Command Line

ASReview provides a powerful command line interface for running tasks like simulations. For a list of available commands, type asreview --help.


asreview lab launches the ASReview LAB software (the webapp).

asreview lab [options]
--ip IP

The IP address the server will listen on.

--port PORT

The port the server will listen on.

--port-retries NUMBER_RETRIES

The number of additional ports to try if the specified port is not available.

--no-browser NO_BROWSER

Do not open ASReview LAB in a browser after startup.


The full path to an SSL/TLS certificate file.


The full path to a private key file for usage with SSL/TLS.

--embedding EMBEDDING_FP

File path of embedding matrix. Required for LSTM models.

--clean-project CLEAN_PROJECT

Safe cleanup of temporary files in project.

--clean-all-projects CLEAN_ALL_PROJECTS

Safe cleanup of temporary files in all projects.

--seed SEED

Seed for the model (classifiers, balance strategies, feature extraction techniques, and query strategies). Use an integer between 0 and 2^32 - 1.

-h, --help

Show help message and exit.


asreview simulate measures the performance of the software on existing systematic reviews. The software shows how many papers you could have potentially skipped during the systematic review. You can use your labeled dataset

asreview simulate [options] [dataset [dataset ...]]

or one of the benchmark-datasets (see index.csv for dataset IDs).

asreview simulate [options] benchmark: [dataset_id]


asreview simulate YOUR_DATA.csv --state_file myreview.asreview
asreview simulate benchmark:van_de_Schoot_2017 --state_file myreview.asreview

A dataset to simulate

-m, --model MODEL

The prediction model for Active Learning. Default: nb. (See available options below: Classifiers)

-q, --query_strategy QUERY_STRATEGY

The query strategy for Active Learning. Default: max. (See available options below: Query strategies)

-b, --balance_strategy BALANCE_STRATEGY

Data rebalancing strategy. Helps against imbalanced datasets with few inclusions and many exclusions. Default: double. (See available options below: Balance strategies)

-e, --feature_extraction FEATURE_EXTRACTION

Feature extraction method. Some combinations of feature extraction method and prediction model are not available. Default: tfidf. (See available options below: Feature extraction)

--embedding EMBEDDING_FP

File path of embedding matrix. Required for LSTM models.

--config_file CONFIG_FILE

Configuration file with model settings and parameter values.

--seed SEED

Seed for the model (classifiers, balance strategies, feature extraction techniques, and query strategies). Use an integer between 0 and 2^32 - 1.

--n_prior_included N_PRIOR_INCLUDED

The number of prior included papers. Only used when prior_idx is not given. Default 1.

--n_prior_excluded N_PRIOR_EXCLUDED

The number of prior excluded papers. Only used when prior_idx is not given. Default 1.

--prior_idx [PRIOR_IDX [PRIOR_IDX ...]]

Prior indices by rownumber (0 is first rownumber).

--prior_record_id [PRIOR_RECORD_ID [PRIOR_RECORD_ID ...]]

Prior indices by record_id.

--state_file STATE_FILE, -s STATE_FILE

Location to ASReview project file of simulation.

--init_seed INIT_SEED

Seed for setting the prior indices if the prior_idx option is not used. If the option prior_idx is used with one or more index, this option is ignored.

--n_instances N_INSTANCES

Number of papers queried each query.Default 1.

--stop_if STOP_IF

The number of label actions to simulate. Default, ‘min’ will stop simulating when all relevant records are found. Use -1 to simulate all labels actions.


The simulation data will be written away after each set of thismany labeled records. By default only writes away data at the endof the simulation to make it as fast as possible.

--verbose VERBOSE, -v VERBOSE


-h, --help

Show help message and exit.


Some classifiers (models) and feature extraction algorithms require additional dependecies. Use pip install asreview[all] to install all additional dependencies at once.

Feature Extraction

Name Reference Requires
tfidf asreview.models.feature_extraction.Tfidf  
doc2vec asreview.models.feature_extraction.Doc2Vec gensim
embedding-idf asreview.models.feature_extraction.EmbeddingIdf  
embedding-lstm asreview.models.feature_extraction.EmbeddingLSTM  
sbert asreview.models.feature_extraction.SBERT sentence_transformers


Name Reference Requires
nb asreview.models.classifiers.NaiveBayesClassifier  
svm asreview.models.classifiers.SVMClassifier  
logistic asreview.models.classifiers.LogisticClassifier  
rf asreview.models.classifiers.RandomForestClassifier  
nn-2-layer asreview.models.classifiers.NN2LayerClassifier tensorflow
lstm-base asreview.models.classifiers.LSTMBaseClassifier tensorflow
lstm-pool asreview.models.classifiers.LSTMPoolClassifier tensorflow

Query Strategies

Name Reference Requires
max asreview.models.query.MaxQuery  
random asreview.models.query.RandomQuery  
uncertainty asreview.models.query.UncertaintyQuery  
cluster asreview.models.query.ClusterQuery  
max_random asreview.models.query.MaxRandomQuery  
max_uncertainty asreview.models.query.MaxUncertaintyQuery  

Balance Strategies

Name Reference Requires
simple asreview.models.balance.SimpleBalance  
double asreview.models.balance.DoubleBalance  
undersample asreview.models.balance.UndersampleBalance  


asreview algorithms provides an overview of all available active learning model elements (classifiers, query strategies, balance strategies, and feature extraction algorithms) in ASReview.

asreview algorithms


asreview algorithms included models added via extensions. See Create extensions for more information on extending ASReview with new models via extensions.