output_types":["assignment records in database","annotator task queues","assignment statistics (examples per annotator)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_6","uri":"capability://tool.use.integration.restful.api.for.programmatic.project.and.annotation.management","name":"restful api for programmatic project and annotation management","description":"Exposes full Doccano functionality through a Django REST Framework API with endpoints for creating projects, uploading data, retrieving annotations, and managing users. All operations that can be done in the UI are available via HTTP endpoints with JSON request/response bodies. The API uses token-based authentication (JWT or session tokens) and enforces the same RBAC as the UI. Supports pagination for large result sets and filtering by query parameters. API documentation is auto-generated via drf-spectacular (OpenAPI/Swagger).","intents":["I need to automate annotation project creation and data import from my data pipeline","I want to retrieve annotations programmatically for downstream ML training without manual export","I need to integrate Doccano with external tools (CI/CD, data versioning, model training) via API"],"best_for":["ML engineers building automated data pipelines","teams integrating Doccano with MLOps platforms","developers building custom frontends or mobile apps on top of Doccano"],"limitations":["No GraphQL support — REST-only, requires multiple requests for complex queries","No batch operations — creating 1000 annotations requires 1000 POST requests","Rate limiting is not enforced — can be abused to overload the server","API versioning is not explicit; breaking changes may occur between releases"],"requires":["Django REST Framework 3.12+","API token (obtained via login endpoint or admin panel)","HTTP client (curl, requests, etc.)"],"input_types":["JSON request bodies","query parameters for filtering/pagination","authentication tokens in Authorization header"],"output_types":["JSON responses with project/annotation data","HTTP status codes (200, 400, 401, 403, 404, 500)","error messages with detail field"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_7","uri":"capability://text.generation.language.multi.language.annotation.interface.with.rtl.and.character.set.support","name":"multi-language annotation interface with rtl and character-set support","description":"Provides a Vue.js frontend that supports annotation in 20+ languages with proper right-to-left (RTL) text rendering for Arabic, Hebrew, and Persian. The UI dynamically switches text direction and font rendering based on detected language. Character-set support includes CJK (Chinese, Japanese, Korean), Devanagari, and other non-Latin scripts. Language is set per-project and enforced in the annotation interface, with translations for UI elements (labels, buttons, help text) provided via i18n framework.","intents":["I need to annotate text in non-English languages with proper character rendering and text direction","I want my annotation team to work in their native language with translated UI","I need to support multilingual datasets with mixed scripts (Latin, Arabic, CJK) in the same project"],"best_for":["teams building multilingual NLP datasets","organizations with non-English-speaking annotators","projects requiring proper RTL rendering for Arabic/Hebrew/Persian text"],"limitations":["Language is project-scoped; cannot mix languages in a single project","UI translations are community-contributed; not all languages have complete translations","RTL support is CSS-based; complex layouts may not render correctly in RTL mode","Font rendering depends on system fonts; some scripts may not display without custom font installation"],"requires":["Vue.js 3.0+","i18n library for translations","CSS with RTL support (flexbox, grid)","Unicode-aware text processing (handled by browser)"],"input_types":["text in any language/script","language code (en, ar, zh, ja, etc.)"],"output_types":["annotations with language metadata","UI rendered in selected language with proper text direction"],"categories":["text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_8","uri":"capability://automation.workflow.annotation.interface.customization.via.project.level.ui.configuration","name":"annotation interface customization via project-level ui configuration","description":"Allows project administrators to customize the annotation interface without code by configuring display options (show/hide fields, reorder labels, set keyboard shortcuts) through the Django admin or API. Configuration is stored per-project and applied dynamically in the Vue.js frontend. Supports task-specific customizations: text classification can show labels as buttons or dropdown, sequence labeling can show span highlighting or tag list, etc. Changes apply immediately to all annotators without redeployment.","intents":["I want to customize the annotation UI for my team without modifying code or redeploying","I need different UI layouts for different annotation tasks (classification vs NER) in the same deployment","I want to set keyboard shortcuts for common labels to speed up annotation"],"best_for":["teams with non-technical project managers","organizations running multiple annotation projects with different UI needs","projects where UI optimization is critical