spaCy/website/docs/api/cli.md

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 Debug data debug-data 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 models for spaCy. The downloader finds the best-matching compatible version, uses pip install to download the model as a package and creates a shortcut link if the model was downloaded via a shortcut. 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-2.2.0).

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] [pip args]

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.
pip args 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 or --no-deps to not install model dependencies.
--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 if installed via shortcut.

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

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] [--silent]

$ python -m spacy info [model] [--markdown] [--silent]

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

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 show a list of models and their installed versions. If any model is out of date, the latest compatible versions and command for updating are shown.

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 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.
--seg-sents, -s 2.2	flag	Segment sentences (for -c ner)
--model, -b 2.2	option	Model for parser-based sentence segmentation (for -s)
--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

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

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

Converter options

ID	Description
auto	Automatically pick converter based on file extension and file content (default).
conll, conllu, conllubio	Universal Dependencies .conllu or .conll format.
ner	NER with IOB/IOB2 tags, one token per line with columns separated by whitespace. The first column is the token and the final column is the IOB tag. Sentences are separated by blank lines and documents are separated by the line -DOCSTART- -X- O O. Supports CoNLL 2003 NER format. See sample data.
iob	NER with IOB/IOB2 tags, one sentence per line with tokens separated by whitespace and annotation separated by `
jsonl	NER data formatted as JSONL with one dict per line and a "text" and "spans" key. This is also the format exported by the Prodigy annotation tool. See sample data.

Debug data

Analyze, debug, and validate your training and development data. Get useful stats, and find problems like invalid entity annotations, cyclic dependencies, low data labels and more.

$ python -m spacy debug-data [lang] [train_path] [dev_path] [--base-model] [--pipeline] [--ignore-warnings] [--verbose] [--no-format]

Argument	Type	Description
lang	positional	Model language.
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.
--tag-map-path, -tm 2.2.4	option	Location of JSON-formatted tag map.
--base-model, -b	option	Optional name of base model to update. Can be any loadable spaCy model.
--pipeline, -p	option	Comma-separated names of pipeline components to train. Defaults to 'tagger,parser,ner'.
--ignore-warnings, -IW	flag	Ignore warnings, only show stats and errors.
--verbose, -V	flag	Print additional information and explanations.
--no-format, -NF	flag	Don't pretty-print the results. Use this if you want to write to a file.

=========================== Data format validation ===========================
✔ Corpus is loadable

=============================== Training stats ===============================
Training pipeline: tagger, parser, ner
Starting with blank model 'en'
18127 training docs
2939 evaluation docs
⚠ 34 training examples also in evaluation data

============================== Vocab & Vectors ==============================
ℹ 2083156 total words in the data (56962 unique)
⚠ 13020 misaligned tokens in the training data
⚠ 2423 misaligned tokens in the dev data
10 most common words: 'the' (98429), ',' (91756), '.' (87073), 'to' (50058),
'of' (49559), 'and' (44416), 'a' (34010), 'in' (31424), 'that' (22792), 'is'
(18952)
ℹ No word vectors present in the model

========================== Named Entity Recognition ==========================
ℹ 18 new labels, 0 existing labels
528978 missing values (tokens with '-' label)
New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
(10490), 'NORP' (9033), 'MONEY' (5164), 'PERCENT' (3761), 'ORDINAL' (2122),
'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
(1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
✔ Good amount of examples for all labels
✔ Examples without occurences available for all labels
✔ No entities consisting of or starting/ending with whitespace

