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title teaser source menu
Command Line Interface Download, train and package models, and debug spaCy spacy/cli
download
download
info
info
validate
validate
init
init
convert
convert
debug
debug
train
train
pretrain
pretrain
evaluate
evaluate
package
package
project
project

spaCy's CLI provides a range of helpful commands for downloading and training models, converting data and debugging your config, data and installation. For a list of available commands, you can type python -m spacy --help. You can also add the --help flag to any command or subcommand to see the description, available arguments and usage.

download

Download models for spaCy. The downloader finds the best-matching compatible version and uses pip install to download the model as a package. 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]
Name Description
model Model name, e.g. en_core_web_sm. str (positional)
--direct, -d Force direct download of exact model version. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
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. Any (option/flag)
CREATES The installed model package in your site-packages directory.

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]
Name Description
model A model, i.e. package name or path (optional). Optional[str] (positional)
--markdown, -md Print information as Markdown. bool (flag)
--silent, -s 2.0.12 Don't print anything, just return the values. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
PRINTS Information about your spaCy installation.

validate

Find all models installed in the current environment 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. 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 are found, it will return 1.

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

init

The spacy init CLI includes helpful commands for initializing training config files and model directories.

init config

Initialize and save a config.cfg file using the recommended settings for your use case. It works just like the quickstart widget, only that it also auto-fills all default values and exports a training-ready config. The settings you specify will impact the suggested model architectures and pipeline setup, as well as the hyperparameters. You can also adjust and customize those settings in your config file later.

Example

$ python -m spacy init config config.cfg --lang en --pipeline ner,textcat --optimize accuracy

$ python -m spacy init config [output_file] [--lang] [--pipeline] [--optimize] [--cpu]
Name Description
output_file Path to output .cfg file or - to write the config to stdout (so you can pipe it forward to a file). Note that if you're writing to stdout, no additional logging info is printed. Path (positional)
--lang, -l Optional code of the language to use. Defaults to "en". str (option)
--pipeline, -p Comma-separated list of trainable pipeline components to include in the model. Defaults to "tagger,parser,ner". str (option)
--optimize, -o "efficiency" or "accuracy". Whether to optimize for efficiency (faster inference, smaller model, lower memory consumption) or higher accuracy (potentially larger and slower model). This will impact the choice of architecture, pretrained weights and related hyperparameters. Defaults to "efficiency". str (option)
--cpu, -C Whether the model needs to run on CPU. This will impact the choice of architecture, pretrained weights and related hyperparameters. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
CREATES The config file for training.

init fill-config

Auto-fill a partial config.cfg file file with all default values, e.g. a config generated with the quickstart widget. Config files used for training should always be complete and not contain any hidden defaults or missing values, so this command helps you create your final training config. In order to find the available settings and defaults, all functions referenced in the config will be created, and their signatures are used to find the defaults. If your config contains a problem that can't be resolved automatically, spaCy will show you a validation error with more details.

Example

$ python -m spacy init fill-config base.cfg config.cfg

$ python -m spacy init fill-config [base_path] [output_file] [--diff]
Name Description
base_path Path to base config to fill, e.g. generated by the quickstart widget. Path (positional)
output_file Path to output .cfg file. If not set, the config is written to stdout so you can pipe it forward to a file. Path (positional)
--diff, -D Print a visual diff highlighting the changes. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
CREATES Complete and auto-filled config file for training.

init model

Create a new model directory from raw data, like word frequencies, Brown clusters and word vectors. 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.

The init-model command is now available as a subcommand of spacy init.

$ python -m spacy init model [lang] [output_dir] [--jsonl-loc] [--vectors-loc] [--prune-vectors]
Name Description
lang Model language ISO code, e.g. en. str (positional)
output_dir Model output directory. Will be created if it doesn't exist. Path (positional)
--jsonl-loc, -j Optional location of JSONL-formatted vocabulary file with lexical attributes. Optional[Path] (option)
--vectors-loc, -v 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. Optional[Path] (option)
--truncate-vectors, -t 2.3 Number of vectors to truncate to when reading in vectors file. Defaults to 0 for no truncation. int (option)
--prune-vectors, -V Number of vectors to prune the vocabulary to. Defaults to -1 for no pruning. int (option)
--vectors-name, -vn Name to assign to the word vectors in the meta.json, e.g. en_core_web_md.vectors. str (option)
--help, -h Show help message and available arguments. bool (flag)
CREATES A spaCy model containing the vocab and vectors.

