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//- 💫 DOCS > API > COMMAND LINE INTERFACE
include ../_includes/_mixins
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-2.0.0]).
+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.
+code(false, "bash", "$").
python -m spacy download [model] [--direct]
+table(["Argument", "Type", "Description"])
+row
+cell #[code model]
+cell positional
+cell
| Model name or shortcut (#[code en], #[code de],
| #[code en_core_web_sm]).
+row
+cell #[code --direct], #[code -d]
+cell flag
+cell Force direct download of exact model version.
+row
+cell other
+tag-new(2.1)
+cell -
+cell
| Additional installation options to be passed to
| #[code pip install] when installing the model package. For
| example, #[code --user] to install to the user home directory.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell directory, symlink
+cell
| The installed model package in your #[code site-packages]
| directory and a shortcut link as a symlink in #[code spacy/data].
+h(3, "link") Link
p
| Create a #[+a("/usage/models#usage") shortcut link] for a model,
| either a Python package or a local directory. This will let you load
| models from any location using a custom name via
| #[+api("spacy#load") #[code spacy.load()]].
+infobox("Important note")
| In spaCy v1.x, you had to use the model data directory to set up a shortcut
| link for a local path. As of v2.0, spaCy expects all shortcut links to
| be #[strong loadable model packages]. If you want to load a data directory,
| call #[+api("spacy#load") #[code spacy.load()]] or
| #[+api("language#from_disk") #[code Language.from_disk()]] with the path,
| or use the #[+api("cli#package") #[code package]] command to create a
| model package.
+code(false, "bash", "$").
python -m 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.
+row("foot")
+cell creates
+cell symlink
+cell
| A shortcut link of the given name as a symlink in
| #[code spacy/data].
+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").
python -m spacy info [--markdown]
python -m 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 --silent], #[code -s]
+tag-new("2.0.12")
+cell flag
+cell Don't print anything, just return the values.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell prints
+cell #[code stdout]
+cell Information about your spaCy installation.
+h(3, "validate") Validate
+tag-new(2)
p
| Find all models installed in the current environment (both packages and
| shortcut links) and check whether they are compatible with the currently
| installed version of spaCy. Should be run after upgrading spaCy via
| #[code pip install -U spacy] to ensure that all installed models are
| can be used with the new version. The command is also useful to detect
| out-of-sync model links resulting from links created in different virtual
| environments. It will a list of models, the installed versions, the
| latest compatible version (if out of date) and the commands for updating.
+aside("Automated validation")
| You can also use the #[code validate] command as part of your build
| process or test suite, to ensure all models are up to date before
| proceeding. If incompatible models or shortcut links are found, it will
| return #[code 1].
+code(false, "bash", "$").
python -m spacy validate
+table(["Argument", "Type", "Description"])
+row("foot")
+cell prints
+cell #[code stdout]
+cell Details about the compatibility of your installed models.
+h(3, "convert") Convert
p
| Convert files into spaCy's #[+a("/api/annotation#json-input") JSON format]
| for use with the #[code train] command and other experiment management
| functions. The converter can be specified on the command line, or
| chosen based on the file extension of the input file.
+code(false, "bash", "$", false, false, true).
python -m spacy convert [input_file] [output_dir] [--converter] [--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 converter], #[code -c]
+cell option
+cell #[+tag-new(2)] Name of converter to use (see below).
+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.
+row("foot")
+cell creates
+cell JSON
+cell Data in spaCy's #[+a("/api/annotation#json-input") JSON format].
p The following file format converters are available:
+table(["ID", "Description"])
+row
+cell #[code auto]
+cell Automatically pick converter based on file extension (default).
+row
+cell #[code conllu], #[code conll]
+cell Universal Dependencies #[code .conllu] or #[code .conll] format.
+row
+cell #[code ner]
+cell Tab-based named entity recognition format.
+row
+cell #[code iob]
+cell IOB or IOB2 named entity recognition format.
+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.