=========================== Part-of-speech Tagging ===========================
ℹ 49 labels in data (57 labels in tag map)
'NN' (266331), 'IN' (227365), 'DT' (185600), 'NNP' (164404), 'JJ' (119830),
'NNS' (110957), '.' (101482), ',' (92476), 'RB' (90090), 'PRP' (90081), 'VB'
(74538), 'VBD' (68199), 'CC' (62862), 'VBZ' (50712), 'VBP' (43420), 'VBN'
(42193), 'CD' (40326), 'VBG' (34764), 'TO' (31085), 'MD' (25863), 'PRP$'
(23335), 'HYPH' (13833), 'POS' (13427), 'UH' (13322), 'WP' (10423), 'WDT'
(9850), 'RP' (8230), 'WRB' (8201), ':' (8168), '''' (7392), '``' (6984), 'NNPS'
(5817), 'JJR' (5689), '$' (3710), 'EX' (3465), 'JJS' (3118), 'RBR' (2872),
'-RRB-' (2825), '-LRB-' (2788), 'PDT' (2078), 'XX' (1316), 'RBS' (1142), 'FW'
(794), 'NFP' (557), 'SYM' (440), 'WP$' (294), 'LS' (293), 'ADD' (191), 'AFX'
(24)
✔ All labels present in tag map for language 'en'