convert

Convert files into spaCy's binary training data format, a serialized DocBin, 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] [--converter] [--file-type] [--n-sents] [--seg-sents] [--model] [--morphology] [--merge-subtokens] [--ner-map] [--lang]
Name Description
input_file Input file. Path (positional)
output_dir Output directory for converted file. Defaults to "-", meaning data will be written to stdout. Optional[Path] (positional)
--converter, -c 2 Name of converter to use (see below). str (option)
--file-type, -t 2.1 Type of file to create. Either spacy (default) for binary DocBin data or json for v2.x JSON format. str (option)
--n-sents, -n Number of sentences per document. int (option)
--seg-sents, -s 2.2 Segment sentences (for --converter ner). bool (flag)
--model, -b 2.2 Model for parser-based sentence segmentation (for --seg-sents). Optionalstr
--morphology, -m Enable appending morphology to tags. bool (flag)
--ner-map, -nm NER tag mapping (as JSON-encoded dict of entity types). OptionalPath
--lang, -l 2.1 Language code (if tokenizer required). Optional[str] (option)
--help, -h Show help message and available arguments. bool (flag)
CREATES Binary DocBin training data that can be used with spacy train.

Converters

ID Description
auto Automatically pick converter based on file extension and file content (default).
json JSON-formatted training data used in spaCy v2.x.
conll 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 `

debug

The spacy debug CLI includes helpful commands for debugging and profiling your configs, data and implementations.

debug config

Debug a config.cfg file and show validation errors. The command will create all objects in the tree and validate them. Note that some config validation errors are blocking and will prevent the rest of the config from being resolved. This means that you may not see all validation errors at once and some issues are only shown once previous errors have been fixed. To auto-fill a partial config and save the result, you can use the init fillconfig command.

$ python -m spacy debug config [config_path] [--code_path] [overrides]

Example

$ python -m spacy debug config ./config.cfg

✘ Config validation error

training -> dropout     field required
training -> optimizer   field required
training -> optimize    extra fields not permitted

{'vectors': 'en_vectors_web_lg', 'seed': 0, 'accumulate_gradient': 1, 'init_tok2vec': None, 'raw_text': None, 'patience': 1600, 'max_epochs': 0, 'max_steps': 20000, 'eval_frequency': 200, 'frozen_components': [], 'optimize': None, 'batcher': {'@batchers': 'batch_by_words.v1', 'discard_oversize': False, 'tolerance': 0.2, 'get_length': None, 'size': {'@schedules': 'compounding.v1', 'start': 100, 'stop': 1000, 'compound': 1.001, 't': 0.0}}, 'dev_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}, 'score_weights': {'tag_acc': 0.5, 'dep_uas': 0.25, 'dep_las': 0.25, 'sents_f': 0.0}, 'train_corpus': {'@readers': 'spacy.Corpus.v1', 'path': '', 'max_length': 0, 'gold_preproc': False, 'limit': 0}}

If your config contains missing values, you can run the 'init fill-config'
command to fill in all the defaults, if possible:

python -m spacy init fill-config tmp/starter-config_invalid.cfg --base tmp/starter-config_invalid.cfg
Name Description
config_path Path to training config file containing all settings and hyperparameters. Path (positional)
--code_path, -c Path to Python file with additional code to be imported. Allows registering custom functions for new architectures. Optional[Path] (option)
--help, -h Show help message and available arguments. bool (flag)
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --paths.train ./train.spacy. Any (option/flag)
PRINTS Config validation errors, if available.

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.

The debug data command is now available as a subcommand of spacy debug. It takes the same arguments as train and reads settings off the config.cfg file and optional overrides on the CLI.

$ python -m spacy debug data [config_path] [--code] [--ignore-warnings] [--verbose] [--no-format] [overrides]

Example

$ python -m spacy debug data ./config.cfg

=========================== 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
Name Description
config_path Path to training config file containing all settings and hyperparameters. Path (positional)
--code, -c Path to Python file with additional code to be imported. Allows registering custom functions for new architectures. Optional[Path] (option)
--ignore-warnings, -IW Ignore warnings, only show stats and errors. bool (flag)
--verbose, -V Print additional information and explanations. bool (flag)
--no-format, -NF Don't pretty-print the results. Use this if you want to write to a file. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --paths.train ./train.spacy. Any (option/flag)
PRINTS Debugging information.

debug profile

Profile which functions take the most time in a spaCy pipeline. Input should be formatted as one JSON object per line with a key "text". It can either be provided as a JSONL file, or be read from sys.sytdin. If no input file is specified, the IMDB dataset is loaded via ml_datasets.