+infobox("Changed in v2.1", "⚠️")
| As of spaCy 2.1, the #[code --no-tagger], #[code --no-parser] and
| #[code --no-parser] flags have been replaced by a #[code --pipeline]
| option, which lets you define comma-separated names of pipeline
| components to train. For example, #[code --pipeline tagger,parser] will
| only train the tagger and parser.
+code(false, "bash", "$", false, false, true).
python -m spacy train [lang] [output_path] [train_path] [dev_path]
[--base-model] [--pipeline] [--vectors] [--n-iter] [--n-examples] [--use-gpu]
[--version] [--meta-path] [--init-tok2vec] [--parser-multitasks]
[--entity-multitasks] [--gold-preproc] [--noise-level] [--learn-tokens]
[--verbose]
+table(["Argument", "Type", "Description"])
+row
+cell #[code lang]
+cell positional
+cell Model language.
+row
+cell #[code output_path]
+cell positional
+cell Directory to store model in. Will be created if it doesn't exist.
+row
+cell #[code train_path]
+cell positional
+cell Location of JSON-formatted training data.
+row
+cell #[code dev_path]
+cell positional
+cell Location of JSON-formatted development data for evaluation.
+row
+cell #[code --base-model], #[code -b]
+cell option
+cell
| Optional name of base model to update. Can be any loadable
| spaCy model.
+row
+cell #[code --pipeline], #[code -p]
+tag-new("2.1.0")
+cell option
+cell
| Comma-separated names of pipeline components to train. Defaults
| to #[code 'tagger,parser,ner'].
+row
+cell #[code --vectors], #[code -v]
+cell option
+cell Model to load vectors from.
+row
+cell #[code --n-iter], #[code -n]
+cell option
+cell Number of iterations (default: #[code 30]).
+row
+cell #[code --n-examples], #[code -ns]
+cell option
+cell Number of examples to use (defaults to #[code 0] for all examples).
+row
+cell #[code --use-gpu], #[code -g]
+cell option
+cell
| Whether to use GPU. Can be either #[code 0], #[code 1] or
| #[code -1].
+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 --meta-path], #[code -m]
+tag-new(2)
+cell option
+cell
| 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 --init-tok2vec], #[code -t2v]
+tag-new("2.1.0")
+cell option
+cell
| Path to pretrained weights for the token-to-vector parts of the
| models. See #[code spacy pretrain]. Experimental.
+row
+cell #[code --parser-multitasks], #[code -pt]
+cell option
+cell
| Side objectives for parser CNN, e.g. #[code 'dep'] or
| #[code 'dep,tag']
+row
+cell #[code --entity-multitasks], #[code -et]
+cell option
+cell
| Side objectives for NER CNN, e.g. #[code 'dep'] or
| #[code 'dep,tag']
+row
+cell #[code --noise-level], #[code -nl]
+cell option
+cell Float indicating the amount of corruption for data agumentation.
+row
+cell #[code --gold-preproc], #[code -G]
+cell flag
+cell Use gold preprocessing.
+row
+cell #[code --learn-tokens], #[code -T]
+cell flag
+cell
| Make parser learn gold-standard tokenization by merging
] subtokens. Typically used for languages like Chinese.
+row
+cell #[code --verbose], #[code -VV]
+tag-new("2.0.13")
+cell flag
+cell Show more detailed messages during training.
+row
+cell #[code --help], #[code -h]
+cell flag
+cell Show help message and available arguments.
+row("foot")
+cell creates
+cell model, pickle
+cell A spaCy model on each epoch.
+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 spacy train [...]
+code("Usage with alias", "bash", "$").
alias train-parser="spacy train en /output /data /train /dev -n 1000"
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 history_feats]
//- +cell Number of previous action ID features for parser and NER.
//- +cell #[code 128]
//- +row
//- +cell #[code history_width]
//- +cell Number of embedding dimensions for each action ID.
//- +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, "vocab") Vocab
+tag-new(2)
p
| Compile a vocabulary from a
| #[+a("/api/annotation#vocab-jsonl") lexicon JSONL] file and optional
| word vectors. Will save out a valid spaCy model that you can load via
| #[+api("spacy#load") #[code spacy.load]] or package using the
| #[+api("cli#package") #[code package]] command.