============================= Dependency Parsing =============================
ℹ Found 111703 sentences with an average length of 18.6 words.
ℹ Found 2251 nonprojective train sentences
ℹ Found 303 nonprojective dev sentences
ℹ 47 labels in train data
ℹ 211 labels in projectivized train data
'punct' (236796), 'prep' (188853), 'pobj' (182533), 'det' (172674), 'nsubj'
(169481), 'compound' (116142), 'ROOT' (111697), 'amod' (107945), 'dobj' (93540),
'aux' (86802), 'advmod' (86197), 'cc' (62679), 'conj' (59575), 'poss' (36449),
'ccomp' (36343), 'advcl' (29017), 'mark' (27990), 'nummod' (24582), 'relcl'
(21359), 'xcomp' (21081), 'attr' (18347), 'npadvmod' (17740), 'acomp' (17204),
'auxpass' (15639), 'appos' (15368), 'neg' (15266), 'nsubjpass' (13922), 'case'
(13408), 'acl' (12574), 'pcomp' (10340), 'nmod' (9736), 'intj' (9285), 'prt'
(8196), 'quantmod' (7403), 'dep' (4300), 'dative' (4091), 'agent' (3908), 'expl'
(3456), 'parataxis' (3099), 'oprd' (2326), 'predet' (1946), 'csubj' (1494),
'subtok' (1147), 'preconj' (692), 'meta' (469), 'csubjpass' (64), 'iobj' (1)
⚠ Low number of examples for label 'iobj' (1)
⚠ Low number of examples for 130 labels in the projectivized dependency
trees used for training. You may want to projectivize labels such as punct
before training in order to improve parser performance.
⚠ Projectivized labels with low numbers of examples: appos||attr: 12
advmod||dobj: 13 prep||ccomp: 12 nsubjpass||ccomp: 15 pcomp||prep: 14
amod||dobj: 9 attr||xcomp: 14 nmod||nsubj: 17 prep||advcl: 2 prep||prep: 5
nsubj||conj: 12 advcl||advmod: 18 ccomp||advmod: 11 ccomp||pcomp: 5 acl||pobj:
10 npadvmod||acomp: 7 dobj||pcomp: 14 nsubjpass||pcomp: 1 nmod||pobj: 8
amod||attr: 6 nmod||dobj: 12 aux||conj: 1 neg||conj: 1 dative||xcomp: 11
pobj||dative: 3 xcomp||acomp: 19 advcl||pobj: 2 nsubj||advcl: 2 csubj||ccomp: 1
advcl||acl: 1 relcl||nmod: 2 dobj||advcl: 10 advmod||advcl: 3 nmod||nsubjpass: 6
amod||pobj: 5 cc||neg: 1 attr||ccomp: 16 advcl||xcomp: 3 nmod||attr: 4
advcl||nsubjpass: 5 advcl||ccomp: 4 ccomp||conj: 1 punct||acl: 1 meta||acl: 1
parataxis||acl: 1 prep||acl: 1 amod||nsubj: 7 ccomp||ccomp: 3 acomp||xcomp: 5
dobj||acl: 5 prep||oprd: 6 advmod||acl: 2 dative||advcl: 1 pobj||agent: 5
xcomp||amod: 1 dep||advcl: 1 prep||amod: 8 relcl||compound: 1 advcl||csubj: 3
npadvmod||conj: 2 npadvmod||xcomp: 4 advmod||nsubj: 3 ccomp||amod: 7
advcl||conj: 1 nmod||conj: 2 advmod||nsubjpass: 2 dep||xcomp: 2 appos||ccomp: 1
advmod||dep: 1 advmod||advmod: 5 aux||xcomp: 8 dep||advmod: 1 dative||ccomp: 2
prep||dep: 1 conj||conj: 1 dep||ccomp: 4 cc||ROOT: 1 prep||ROOT: 1 nsubj||pcomp:
3 advmod||prep: 2 relcl||dative: 1 acl||conj: 1 advcl||attr: 4 prep||npadvmod: 1
nsubjpass||xcomp: 1 neg||advmod: 1 xcomp||oprd: 1 advcl||advcl: 1 dobj||dep: 3
nsubjpass||parataxis: 1 attr||pcomp: 1 ccomp||parataxis: 1 advmod||attr: 1
nmod||oprd: 1 appos||nmod: 2 advmod||relcl: 1 appos||npadvmod: 1 appos||conj: 1
prep||expl: 1 nsubjpass||conj: 1 punct||pobj: 1 cc||pobj: 1 conj||pobj: 1
punct||conj: 1 ccomp||dep: 1 oprd||xcomp: 3 ccomp||xcomp: 1 ccomp||nsubj: 1
nmod||dep: 1 xcomp||ccomp: 1 acomp||advcl: 1 intj||advmod: 1 advmod||acomp: 2
relcl||oprd: 1 advmod||prt: 1 advmod||pobj: 1 appos||nummod: 1 relcl||npadvmod:
3 mark||advcl: 1 aux||ccomp: 1 amod||nsubjpass: 1 npadvmod||advmod: 1 conj||dep:
1 nummod||pobj: 1 amod||npadvmod: 1 intj||pobj: 1 nummod||npadvmod: 1
xcomp||xcomp: 1 aux||dep: 1 advcl||relcl: 1
⚠ The following labels were found only in the train data: xcomp||amod,
advcl||relcl, prep||nsubjpass, acl||nsubj, nsubjpass||conj, xcomp||oprd,
advmod||conj, advmod||advmod, iobj, advmod||nsubjpass, dobj||conj, ccomp||amod,
meta||acl, xcomp||xcomp, prep||attr, prep||ccomp, advcl||acomp, acl||dobj,
advcl||advcl, pobj||agent, prep||advcl, nsubjpass||xcomp, prep||dep,
acomp||xcomp, aux||ccomp, ccomp||dep, conj||dep, relcl||compound,
nsubjpass||ccomp, nmod||dobj, advmod||advcl, advmod||acl, dobj||advcl,
dative||xcomp, prep||nsubj, ccomp||ccomp, nsubj||ccomp, xcomp||acomp,
prep||acomp, dep||advmod, acl||pobj, appos||dobj, npadvmod||acomp, cc||ROOT,
relcl||nsubj, nmod||pobj, acl||nsubjpass, ccomp||advmod, pcomp||prep,
amod||dobj, advmod||attr, advcl||csubj, appos||attr, dobj||pcomp, prep||ROOT,
relcl||pobj, advmod||pobj, amod||nsubj, ccomp||xcomp, prep||oprd,
npadvmod||advmod, appos||nummod, advcl||pobj, neg||advmod, acl||attr,
appos||nsubjpass, csubj||ccomp, amod||nsubjpass, intj||pobj, dep||advcl,
cc||neg, xcomp||ccomp, dative||ccomp, nmod||oprd, pobj||dative, prep||dobj,
dep||ccomp, relcl||attr, ccomp||nsubj, advcl||xcomp, nmod||dep, advcl||advmod,
ccomp||conj, pobj||prep, advmod||acomp, advmod||relcl, attr||pcomp,
ccomp||parataxis, oprd||xcomp, intj||advmod, nmod||nsubjpass, prep||npadvmod,
parataxis||acl, prep||pobj, advcl||dobj, amod||pobj, prep||acl, conj||pobj,
advmod||dep, punct||pobj, ccomp||acomp, acomp||advcl, nummod||npadvmod,
dobj||dep, npadvmod||xcomp, advcl||conj, relcl||npadvmod, punct||acl,
relcl||dobj, dobj||xcomp, nsubjpass||parataxis, dative||advcl, relcl||nmod,
advcl||ccomp, appos||npadvmod, ccomp||pcomp, prep||amod, mark||advcl,
prep||advmod, prep||xcomp, appos||nsubj, attr||ccomp, advmod||prt, dobj||ccomp,
aux||conj, advcl||nsubj, conj||conj, advmod||ccomp, advcl||nsubjpass,
attr||xcomp, nmod||conj, npadvmod||conj, relcl||dative, prep||expl,
nsubjpass||pcomp, advmod||xcomp, advmod||dobj, appos||pobj, nsubj||conj,
relcl||nsubjpass, advcl||attr, appos||ccomp, advmod||prep, prep||conj,
nmod||attr, punct||conj, neg||conj, dep||xcomp, aux||xcomp, dobj||acl,
nummod||pobj, amod||npadvmod, nsubj||pcomp, advcl||acl, appos||nmod,
relcl||oprd, prep||prep, cc||pobj, nmod||nsubj, amod||attr, aux||dep,
appos||conj, advmod||nsubj, nsubj||advcl, acl||conj
To train a parser, your data should include at least 20 instances of each label.
⚠ Multiple root labels (ROOT, nsubj, aux, npadvmod, prep) found in
training data. spaCy's parser uses a single root label ROOT so this distinction
will not be available.