The profile command is now available as a subcommand of spacy debug.

$ python -m spacy debug profile [model] [inputs] [--n-texts]
Name Description
model A loadable spaCy model. str (positional)
inputs Optional path to input file, or - for standard input. Path (positional)
--n-texts, -n Maximum number of texts to use if available. Defaults to 10000. int (option)
--help, -h Show help message and available arguments. bool (flag)
PRINTS Profiling information for the model.

debug model

Debug a Thinc Model by running it on a sample text and checking how it updates its internal weights and parameters.

$ python -m spacy debug model [config_path] [component] [--layers] [-DIM] [-PAR] [-GRAD] [-ATTR] [-P0] [-P1] [-P2] [P3] [--gpu-id]

In this example log, we just print the name of each layer after creation of the model ("Step 0"), which helps us to understand the internal structure of the Neural Network, and to focus on specific layers that we want to inspect further (see next example).

$ python -m spacy debug model ./config.cfg tagger -P0

ℹ Using CPU
ℹ Fixing random seed: 0
ℹ Analysing model with ID 62

========================== STEP 0 - before training ==========================
ℹ Layer 0: model ID 62:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual>>with_array-softmax'
ℹ Layer 1: model ID 59:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual'
ℹ Layer 2: model ID 61: 'with_array-softmax'
ℹ Layer 3: model ID 24:
'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list'
ℹ Layer 4: model ID 58: 'with_array-residual>>residual>>residual>>residual'
ℹ Layer 5: model ID 60: 'softmax'
ℹ Layer 6: model ID 13: 'extract_features'
ℹ Layer 7: model ID 14: 'list2ragged'
ℹ Layer 8: model ID 16:
'with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
ℹ Layer 9: model ID 22: 'with_array-maxout>>layernorm>>dropout'
ℹ Layer 10: model ID 23: 'ragged2list'
ℹ Layer 11: model ID 57: 'residual>>residual>>residual>>residual'
ℹ Layer 12: model ID 15:
'ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
ℹ Layer 13: model ID 21: 'maxout>>layernorm>>dropout'
ℹ Layer 14: model ID 32: 'residual'
ℹ Layer 15: model ID 40: 'residual'
ℹ Layer 16: model ID 48: 'residual'
ℹ Layer 17: model ID 56: 'residual'
ℹ Layer 18: model ID 3: 'ints-getitem>>hashembed'
ℹ Layer 19: model ID 6: 'ints-getitem>>hashembed'
ℹ Layer 20: model ID 9: 'ints-getitem>>hashembed'
...

In this example log, we see how initialization of the model (Step 1) propagates the correct values for the nI (input) and nO (output) dimensions of the various layers. In the softmax layer, this step also defines the W matrix as an all-zero matrix determined by the nO and nI dimensions. After a first training step (Step 2), this matrix has clearly updated its values through the training feedback loop.

$ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P2

ℹ Using CPU
ℹ Fixing random seed: 0
ℹ Analysing model with ID 62

========================= STEP 0 - before training =========================
ℹ Layer 5: model ID 60: 'softmax'
ℹ  - dim nO: None
ℹ  - dim nI: 96
ℹ  - param W: None
ℹ  - param b: None
ℹ Layer 15: model ID 40: 'residual'
ℹ  - dim nO: None
ℹ  - dim nI: None

======================= STEP 1 - after initialization =======================
ℹ Layer 5: model ID 60: 'softmax'
ℹ  - dim nO: 4
ℹ  - dim nI: 96
ℹ  - param W: (4, 96) - sample: [0. 0. 0. 0. 0.]
ℹ  - param b: (4,) - sample: [0. 0. 0. 0.]
ℹ Layer 15: model ID 40: 'residual'
ℹ  - dim nO: 96
ℹ  - dim nI: None

