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title teaser source menu
Command Line Interface
Download, train and package models, and debug spaCy
spacy/cli
Download
download
Link
link
Info
info
Validate
validate
Convert
convert
Train
train
Pretrain
pretrain
Init Model
init-model
Evaluate
evaluate
Package
package

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 spacy --help.

Download {#download}

Download models for spaCy. The downloader finds the best-matching compatible version, uses pip to download the model as a package and automatically creates a 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. en_core_web_sm-2.0.0).

Downloading best practices

The 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 not recommended to use this command as part of an automated process. If you know which model your project needs, you should consider a 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.

$ python -m spacy download [model] [--direct]
Argument Type Description
model positional Model name or shortcut (en, de, en_core_web_sm).
--direct, -d flag Force direct download of exact model version.
other 2.1 - Additional installation options to be passed to pip install when installing the model package. For example, --user to install to the user home directory.
--help, -h flag Show help message and available arguments.
CREATES directory, symlink The installed model package in your site-packages directory and a shortcut link as a symlink in spacy/data.

Link {#link}

Create a 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 spacy.load().

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 loadable model packages. If you want to load a data directory, call spacy.load() or Language.from_disk() with the path, or use the package command to create a model package.

$ python -m spacy link [origin] [link_name] [--force]
Argument Type Description
origin positional Model name if package, or path to local directory.
link_name positional Name of the shortcut link to create.
--force, -f flag Force overwriting of existing link.
--help, -h flag Show help message and available arguments.
CREATES symlink A shortcut link of the given name as a symlink in spacy/data.

Info {#info}

Print information about your spaCy installation, models and local setup, and generate Markdown-formatted markup to copy-paste into GitHub issues.

$ python -m spacy info [--markdown]
$ python -m spacy info [model] [--markdown]
Argument Type Description
model positional A model, i.e. shortcut link, package name or path (optional).
--markdown, -md flag Print information as Markdown.
--silent, -s 2.0.12 flag Don't print anything, just return the values.
--help, -h flag Show help message and available arguments.
PRINTS stdout Information about your spaCy installation.

Validate {#validate new="2"}

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 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.

Automated validation

You can also use the 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 1.

$ python -m spacy validate
Argument Type Description
PRINTS stdout Details about the compatibility of your installed models.

Convert {#convert}

Convert files into spaCy's JSON format for use with the 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.

$ python -m spacy convert [input_file] [output_dir] [--file-type] [--converter]
[--n-sents] [--morphology] [--lang]
Argument Type Description
input_file positional Input file.
output_dir positional Output directory for converted file. Defaults to "-", meaning data will be written to stdout.
--file-type, -t 2.1 option Type of file to create (see below).
--converter, -c 2 option Name of converter to use (see below).
--n-sents, -n option Number of sentences per document.
--morphology, -m option Enable appending morphology to tags.
--lang, -l 2.1 option Language code (if tokenizer required).
--help, -h flag Show help message and available arguments.
CREATES JSON Data in spaCy's JSON format.

Output file types {new="2.1"}

Which format should I choose?

If you're not sure, go with the default jsonl. Newline-delimited JSON means that there's one JSON object per line. Unlike a regular JSON file, it can also be read in line-by-line and you won't have to parse the entire file first. This makes it a very convenient format for larger corpora.

All output files generated by this command are compatible with spacy train.

ID Description
jsonl Newline-delimited JSON (default).
json Regular JSON.
msg Binary MessagePack format.

Converter options

ID Description
auto Automatically pick converter based on file extension (default).
conll, conllu, conllubio Universal Dependencies .conllu or .conll format.
ner Tab-based named entity recognition format.
iob IOB or IOB2 named entity recognition format.

Train {#train}

Train a model. Expects data in spaCy's JSON format. On each epoch, a model will be saved out to the directory. Accuracy scores and model details will be added to a meta.json to allow packaging the model using the package command.

As of spaCy 2.1, the --no-tagger, --no-parser and --no-entities flags have been replaced by a --pipeline option, which lets you define comma-separated names of pipeline components to train. For example, --pipeline tagger,parser will only train the tagger and parser.