================================== Summary ==================================
✔ 5 checks passed
⚠ 8 warnings

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-early-stopping]
[--n-examples] [--use-gpu] [--version] [--meta-path] [--init-tok2vec]
[--parser-multitasks] [--entity-multitasks] [--gold-preproc] [--noise-level]
[--orth-variant-level] [--learn-tokens] [--textcat-arch] [--textcat-multilabel]
[--textcat-positive-label] [--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 2.1	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'.
--replace-components, -R	flag	Replace components from the base model.
--vectors, -v	option	Model to load vectors from.
--n-iter, -n	option	Number of iterations (default: 30).
--n-early-stopping, -ne	option	Maximum number of training epochs without dev accuracy improvement.
--n-examples, -ns	option	Number of examples to use (defaults to 0 for all examples).
--use-gpu, -g	option	GPU ID or -1 for CPU only (default: -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'
--width, -cw 2.2.4	option	Width of CNN layers of Tok2Vec component.
--conv-depth, -cd 2.2.4	option	Depth of CNN layers of Tok2Vec component.
--cnn-window, -cW 2.2.4	option	Window size for CNN layers of Tok2Vec component.
--cnn-pieces, -cP 2.2.4	option	Maxout size for CNN layers of Tok2Vec component.
--use-chars, -chr 2.2.4	flag	Whether to use character-based embedding of Tok2Vec component.
--bilstm-depth, -lstm 2.2.4	option	Depth of BiLSTM layers of Tok2Vec component (requires PyTorch).
--embed-rows, -er 2.2.4	option	Number of embedding rows of Tok2Vec component.
--noise-level, -nl	option	Float indicating the amount of corruption for data augmentation.
--orth-variant-level, -ovl 2.2	option	Float indicating the orthography variation for data augmentation (e.g. 0.3 for making 30% of occurrences of some tokens subject to replacement).
--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.
--textcat-multilabel, -TML 2.2	flag	Text classification classes aren't mutually exclusive (multilabel).
--textcat-arch, -ta 2.2	option	Text classification model architecture. Defaults to "bow".
--textcat-positive-label, -tpl 2.2	option	Text classification positive label for binary classes with two labels.
--tag-map-path, -tm 2.2.4	option	Location of JSON-formatted tag map.
--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

