spaCy/website/docs/api/cli.md

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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

For a list of available commands, type spacy --help.

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]
Argument Type Description
model positional Model name, e.g. 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 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]
Argument Type Description
model positional A model, i.e. 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 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
Argument Type Description
PRINTS stdout 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 export a config.cfg file for training and update it with all default values, if possible. 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 config. It takes one of the following options:

  • --base: Base config to auto-fill, e.g. created using the training quickstart widget.
  • --lang: Base language code to use for blank config.
  • --model: Base model to copy config from.
### with base config {wrap="true"}
$ python -m spacy init config config.cfg --base base.cfg
### blank language {wrap="true"}
$ python -m spacy init config config.cfg --lang en --pipeline tagger,parser
$ python -m spacy init config [output] [--base] [--lang] [--model] [--pipeline]
Argument Type Description
output positional Path to output .cfg file. If not set, the config is written to stdout so you can pipe it forward to a file.
--base, -b option Optional base config file to auto-fill with defaults.
--lang, -l option Optional language code to use for blank config. If a --pipeline is specified, the components will be added in order.
--model, -m option Optional base model to copy config from. If a --pipeline is specified, only those components will be kept, and all other components not in the model will be added.
--pipeline, -p option Optional comma-separate pipeline of components to add to blank language or model.
CREATES config 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. 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.

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]
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.
CREATES model 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]
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.
--converter, -c 2 option Name of converter to use (see below).
--file-type, -t 2.1 option Type of file to create. Either spacy (default) for binary DocBin data or json for v2.x JSON format.
--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.
--ner-map, -nm option NER tag mapping (as JSON-encoded dict of entity types).
--lang, -l 2.1 option Language code (if tokenizer required).
--help, -h flag Show help message and available arguments.
CREATES binary 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 and produced by docs2json.
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.

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

Example

$ python -m spacy debug config ./config.cfg
✘ Config validation error

training -> use_gpu              field required
training -> omit_extra_lookups   field required
training -> batch_by             field required
training -> raw_text             field required
training -> tag_map              field required
training -> evaluation_batch_size   extra fields not permitted
training -> vectors              extra fields not permitted
training -> width                extra fields not permitted

{'gold_preproc': False, 'max_length': 3000, 'limit': 0, 'orth_variant_level': 0.0, 'dropout': 0.1, 'patience': 6000, 'max_epochs': 0, 'max_steps': 100000, 'eval_frequency': 400, 'seed': 0, 'accumulate_gradient': 4, 'width': 768, 'use_pytorch_for_gpu_memory': True, 'scores': ['speed', 'tags_acc', 'uas', 'las', 'ents_f'], 'score_weights': {'las': 0.4, 'ents_f': 0.4, 'tags_acc': 0.2}, 'init_tok2vec': None, 'vectors': None, 'discard_oversize': True, 'evaluation_batch_size': 16, 'batch_size': {'@schedules': 'compounding.v1', 'start': 800, 'stop': 800, 'compound': 1.001}, 'optimizer': {'@optimizers': 'Adam.v1', 'beta1': 0.9, 'beta2': 0.999, 'L2_is_weight_decay': True, 'L2': 0.01, 'grad_clip': 1.0, 'use_averages': False, 'eps': 1e-08, 'learn_rate': {'@schedules': 'warmup_linear.v1', 'warmup_steps': 250, 'total_steps': 20000, 'initial_rate': 5e-05}}}
Argument Type Description
config_path positional Path to training config file containing all settings and hyperparameters.
--code, -c option Path to Python file with additional code to be imported. Allows registering custom functions for new architectures.
--help, -h flag Show help message and available arguments.
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --training.use_gpu 1.

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.

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

Example

$ python -m spacy debug data ./train.spacy ./dev.spacy ./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 ==============================
 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)
 No word vectors present in the model

========================== Named Entity Recognition ==========================
 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 ===========================
 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 =============================
 Found 111703 sentences with an average length of 18.6 words.
 Found 2251 nonprojective train sentences
 Found 303 nonprojective dev sentences
 47 labels in train data
 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
Argument Type Description
train_path positional Location of binary training data. Can be a file or a directory of files.
dev_path positional Location of binary development data for evaluation. Can be a file or a directory of files.
config_path positional Path to training config file containing all settings and hyperparameters.
--code, -c option Path to Python file with additional code to be imported. Allows registering custom functions for new architectures.
--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.
--help, -h flag Show help message and available arguments.
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --training.use_gpu 1.

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.

$ python -m spacy train [train_path] [dev_path] [config_path] [--output]
[--code] [--verbose] [overrides]
Argument Type Description
train_path positional Location of training data in spaCy's binary format. Can be a file or a directory of files.
dev_path positional Location of development data for evaluation in spaCy's binary format. Can be a file or a directory of files.
config_path positional Path to training config file containing all settings and hyperparameters.
--output, -o positional Directory to store model in. Will be created if it doesn't exist.
--code, -c option Path to Python file with additional code to be imported. Allows registering custom functions for new architectures.
--verbose, -V flag Show more detailed messages during training.
--help, -h flag Show help message and available arguments.
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --training.use_gpu 1.
CREATES model The final model and the best model.

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.

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]
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.
output_dir positional Directory to write models to on each epoch.
config_path positional Path to training config file containing all settings and hyperparameters.
--code, -c option Path to Python file with additional code to be imported. Allows registering custom functions for new architectures.
--resume-path, -r option TODO:
--epoch-resume, -er option TODO:
--help, -h flag Show help message and available arguments.
overrides Config parameters to override. Should be options starting with -- that correspond to the config section and value to override, e.g. --training.use_gpu 1.
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 str 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"]}

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] [--output] [--displacy-path]
[--displacy-limit] [--gpu-id] [--gold-preproc]
Argument Type Description
model positional Model to evaluate. Can be a package or a path to a model data directory.
data_path positional Location of evaluation data in spaCy's binary format.
--output, -o option Output JSON file for metrics. If not set, no metrics will be exported.
--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, JSON, HTML 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
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.
--no-sdist, -NS, flag Don't build the .tar.gz sdist automatically. Can be set if you want to run this step manually.
--version, -v 3 option Package version to override in meta. Useful when training new versions, as it doesn't require editing the meta template.
--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.

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
Argument Type Description
name positional The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like dir/template.
dest positional Where to clone the project. Defaults to current working directory.
--repo, -r option The repository to clone from. Can be any public or private Git repo you have access to.
--help, -h flag Show help message and available arguments.
CREATES directory 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
Argument Type Description
project_dir positional Path to project directory. Defaults to current working directory.
--help, -h flag Show help message and available arguments.
CREATES files 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
Argument Type Description
subcommand positional Name of the command or workflow to run.
project_dir positional Path to project directory. Defaults to current working directory.
--force, -F flag Force re-running steps, even if nothing changed.
--dry, -D flag  Perform a dry run and don't execute scripts.
--help, -h flag Show help message and available arguments.

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
Argument Type Description
project_dir positional Path to project directory. Defaults to current working directory.
workflow positional Name of workflow defined in project.yml. Defaults to first workflow if not set.
--force, -F flag Force-updating config file.
--verbose, -V flag  Print more output generated by DVC.
--help, -h flag Show help message and available arguments.