========================== STEP 2 - after training ==========================
ℹ Layer 5: model ID 60: 'softmax'
ℹ  - dim nO: 4
ℹ  - dim nI: 96
ℹ  - param W: (4, 96) - sample: [ 0.00283958 -0.00294119  0.00268396 -0.00296219
-0.00297141]
ℹ  - param b: (4,) - sample: [0.00300002 0.00300002 0.00300002 0.00300002]
ℹ Layer 15: model ID 40: 'residual'
ℹ  - dim nO: 96
ℹ  - dim nI: None
Name Description
config_path Path to training config file containing all settings and hyperparameters. Path (positional)
component Name of the pipeline component of which the model should be analyzed. str (positional)
--layers, -l Comma-separated names of layer IDs to print. str (option)
--dimensions, -DIM Show dimensions of each layer. bool (flag)
--parameters, -PAR Show parameters of each layer. bool (flag)
--gradients, -GRAD Show gradients of each layer. bool (flag)
--attributes, -ATTR Show attributes of each layer. bool (flag)
--print-step0, -P0 Print model before training. bool (flag)
--print-step1, -P1 Print model after initialization. bool (flag)
--print-step2, -P2 Print model after training. bool (flag)
--print-step3, -P3 Print final predictions. bool (flag)
--gpu-id, -g GPU ID or -1 for CPU. Defaults to -1. int (option)
--help, -h Show help message and available arguments. bool (flag)
PRINTS Debugging information.

train

Train a model. Expects data in spaCy's binary format and a config file with all settings and hyperparameters. Will save out the best model from all epochs, as well as the final model. The --code argument can be used to provide a Python file that's imported before the training process starts. This lets you register custom functions and architectures and refer to them in your config, all while still using spaCy's built-in train workflow. If you need to manage complex multi-step training workflows, check out the new spaCy projects.

The train command doesn't take a long list of command-line arguments anymore and instead expects a single config.cfg file containing all settings for the pipeline, training process and hyperparameters. Config values can be overwritten on the CLI if needed. For example, --paths.train ./train.spacy sets the variable train in the section [paths].

$ python -m spacy train [config_path] [--output] [--code] [--verbose] [overrides]
Name Description
config_path Path to training config file containing all settings and hyperparameters. Path (positional)
--output, -o Directory to store model in. Will be created if it doesn't exist. Optional[Path] (positional)
--code, -c Path to Python file with additional code to be imported. Allows registering custom functions for new architectures. Optional[Path] (option)
--verbose, -V Show more detailed messages during training. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --paths.train ./train.spacy. Any (option/flag)
CREATES The final model and the best model.

pretrain

Pretrain the "token to vector" (Tok2vec) layer of pipeline components on raw text, 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 include a path to one of these pretrained weights files in your training config as the init_tok2vec setting when you train your model. This technique may be especially helpful if you have little labelled data. See the usage docs on pretraining for more info.

As of spaCy v3.0, the pretrain command takes the same config file as the train command. This ensures that settings are consistent between pretraining and training. Settings for pretraining can be defined in the [pretraining] block of the config file. See the data format for details.

$ python -m spacy pretrain [texts_loc] [output_dir] [config_path] [--code] [--resume-path] [--epoch-resume] [overrides]
Name Description
texts_loc 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. Path (positional)
output_dir Directory to write models to on each epoch. Path (positional)
config_path Path to training config file containing all settings and hyperparameters. Path (positional)
--code, -c Path to Python file with additional code to be imported. Allows registering custom functions for new architectures. Optional[Path] (option)
--resume-path, -r Path to pretrained weights from which to resume pretraining. Optional[Path] (option)
--epoch-resume, -er The epoch to resume counting from when using --resume-path. Prevents unintended overwriting of existing weight files. Optional[int] (option)
--help, -h Show help message and available arguments. bool (flag)
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --training.dropout 0.2. Any (option/flag)
CREATES The pretrained weights that can be used to initialize spacy train.

evaluate

Evaluate a model. Expects a loadable spaCy model and evaluation data in the binary .spacy format. The --gold-preproc option sets up the evaluation examples with gold-standard sentences and tokens for the predictions. Gold preprocessing helps the annotations align to the tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due to train/test skew. To render a sample of dependency parses in a HTML file using the displaCy visualizations, set as output directory as the --displacy-path argument.

$ python -m spacy evaluate [model] [data_path] [--output] [--gold-preproc] [--gpu-id] [--displacy-path] [--displacy-limit]
Name Description
model Model to evaluate. Can be a package or a path to a model data directory. str (positional)
data_path Location of evaluation data in spaCy's binary format. Path (positional)
--output, -o Output JSON file for metrics. If not set, no metrics will be exported. Optional[Path] (option)
--gold-preproc, -G Use gold preprocessing. bool (flag)
--gpu-id, -g GPU to use, if any. Defaults to -1 for CPU. int (option)
--displacy-path, -dp Directory to output rendered parses as HTML. If not set, no visualizations will be generated. Optional[Path] (option)
--displacy-limit, -dl 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. int (option)
--help, -h Show help message and available arguments. bool (flag)
CREATES Training results and optional metrics and visualizations.

package

Generate an installable 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. spaCy will then create a .tar.gz archive file that you can distribute and install with pip install.

