The evaluate subcommand can be used to evaluate a trained model against a dataset and report any metrics calculated by the model.

$ allennlp evaluate --help
usage: allennlp evaluate [-h] [--output-file OUTPUT_FILE]
                         [--weights-file WEIGHTS_FILE]
                         [--cuda-device CUDA_DEVICE] [-o OVERRIDES]
                         [--batch-weight-key BATCH_WEIGHT_KEY]
                         [--embedding-sources-mapping EMBEDDING_SOURCES_MAPPING]
                         [--include-package INCLUDE_PACKAGE]
                         archive_file input_file

Evaluate the specified model + dataset

positional arguments:
  archive_file          path to an archived trained model
  input_file            path to the file containing the evaluation data

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
  --cuda-device CUDA_DEVICE
                        id of GPU to use (if any)
  -o OVERRIDES, --overrides OVERRIDES
                        a JSON structure used to override the experiment
  --batch-weight-key BATCH_WEIGHT_KEY
                        If non-empty, name of metric used to weight the loss
                        on a per-batch basis.
  --extend-vocab        if specified, we will use the instances in your new
                        dataset to extend your vocabulary. If pretrained-file
                        was used to initialize embedding layers, you may also
                        need to pass --embedding-sources-mapping.
  --embedding-sources-mapping EMBEDDING_SOURCES_MAPPING
                        a JSON dict defining mapping from embedding module
                        path to embeddingpretrained-file used during training.
                        If not passed, and embedding needs to be extended, we
                        will try to use the original file paths used during
                        training. If they are not available we will use random
                        vectors for embedding extension.
  --include-package INCLUDE_PACKAGE
                        additional packages to include