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//- 💫 DOCS > API > TOP-LEVEL > COMMAND LINE INTERFACE
p
| As of v1.7.0, spaCy comes with new command line helpers to download and
| link models and show useful debugging information. For a list of available
| commands, type #[code spacy --help].
+h(3, "download") Download
p
| Download #[+a("/usage/models") models] for spaCy. The downloader finds the
| best-matching compatible version, uses pip to download the model as a
| package and automatically creates a
| #[+a("/usage/models#usage") shortcut link] to load the model by name.
| Direct downloads don't perform any compatibility checks and require the
| model name to be specified with its version (e.g., #[code en_core_web_sm-1.2.0]).
+code(false, "bash", "$").
spacy download [model] [--direct]
+table(["Argument", "Type", "Description"])
+row
+cell #[code model]
+cell positional
+cell Model name or shortcut (#[code en], #[code de], #[code vectors]).
+row
+cell #[code --direct], #[code -d]
+cell flag
+cell Force direct download of exact model version.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+aside("Downloading best practices")
| The #[code download] command is mostly intended as a convenient,
| interactive wrapper – it performs compatibility checks and prints
| detailed messages in case things go wrong. It's #[strong not recommended]
| to use this command as part of an automated process. If you know which
| model your project needs, you should consider a
| #[+a("/usage/models#download-pip") direct download via pip], or
| uploading the model to a local PyPi installation and fetching it straight
| from there. This will also allow you to add it as a versioned package
| dependency to your project.
+h(3, "link") Link
p
| Create a #[+a("/usage/models#usage") shortcut link] for a model,
| either a Python package or a local directory. This will let you load
| models from any location using a custom name via
| #[+api("spacy#load") #[code spacy.load()]].
+infobox("Important note")
| In spaCy v1.x, you had to use the model data directory to set up a shortcut
| link for a local path. As of v2.0, spaCy expects all shortcut links to
| be #[strong loadable model packages]. If you want to load a data directory,
| call #[+api("spacy#load") #[code spacy.load()]] or
| #[+api("language#from_disk") #[code Language.from_disk()]] with the path,
| or use the #[+api("cli#package") #[code package]] command to create a
| model package.
+code(false, "bash", "$").
spacy link [origin] [link_name] [--force]
+table(["Argument", "Type", "Description"])
+row
+cell #[code origin]
+cell positional
+cell Model name if package, or path to local directory.
+row
+cell #[code link_name]
+cell positional
+cell Name of the shortcut link to create.
+row
+cell #[code --force], #[code -f]
+cell flag
+cell Force overwriting of existing link.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+h(3, "info") Info
p
| Print information about your spaCy installation, models and local setup,
| and generate #[+a("https://en.wikipedia.org/wiki/Markdown") Markdown]-formatted
| markup to copy-paste into #[+a(gh("spacy") + "/issues") GitHub issues].
+code(false, "bash").
spacy info [--markdown]
spacy info [model] [--markdown]
+table(["Argument", "Type", "Description"])
+row
+cell #[code model]
+cell positional
+cell A model, i.e. shortcut link, package name or path (optional).
+row
+cell #[code --markdown], #[code -md]
+cell flag
+cell Print information as Markdown.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+h(3, "convert") Convert
p
| Convert files into spaCy's #[+a("/api/annotation#json-input") JSON format]
| for use with the #[code train] command and other experiment management
| functions. The right converter is chosen based on the file extension of
| the input file. Currently only supports #[code .conllu].
+code(false, "bash", "$", false, false, true).
spacy convert [input_file] [output_dir] [--n-sents] [--morphology]
+table(["Argument", "Type", "Description"])
+row
+cell #[code input_file]
+cell positional
+cell Input file.
+row
+cell #[code output_dir]
+cell positional
+cell Output directory for converted JSON file.
+row
+cell #[code --n-sents], #[code -n]
+cell option
+cell Number of sentences per document.
+row
+cell #[code --morphology], #[code -m]
+cell option
+cell Enable appending morphology to tags.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+h(3, "train") Train
p
| Train a model. Expects data in spaCy's
| #[+a("/api/annotation#json-input") JSON format]. On each epoch, a model
| will be saved out to the directory. Accuracy scores and model details
| will be added to a #[+a("/usage/training#models-generating") #[code meta.json]]
| to allow packaging the model using the
| #[+api("cli#package") #[code package]] command.
+code(false, "bash", "$", false, false, true).
spacy train [lang] [output_dir] [train_data] [dev_data] [--n-iter] [--n-sents] [--use-gpu] [--meta-path] [--vectors] [--no-tagger] [--no-parser] [--no-entities] [--gold-preproc]
+table(["Argument", "Type", "Description"])
+row
+cell #[code lang]
+cell positional
+cell Model language.
+row
+cell #[code output_dir]
+cell positional
+cell Directory to store model in.
+row
+cell #[code train_data]
+cell positional
+cell Location of JSON-formatted training data.
+row
+cell #[code dev_data]
+cell positional
+cell Location of JSON-formatted dev data (optional).
+row
+cell #[code --n-iter], #[code -n]
+cell option
+cell Number of iterations (default: #[code 20]).
+row
+cell #[code --n-sents], #[code -ns]
+cell option
+cell Number of sentences (default: #[code 0]).
+row
+cell #[code --use-gpu], #[code -g]
+cell option
+cell Use GPU.
+row
+cell #[code --vectors], #[code -v]
+cell option
+cell Model to load vectors from.
+row
+cell #[code --meta-path], #[code -m]
+cell option
+cell
| #[+tag-new(2)] Optional path to model
| #[+a("/usage/training#models-generating") #[code meta.json]].
| All relevant properties like #[code lang], #[code pipeline] and
| #[code spacy_version] will be overwritten.