$ 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]
Argument Type Description
lang positional Model language.
output_path positional Directory to store model in. Will be created if it doesn't exist.
train_path positional Location of JSON-formatted training data. Can be a file or a directory of files.
dev_path positional Location of JSON-formatted development data for evaluation. Can be a file or a directory of files.
--base-model, -b option Optional name of base model to update. Can be any loadable spaCy model.
--pipeline, -p 2.1 option Comma-separated names of pipeline components to train. Defaults to 'tagger,parser,ner'.
--vectors, -v option Model to load vectors from.
--n-iter, -n option Number of iterations (default: 30).
--n-examples, -ns option Number of examples to use (defaults to 0 for all examples).
--use-gpu, -g option Whether to use GPU. Can be either 0, 1 or -1.
--version, -V option Model version. Will be written out to the model's meta.json after training.
--meta-path, -m 2 option Optional path to model meta.json. All relevant properties like lang, pipeline and spacy_version will be overwritten.
--init-tok2vec, -t2v 2.1 option Path to pretrained weights for the token-to-vector parts of the models. See spacy pretrain. Experimental.
--parser-multitasks, -pt option Side objectives for parser CNN, e.g. 'dep' or 'dep,tag'
--entity-multitasks, -et option Side objectives for NER CNN, e.g. 'dep' or 'dep,tag'
--noise-level, -nl option Float indicating the amount of corruption for data augmentation.
--gold-preproc, -G flag Use gold preprocessing.
--learn-tokens, -T flag Make parser learn gold-standard tokenization by merging ] subtokens. Typically used for languages like Chinese.
--verbose, -VV 2.0.13 flag Show more detailed messages during training.
--help, -h flag Show help message and available arguments.
CREATES model, pickle A spaCy model on each epoch.

Environment variables for hyperparameters {#train-hyperparams new="2"}

spaCy lets you set hyperparameters for training via environment variables. For example:

$ token_vector_width=256 learn_rate=0.0001 spacy train [...]

Usage with alias

Environment variables keep the command simple and allow you to to create an alias for your custom train command while still being able to easily tweak the hyperparameters.

alias train-parser="python -m spacy train en /output /data /train /dev -n 1000"
token_vector_width=256 train-parser
Name Description Default
dropout_from Initial dropout rate. 0.2
dropout_to Final dropout rate. 0.2
dropout_decay Rate of dropout change. 0.0
batch_from Initial batch size. 1
batch_to Final batch size. 64
batch_compound Rate of batch size acceleration. 1.001
token_vector_width Width of embedding tables and convolutional layers. 128
embed_size Number of rows in embedding tables. 7500
hidden_width Size of the parser's and NER's hidden layers. 128
learn_rate Learning rate. 0.001
optimizer_B1 Momentum for the Adam solver. 0.9
optimizer_B2 Adagrad-momentum for the Adam solver. 0.999
optimizer_eps Epsilon value for the Adam solver. 1e-08
L2_penalty L2 regularization penalty. 1e-06
grad_norm_clip Gradient L2 norm constraint. 1.0

Pretrain {#pretrain new="2.1" tag="experimental"}

Pre-train the "token to vector" (tok2vec) layer of pipeline components, using an approximate language-modeling objective. Specifically, we load pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which match the pre-trained ones. The weights are saved to a directory after each epoch. You can then pass a path to one of these pre-trained weights files to the spacy train command.

This technique may be especially helpful if you have little labelled data. However, it's still quite experimental, so your mileage may vary. To load the weights back in during spacy train, you need to ensure all settings are the same between pretraining and training. The API and errors around this need some improvement.

