The predict subcommand allows you to make bulk JSON-to-JSON or dataset to JSON predictions using a trained model and its Predictor wrapper.

$ allennlp predict --help
usage: allennlp predict [-h] [--output-file OUTPUT_FILE]
                        [--weights-file WEIGHTS_FILE]
                        [--batch-size BATCH_SIZE] [--silent]
                        [--cuda-device CUDA_DEVICE] [--use-dataset-reader]
                        [--dataset-reader-choice {train,validation}]
                        [-o OVERRIDES] [--predictor PREDICTOR]
                        [--include-package INCLUDE_PACKAGE]
                        archive_file input_file

Run the specified model against a JSON-lines input file.

positional arguments:
  archive_file          the archived model to make predictions with
  input_file            path to or url of the input file

optional arguments:
  -h, --help            show this help message and exit
  --output-file OUTPUT_FILE
                        path to output file
  --weights-file WEIGHTS_FILE
                        a path that overrides which weights file to use
  --batch-size BATCH_SIZE
                        The batch size to use for processing
  --silent              do not print output to stdout
  --cuda-device CUDA_DEVICE
                        id of GPU to use (if any)
  --use-dataset-reader  Whether to use the dataset reader of the original
                        model to load Instances. The validation dataset reader
                        will be used if it exists, otherwise it will fall back
                        to the train dataset reader. This behavior can be
                        overridden with the --dataset-reader-choice flag.
  --dataset-reader-choice {train,validation}
                        Indicates which model dataset reader to use if the
                        --use-dataset-reader flag is set. (default =
  -o OVERRIDES, --overrides OVERRIDES
                        a JSON structure used to override the experiment
  --predictor PREDICTOR
                        optionally specify a specific predictor to use
  --include-package INCLUDE_PACKAGE
                        additional packages to include