+code(false, "bash", "$").
python -m spacy vocab [lang] [output_dir] [lexemes_loc] [vectors_loc]
+table(["Argument", "Type", "Description"])
+row
+cell #[code lang]
+cell positional
+cell
| Model language
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code],
| e.g. #[code en].
+row
+cell #[code output_dir]
+cell positional
+cell Model output directory. Will be created if it doesn't exist.
+row
+cell #[code lexemes_loc]
+cell positional
+cell
| Location of lexical data in spaCy's
| #[+a("/api/annotation#vocab-jsonl") JSONL format].
+row
+cell #[code vectors_loc]
+cell positional
+cell Optional location of vectors data as numpy #[code .npz] file.
+row("foot")
+cell creates
+cell model
+cell A spaCy model containing the vocab and vectors.
+h(3, "init-model") Init Model
+tag-new(2)
p
| Create a new model directory from raw data, like word frequencies, Brown
| clusters and word vectors. This command is similar to the
| #[code spacy model] command in v1.x.
+code(false, "bash", "$", false, false, true).
python -m spacy init-model [lang] [output_dir] [freqs_loc] [--clusters-loc] [--vectors-loc] [--prune-vectors]
+table(["Argument", "Type", "Description"])
+row
+cell #[code lang]
+cell positional
+cell
| Model language
| #[+a("https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes") ISO code],
| e.g. #[code en].
+row
+cell #[code output_dir]
+cell positional
+cell Model output directory. Will be created if it doesn't exist.
+row
+cell #[code freqs_loc]
+cell positional
+cell
| Location of word frequencies file. Should be a tab-separated
| file with three columns: frequency, document frequency and
| frequency count.
+row
+cell #[code --clusters-loc], #[code -c]
+cell option
+cell
| Optional location of clusters file. Should be a tab-separated
| file with three columns: cluster, word and frequency.
+row
+cell #[code --vectors-loc], #[code -v]
+cell option
+cell
| Optional location of vectors file. Should be a tab-separated
| file in Word2Vec format where the first column contains the word
| and the remaining columns the values. File can be provided in
| #[code .txt] format or as a zipped text file in #[code .zip] or
| #[code .tar.gz] format.
+row
+cell #[code --prune-vectors], #[code -V]
+cell flag
+cell
| Number of vectors to prune the vocabulary to. Defaults to
| #[code -1] for no pruning.
+row("foot")
+cell creates
+cell model
+cell A spaCy model containing the vocab and vectors.
+h(3, "evaluate") Evaluate
+tag-new(2)
p
| Evaluate a model's accuracy and speed on JSON-formatted annotated data.
| Will print the results and optionally export
| #[+a("/usage/visualizers") displaCy visualizations] of a sample set of
| parses to #[code .html] files. Visualizations for the dependency parse
| and NER will be exported as separate files if the respective component
| is present in the model's pipeline.
+code(false, "bash", "$", false, false, true).
python -m 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.
+row("foot")
+cell prints / creates
+cell #[code stdout], HTML
+cell Training results and optional displaCy visualizations.
+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 #[code meta.json] is supplied, or a #[code meta.json] is
| found in the input directory, this file is used. Otherwise, the data can
| be entered directly from the command line. After packaging, you can run
| #[code python setup.py sdist] from the newly created directory to turn
| your model into an installable archive file.
+code(false, "bash", "$", false, false, true).
python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
+aside-code("Example", "bash").
python -m spacy package /input /output
cd /output/en_model-0.0.0
python setup.py sdist
pip install dist/en_model-0.0.0.tar.gz
+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 #[code meta.json] file (optional).
+row
+cell #[code --create-meta], #[code -c]
+cell flag
+cell
| #[+tag-new(2)] Create a #[code meta.json] file on the command
| line, even if one already exists in the directory. If an
| existing file is found, its entries will be shown as the defaults
| in the command line prompt.
+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.
+row("foot")
+cell creates
+cell directory
+cell A Python package containing the spaCy model.
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