annotlib: Annotator Library
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Quick Start
Overview
Base and Standard Annotators
Clusters as Knowledge Areas of Annotators
Classifiers as Annotators
Estimate Sample Difficulty to Simulate Annotators
Annotators with Dynamic Labelling Performance
Active Learning with Multiple Types of Annotators
API Reference
Bibliography
annotlib: Annotator Library
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Welcome to
annotlib
– An Annotator Library
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Welcome to
annotlib
– An Annotator Library
¶
Quick Start
Overview
Introduction – Why annotlib?
Pool-based Active Learning Cycle with Multiple Annotators
Modelling Annotators – scikit-learn compatible
Annotator Types and Simulation
Base and Standard Annotators
Defintion of Standard Annotators
Number of Annotators
Class Labels and Certainty Scores
Query Statistics and Queried Samples
Labelling Performance
Confidence Noise
Clusters as Knowledge Areas of Annotators
1. Class Labels as Clustering
2. Clustering Algorithms to Find Clustering
3. Feature Space as a Single Cluster
Classifiers as Annotators
Varying the Size of the Training Set
Varying the Number of the Features Used for Training
Varying the Classifier Type and its Parametrisation
Estimate Sample Difficulty to Simulate Annotators
Estimating the Difficulty of Samples
Simulating and Analyising Annotators
Annotators with Dynamic Labelling Performance
Dynamic Annotators without Adversarial Labelling Performances
Dynamic Annotators with Adversarial Labelling Performances
Active Learning with Multiple Types of Annotators
Experimental Setup
Results
API Reference
module: base
module: standard
module: classifier_based
module: cluster_based
module: difficulty_based
module: dynamic
module: multi_types
module: utils
Bibliography
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