The fine-tune subcommand is used to continue training (or fine-tune) a model on a different dataset than the one it was originally trained on. It requires a saved model archive file, a path to the data you will continue training with, and a directory in which to write the results.

$ allennlp fine-tune --help
 usage: allennlp fine-tune [-h] -m MODEL_ARCHIVE -c CONFIG_FILE -s
                           SERIALIZATION_DIR [-o OVERRIDES] [--extend-vocab]
                           [--batch-weight-key BATCH_WEIGHT_KEY]
                           [--embedding-sources-mapping EMBEDDING_SOURCES_MAPPING]
                           [--include-package INCLUDE_PACKAGE]

 Continues training a saved model on a new dataset.

 optional arguments:
   -h, --help            show this help message and exit
   -m MODEL_ARCHIVE, --model-archive MODEL_ARCHIVE
                         path to the saved model archive from training on the
                         original data
   -c CONFIG_FILE, --config-file CONFIG_FILE
                         configuration file to use for training. Format is the
                         same as for the "train" command, but the "model"
                         section is ignored.
                         directory in which to save the fine-tuned model and
                         its logs
   -o OVERRIDES, --overrides OVERRIDES
                         a JSON structure used to override the training
                         configuration (only affects the config_file, _not_ the
   --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.
                         outputs tqdm status on separate lines and slows tqdm
                         refresh rate
   --batch-weight-key BATCH_WEIGHT_KEY
                         If non-empty, name of metric used to weight the loss
                         on a per-batch basis.
   --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