Gestisci Dati labeling projects and annotations attraverso la conversazione - Neotask uses AI agents to operate your Label Studio annotation infrastructure.
Crea and Gestisci Label Studio projects, import Dati, and configure labeling interfaces tramite linguaggio naturale
Query annotation progress, export labeled datasets, and review task statistics without touching the Label Studio UI
Gestisci annotator assignments, review queues, and prediction imports attraverso la conversazioneal commands
Cosa Puoi Fare
Crea and Configure Labeling Projects
Dì Neotask to Crea a Label Studio project with a specific task type - image classification, NER, bounding boxes, sentiment, or custom - and configure the labeling interface. Provide the label schema in plain English; Neotask translates it into the correct Label Studio XML configuration.
Import Dati for Annotation
Chiedi Neotask to import tasks into a Label Studio project from a URL Elenca, S3 path, or JSON payload. It handles the import API call and returns the number of tasks created.
Track Annotation Progress
Chiedi Neotask for the annotation progress on any project: how many tasks are total, how many are annotated, how many are in review, and how many are skipped.
Export Labeled Dati
Chiedi Neotask to export annotations from a project in your preferred format - JSON, CSV, COCO, YOLO, Pascal VOC - and receive the Dati or a download link.
Gestisci Predictions and Pre-Annotations
Import model predictions as pre-annotations into a Label Studio project to speed up human review. Chiedi Neotask to upload predictions and convert them to annotations or leave them as suggestions.
Assign and Gestisci Annotators
Chiedi Neotask to assign annotators to specific tasks, Imposta up annotation queues, or Controlla which annotator has the most tasks pending.
Prova a Chiedere
"Crea a new Label Studio project for sentiment classification with three labels: Positive, Negative, Neutral"
"Import 500 text tasks from this S3 URL into project 42"
"What is the annotation progress on project customer-feedback-q3?"
"Export all annotations from project 15 in COCO format"
"Upload these model predictions to project 42 as pre-annotations"
"Who has the most incomplete tasks in project invoice-ner?"
"Elenca all projects and their current status"
"Elimina all skipped tasks from project 22 so annotators can re-attempt them"
Suggerimenti Professionali
Use pre-annotations to speed labeling - import your model's predictions as pre-annotations before humans review; annotators confirm or correct rather than labeling from scratch, which cuts annotation time by 50-70% for mature models.
Export early and often - do not wait until a project is 100% done to export; Chiedi Neotask to export partial datasets for initial model training runs while labeling continues in parallel.
Consistent label schemas matter - define your label schema carefully before importing Dati; changing labels mid-project forces re-annotation.
Filter tasks by status - when reviewing quality, Chiedi Neotask to Elenca tasks with disagreements or low annotation confidence first; addressing uncertainty early improves overall dataset quality.
API tokens per annotator - each Label Studio user has their own API token; for audit trails, ensure individual annotators use their own credentials rather than a shared account.
Multiple workspaces and capacity for larger teams.
Works Well With
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