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

Pre-train the "token to vector" (tok2vec) layer of pipeline components, using an approximate language-modeling objective. Specifically, we load pretrained vectors, and train a component like a CNN, BiLSTM, etc to predict vectors which match the pretrained ones. The weights are saved to a directory after each epoch. You can then pass a path to one of these pretrained 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] [--conv-depth] [--cnn-window] [--cnn-pieces] [--use-chars] [--sa-depth]
[--embed-rows] [--loss_func] [--dropout] [--batch-size] [--max-length]
[--min-length]  [--seed] [--n-iter] [--use-vectors] [--n-save-every]
[--init-tok2vec] [--epoch-start]

Argument	Type	Description
texts_loc	positional	Path to JSONL file with raw texts to learn from, with text provided as the key "text" or tokens as the key "tokens". 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.
--conv-depth, -cd	option	Depth of CNN layers.
--cnn-window, -cW 2.2.2	option	Window size for CNN layers.
--cnn-pieces, -cP 2.2.2	option	Maxout size for CNN layers. 1 for Mish.
--use-chars, -chr 2.2.2	flag	Whether to use character-based embedding.
--sa-depth, -sa 2.2.2	option	Depth of self-attention layers.
--embed-rows, -er	option	Number of embedding rows.
--loss-func, -L	option	Loss function to use for the objective. Either "cosine", "L2" or "characters".
--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.
--n-save-every, -se	option	Save model every X batches.
--init-tok2vec, -t2v 2.1	option	Path to pretrained weights for the token-to-vector parts of the models. See spacy pretrain. Experimental.
--epoch-start, -es 2.1.5	option	The epoch to start counting at. Only relevant when using --init-tok2vec and the given weight file has been renamed. Prevents unintended overwriting of existing weight files.
CREATES	weights	The pretrained weights that can be used to initialize spacy train.

JSONL format for raw text

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. Is not required if tokens available.
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."}
{"tokens": ["If", "tokens", "are", "provided", "then", "we", "can", "skip", "the", "raw", "input", "text"]}

Init Model

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. Note that in order to populate the model's vocab, you need to pass in a JSONL-formatted vocabulary file as --jsonl-loc with optional id values that correspond to the vectors table. Just loading in vectors will not automatically populate the vocab.

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 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. Should be a file where the first row contains the dimensions of the vectors, followed by a space-separated Word2Vec table. File can be provided in .txt format or as a zipped text file in .zip or .tar.gz format.
--truncate-vectors, -t 2.3	option	Number of vectors to truncate to when reading in vectors file. Defaults to 0 for no truncation.
--prune-vectors, -V	option	Number of vectors to prune the vocabulary to. Defaults to -1 for no pruning.
--vectors-name, -vn	option	Name to assign to the word vectors in the meta.json, e.g. en_core_web_md.vectors.
--omit-extra-lookups, -OEL 2.3	flag	Do not include any of the extra lookups tables (cluster/prob/sentiment) from spacy-lookups-data in the model.
CREATES	model	A spaCy model containing the vocab and vectors.

Evaluate

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] [--return-scores]

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.
--return-scores, -R	flag	Return dict containing model scores.
CREATES	stdout, HTML	Training results and optional displaCy visualizations.

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.
--force, -f	flag	Force overwriting of existing folder in output directory.
--help, -h	flag	Show help message and available arguments.
CREATES	directory	A Python package containing the spaCy model.