$ python -m spacy pretrain [texts_loc] [vectors_model] [output_dir] [--width]
[--depth] [--embed-rows] [--dropout] [--seed] [--n-iter] [--use-vectors]
Argument Type Description
texts_loc positional Path to JSONL file with raw texts to learn from, with text provided as the key "text". See here for details.
vectors_model positional Name or path to spaCy model with vectors to learn from.
output_dir positional Directory to write models to on each epoch.
--width, -cw option Width of CNN layers.
--depth, -cd option Depth of CNN layers.
--embed-rows, -er option Number of embedding rows.
--dropout, -d option Dropout rate.
--batch-size, -bs option Number of words per training batch.
--max-length, -xw option Maximum words per example. Longer examples are discarded.
--min-length, -nw option Minimum words per example. Shorter examples are discarded.
--seed, -s option Seed for random number generators.
--n-iter, -i option Number of iterations to pretrain.
--use-vectors, -uv flag Whether to use the static vectors as input features.
CREATES weights The pre-trained weights that can be used to initialize spacy train.

JSONL format for raw text {#pretrain-jsonl}

Raw text can be provided as a .jsonl (newline-delimited JSON) file containing one input text per line (roughly paragraph length is good). Optionally, custom tokenization can be provided.

Tip: Writing JSONL

Our utility library srsly provides a handy write_jsonl helper that takes a file path and list of dictionaries and writes out JSONL-formatted data.

import srsly
data = [{"text": "Some text"}, {"text": "More..."}]
srsly.write_jsonl("/path/to/text.jsonl", data)
Key Type Description
text unicode The raw input text.
tokens list Optional tokenization, one string per token.
### Example
{"text": "Can I ask where you work now and what you do, and if you enjoy it?"}
{"text": "They may just pull out of the Seattle market completely, at least until they have autonomous vehicles."}
{"text": "My cynical view on this is that it will never be free to the public. Reason: what would be the draw of joining the military? Right now their selling point is free Healthcare and Education. Ironically both are run horribly and most, that I've talked to, come out wishing they never went in."}

Init Model {#init-model new="2"}

Create a new model directory from raw data, like word frequencies, Brown clusters and word vectors. This command is similar to the spacy model command in v1.x.

As of v2.1.0, the --freqs-loc and --clusters-loc are deprecated and have been replaced with the --jsonl-loc argument, which lets you pass in a a newline-delimited JSON (JSONL) file containing one lexical entry per line. For more details on the format, see the annotation specs.

$ python -m spacy init-model [lang] [output_dir] [--jsonl-loc] [--vectors-loc]
[--prune-vectors]
Argument Type Description
lang positional Model language ISO code, e.g. en.
output_dir positional Model output directory. Will be created if it doesn't exist.
--jsonl-loc, -j option Optional location of JSONL-formatted vocabulary file with lexical attributes.
--vectors-loc, -v option 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 .txt format or as a zipped text file in .zip or .tar.gz format.
--prune-vectors, -V flag Number of vectors to prune the vocabulary to. Defaults to -1 for no pruning.
CREATES model A spaCy model containing the vocab and vectors.

Evaluate {#evaluate new="2"}

Evaluate a model's accuracy and speed on JSON-formatted annotated data. Will print the results and optionally export displaCy visualizations of a sample set of parses to .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.

$ python -m spacy evaluate [model] [data_path] [--displacy-path] [--displacy-limit]
[--gpu-id] [--gold-preproc]
Argument Type Description
model positional Model to evaluate. Can be a package or shortcut link name, or a path to a model data directory.
data_path positional Location of JSON-formatted evaluation data.
--displacy-path, -dp option Directory to output rendered parses as HTML. If not set, no visualizations will be generated.
--displacy-limit, -dl option Number of parses to generate per file. Defaults to 25. Keep in mind that a significantly higher number might cause the .html files to render slowly.
--gpu-id, -g option GPU to use, if any. Defaults to -1 for CPU.
--gold-preproc, -G flag Use gold preprocessing.
CREATES stdout, HTML Training results and optional displaCy visualizations.

Package {#package}

Generate a 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. After packaging, you can run python setup.py sdist from the newly created directory to turn your model into an installable archive file.

$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
### Example
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
Argument Type Description
input_dir positional Path to directory containing model data.
output_dir positional Directory to create package folder in.
--meta-path, -m 2 option Path to meta.json file (optional).
--create-meta, -c 2 flag Create a 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.
--help, -h flag Show help message and available arguments.
CREATES directory A Python package containing the spaCy model.
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