+row
+cell #[code --version], #[code -V]
+cell option
+cell
| Model version. Will be written out to the model's
| #[code meta.json] after training.
+row
+cell #[code --no-tagger], #[code -T]
+cell flag
+cell Don't train tagger.
+row
+cell #[code --no-parser], #[code -P]
+cell flag
+cell Don't train parser.
+row
+cell #[code --no-entities], #[code -N]
+cell flag
+cell Don't train NER.
+row
+cell #[code --gold-preproc], #[code -G]
+cell flag
+cell Use gold preprocessing.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+h(4, "train-hyperparams") Environment variables for hyperparameters
+tag-new(2)
p
| spaCy lets you set hyperparameters for training via environment variables.
| This is useful, because it keeps the command simple and allows you to
| #[+a("https://askubuntu.com/questions/17536/how-do-i-create-a-permanent-bash-alias/17537#17537") create an alias]
| for your custom #[code train] command while still being able to easily
| tweak the hyperparameters. For example:
+code(false, "bash").
parser_hidden_depth=2 parser_maxout_pieces=1 train-parser
+table(["Name", "Description", "Default"])
+row
+cell #[code dropout_from]
+cell Initial dropout rate.
+cell #[code 0.2]
+row
+cell #[code dropout_to]
+cell Final dropout rate.
+cell #[code 0.2]
+row
+cell #[code dropout_decay]
+cell Rate of dropout change.
+cell #[code 0.0]
+row
+cell #[code batch_from]
+cell Initial batch size.
+cell #[code 1]
+row
+cell #[code batch_to]
+cell Final batch size.
+cell #[code 64]
+row
+cell #[code batch_compound]
+cell Rate of batch size acceleration.
+cell #[code 1.001]
+row
+cell #[code token_vector_width]
+cell Width of embedding tables and convolutional layers.
+cell #[code 128]
+row
+cell #[code embed_size]
+cell Number of rows in embedding tables.
+cell #[code 7500]
+row
+cell #[code parser_maxout_pieces]
+cell Number of pieces in the parser's and NER's first maxout layer.
+cell #[code 2]
+row
+cell #[code parser_hidden_depth]
+cell Number of hidden layers in the parser and NER.
+cell #[code 1]
+row
+cell #[code hidden_width]
+cell Size of the parser's and NER's hidden layers.
+cell #[code 128]
+row
+cell #[code learn_rate]
+cell Learning rate.
+cell #[code 0.001]
+row
+cell #[code optimizer_B1]
+cell Momentum for the Adam solver.
+cell #[code 0.9]
+row
+cell #[code optimizer_B2]
+cell Adagrad-momentum for the Adam solver.
+cell #[code 0.999]
+row
+cell #[code optimizer_eps]
+cell Epsylon value for the Adam solver.
+cell #[code 1e-08]
+row
+cell #[code L2_penalty]
+cell L2 regularisation penalty.
+cell #[code 1e-06]
+row
+cell #[code grad_norm_clip]
+cell Gradient L2 norm constraint.
+cell #[code 1.0]
+h(3, "evaluate") Evaluate
+tag-new(2)
p
| Evaluate a model's accuracy and speed on JSON-formatted annotated data.
| Will print the results and optionally export
| #[+a("/usage/visualizers") displaCy visualizations] of a sample set of
| parses to #[code .html] files. Visualizations for the dependency parse
| and NER will be exported as separate files if the respective component
| is present in the model's pipeline.
+code(false, "bash", "$", false, false, true).
spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit] [--gpu-id] [--gold-preproc]
+table(["Argument", "Type", "Description"])
+row
+cell #[code model]
+cell positional
+cell
| Model to evaluate. Can be a package or shortcut link name, or a
| path to a model data directory.
+row
+cell #[code data_path]
+cell positional
+cell Location of JSON-formatted evaluation data.
+row
+cell #[code --displacy-path], #[code -dp]
+cell option
+cell
| Directory to output rendered parses as HTML. If not set, no
| visualizations will be generated.
+row
+cell #[code --displacy-limit], #[code -dl]
+cell option
+cell
| Number of parses to generate per file. Defaults to #[code 25].
| Keep in mind that a significantly higher number might cause the
| #[code .html] files to render slowly.
+row
+cell #[code --gpu-id], #[code -g]
+cell option
+cell GPU to use, if any. Defaults to #[code -1] for CPU.
+row
+cell #[code --gold-preproc], #[code -G]
+cell flag
+cell Use gold preprocessing.
+h(3, "package") Package
p
| Generate a #[+a("/usage/training#models-generating") model Python package]
| from an existing model data directory. All data files are copied over.
| If the path to a meta.json is supplied, or a meta.json is found in the
| input directory, this file is used. Otherwise, the data can be entered
| directly from the command line. The required file templates are downloaded
| from #[+src(gh("spacy-dev-resources", "templates/model")) GitHub] to make
| sure you're always using the latest versions. This means you need to be
| connected to the internet to use this command.
+code(false, "bash", "$", false, false, true).
spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
+table(["Argument", "Type", "Description"])
+row
+cell #[code input_dir]
+cell positional
+cell Path to directory containing model data.
+row
+cell #[code output_dir]
+cell positional
+cell Directory to create package folder in.
+row
+cell #[code --meta-path], #[code -m]
+cell option
+cell #[+tag-new(2)] Path to meta.json file (optional).
+row
+cell #[code --create-meta], #[code -c]
+cell flag
+cell
| #[+tag-new(2)] Create a meta.json file on the command line, even
| if one already exists in the directory.
+row
+cell #[code --force], #[code -f]
+cell flag
+cell Force overwriting of existing folder in output directory.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
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