The spacy package command now also builds the .tar.gz archive automatically, so you don't have to run python setup.py sdist separately anymore. To disable this, you can set the --no-sdist flag.

$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--no-sdist] [--version] [--force]

Example

$ python -m spacy package /input /output
$ cd /output/en_model-0.0.0
$ pip install dist/en_model-0.0.0.tar.gz
Name Description
input_dir Path to directory containing model data. Path (positional)
output_dir Directory to create package folder in. Path (positional)
--meta-path, -m 2 Path to meta.json file (optional). Optional[Path] (option)
--create-meta, -C 2 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. bool (flag)
--no-sdist, -NS, Don't build the .tar.gz sdist automatically. Can be set if you want to run this step manually. bool (flag)
--version, -v 3 Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template. Optional[str] (option)
--force, -f Force overwriting of existing folder in output directory. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
CREATES A Python package containing the spaCy model.

project

The spacy project CLI includes subcommands for working with spaCy projects, end-to-end workflows for building and deploying custom spaCy models.

project clone

Clone a project template from a Git repository. Calls into git under the hood and uses the sparse checkout feature, so you're only downloading what you need. By default, spaCy's project templates repo is used, but you can provide any other repo (public or private) that you have access to using the --repo option.

$ python -m spacy project clone [name] [dest] [--repo]

Example

$ python -m spacy project clone some_example

Clone from custom repo:

$ python -m spacy project clone template --repo https://github.com/your_org/your_repo
Name Description
name The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like dir/template. str (positional)
dest Where to clone the project. Defaults to current working directory. Path (positional)
--repo, -r The repository to clone from. Can be any public or private Git repo you have access to. str (option)
--help, -h Show help message and available arguments. bool (flag)
CREATES The cloned project directory.

project assets

Fetch project assets like datasets and pretrained weights. Assets are defined in the assets section of the project.yml. If a checksum is provided, the file is only downloaded if no local file with the same checksum exists and spaCy will show an error if the checksum of the downloaded file doesn't match. If assets don't specify a url they're considered "private" and you have to take care of putting them into the destination directory yourself. If a local path is provided, the asset is copied into the current project.

$ python -m spacy project assets [project_dir]

Example

$ python -m spacy project assets
Name Description
project_dir Path to project directory. Defaults to current working directory. Path (positional)
--help, -h Show help message and available arguments. bool (flag)
CREATES Downloaded or copied assets defined in the project.yml.

project run

Run a named command or workflow defined in the project.yml. If a workflow name is specified, all commands in the workflow are run, in order. If commands define dependencies or outputs, they will only be re-run if state has changed. For example, if the input dataset changes, a preprocessing command that depends on those files will be re-run.

$ python -m spacy project run [subcommand] [project_dir] [--force] [--dry]

Example

$ python -m spacy project run train
Name Description
subcommand Name of the command or workflow to run. str (positional)
project_dir Path to project directory. Defaults to current working directory. Path (positional)
--force, -F Force re-running steps, even if nothing changed. bool (flag)
--dry, -D  Perform a dry run and don't execute scripts. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
EXECUTES The command defined in the project.yml.

project dvc

Auto-generate Data Version Control (DVC) config file. Calls dvc run with --no-exec under the hood to generate the dvc.yaml. A DVC project can only define one pipeline, so you need to specify one workflow defined in the project.yml. If no workflow is specified, the first defined workflow is used. The DVC config will only be updated if the project.yml changed. For details, see the DVC integration docs.

This command requires DVC to be installed and initialized in the project directory, e.g. via dvc init. You'll also need to add the assets you want to track with dvc add.

$ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose]

Example

$ git init
$ dvc init
$ python -m spacy project dvc all
Name Description
project_dir Path to project directory. Defaults to current working directory. Path (positional)
workflow Name of workflow defined in project.yml. Defaults to first workflow if not set. Optional[str] (positional)
--force, -F Force-updating config file. bool (flag)
--verbose, -V  Print more output generated by DVC. bool (flag)
--help, -h Show help message and available arguments. bool (flag)
CREATES A dvc.yaml file in the project directory, based on the steps defined in the given workflow.