for annotator speed"],"limitations":["Customization is limited to predefined options; cannot add custom UI components","No A/B testing framework — cannot compare UI variants to measure impact on annotation speed","Keyboard shortcuts are global per project; cannot customize per-annotator","Configuration changes are not versioned; no rollback mechanism"],"requires":["Django admin access or API token","Project admin role"],"input_types":["UI configuration JSON (field visibility, label order, shortcuts)"],"output_types":["customized annotation interface in Vue.js"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__cap_9","uri":"capability://data.processing.analysis.annotation.quality.monitoring.with.inter.annotator.agreement.metrics","name":"annotation quality monitoring with inter-annotator agreement metrics","description":"Provides built-in metrics for measuring annotation quality through inter-annotator agreement (IAA) calculations. Supports Cohen's Kappa for binary classification, Fleiss' Kappa for multi-class, and Krippendorff's Alpha for sequence labeling. Metrics are computed on overlapping annotations (examples assigned to multiple annotators) and displayed in the admin dashboard. The backend computes metrics on-demand via a Celery task, caching results for performance. Supports filtering by label, date range, and annotator pair.","intents":["I need to measure annotation quality and identify disagreements between annotators","I want to track IAA metrics over time to ensure consistent annotation quality","I need to identify problematic labels or annotators with low agreement"],"best_for":["teams with multiple annotators requiring quality assurance","projects where annotation quality is critical (medical, legal, safety-critical NLP)","researchers publishing datasets and needing to report IAA metrics"],"limitations":["Requires overlapping annotations — cannot compute IAA if each example is annotated by only one person","Metrics are computed post-hoc; no real-time alerts for low agreement","No visualization of disagreement patterns — requires manual inspection of conflicting annotations","Metrics assume independent annotators; no handling of annotator bias or expertise levels"],"requires":["Python 3.8+","scikit-learn for metric computation","Celery for async computation","overlapping annotations (examples assigned to 2+ annotators)"],"input_types":["annotations from multiple annotators on the same examples"],"output_types":["Cohen's Kappa, Fleiss' Kappa, Krippendorff's Alpha scores","per-label agreement metrics","annotator pair agreement matrix"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"doccano__headline","uri":"capability://data.processing.analysis.open.source.text.annotation.tool.for.machine.learning","name":"open-source text annotation tool for machine learning","description":"Doccano is an open-source platform that enables machine learning practitioners to collaboratively annotate text for various NLP tasks, including text classification and sequence labeling, with multi-language support and dataset export capabilities.","intents":["best open-source text annotation tool","text annotation tool for machine learning","collaborative annotation platform for NLP","open-source tool for labeling datasets","text classification annotation software"],"best_for":["machine learning practitioners","NLP researchers"],"limitations":[],"requires":[],"input_types":["text data"],"output_types":["labeled datasets"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+ (backend)","Node.js 14+ (frontend)","PostgreSQL 10+ or SQLite 3.x","Django 3.2+ and Django REST Framework","Django authentication backend (default: database, supports LDAP/OAuth via extensions)","PostgreSQL recommended for concurrent write handling (SQLite has locking limitations)","User accounts pre-created by admin or via registration endpoint","Docker 20.10+","Docker Compose 1.29+","4GB RAM minimum"],"failure_modes":["No hierarchical label support — labels are flat per project, limiting complex taxonomies","Annotation type is immutable after project creation — requires project recreation to switch tasks","No built-in inter-annotator agreement metrics — requires external analysis of exported annotations","No optimistic locking — concurrent edits to the same annotation can overwrite without warning","RBAC is project-scoped only; no organization-level or resource-level permissions","No built-in conflict resolution for simultaneous annotations of the same example","Role permissions are static — no custom role creation, only predefined admin/annotator/viewer roles","Docker Compose is single-host only; Kubernetes requires additional manifests","No health checks configured in Compose file; requires manual addition for production","Persistent volumes are not configured; data is lost if containers are removed","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=doccano","compare_url":"https://unfragile.ai/compare?artifact=doccano"}},"signature":"x5ZZi2MzOEGu0VUyYQrH1kWIgOiE0DD6gfxbYg3P74KWhKEDcjY2MumG5GLh8MOOV1Mk++jeSMt0aGKAJdjTCQ==","signedAt":"2026-06-21T06:58:46.248Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/doccano","artifact":"https://unfragile.ai/doccano","verify":"https://unfragile.ai/api/v1/verify?slug=doccano","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"}}