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Fix links to CLI docs
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@ -225,7 +225,7 @@ p
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| Print a formatted, text-wrapped message with optional title. If a text
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| Print a formatted, text-wrapped message with optional title. If a text
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| argument is a #[code Path], it's converted to a string. Should only
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| argument is a #[code Path], it's converted to a string. Should only
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| be used for interactive components like the #[+a("/docs/usage/cli") CLI].
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| be used for interactive components like the #[+a("/docs/api/cli") CLI].
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+aside-code("Example").
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+aside-code("Example").
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data_path = Path('/some/path')
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data_path = Path('/some/path')
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@ -535,7 +535,7 @@ p
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| #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) word_freqs.py]
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| #[+src(gh("spacy-dev-resources", "training/word_freqs.py")) word_freqs.py]
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| script from the spaCy developer resources. Note that your corpus should
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| script from the spaCy developer resources. Note that your corpus should
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| not be preprocessed (i.e. you need punctuation for example). The
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| not be preprocessed (i.e. you need punctuation for example). The
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| #[+a("/docs/usage/cli#model") #[code model] command] expects a
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| #[+a("/docs/api/cli#model") #[code model]] command expects a
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| tab-separated word frequencies file with three columns:
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| tab-separated word frequencies file with three columns:
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+list("numbers")
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+list("numbers")
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@ -651,13 +651,13 @@ p
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| If your corpus uses the
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| If your corpus uses the
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| #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
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| #[+a("http://universaldependencies.org/docs/format.html") CoNLL-U] format,
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| i.e. files with the extension #[code .conllu], you can use the
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| i.e. files with the extension #[code .conllu], you can use the
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| #[+a("/docs/usage/cli#convert") #[code convert] command] to convert it to
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| #[+a("/docs/api/cli#convert") #[code convert]] command to convert it to
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| spaCy's #[+a("/docs/api/annotation#json-input") JSON format] for training.
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| spaCy's #[+a("/docs/api/annotation#json-input") JSON format] for training.
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| Once you have your UD corpus transformed into JSON, you can train your
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| Once you have your UD corpus transformed into JSON, you can train your
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| model use the using spaCy's
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| model use the using spaCy's
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| #[+a("/docs/usage/cli#train") #[code train] command]:
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| #[+a("/docs/api/cli#train") #[code train]] command:
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+code(false, "bash").
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+code(false, "bash").
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]
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python -m spacy train [lang] [output_dir] [train_data] [dev_data] [--n_iter] [--parser_L1] [--no_tagger] [--no_parser] [--no_ner]
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@ -28,7 +28,7 @@ p
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| and walk you through generating the meta data. You can also create the
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| and walk you through generating the meta data. You can also create the
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| meta.json manually and place it in the model data directory, or supply a
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| meta.json manually and place it in the model data directory, or supply a
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| path to it using the #[code --meta] flag. For more info on this, see the
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| path to it using the #[code --meta] flag. For more info on this, see the
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| #[+a("/docs/usage/cli/#package") #[code package] command] documentation.
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| #[+a("/docs/api/cli#package") #[code package]] command documentation.
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+aside-code("meta.json", "json").
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+aside-code("meta.json", "json").
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{
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{
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@ -77,8 +77,8 @@ p
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p
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| To make the model more convenient to deploy, we recommend wrapping it as
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| To make the model more convenient to deploy, we recommend wrapping it as
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| a Python package, so that you can install it via pip and load it as a
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| a Python package, so that you can install it via pip and load it as a
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| module. spaCy comes with a handy #[+a("/docs/usage/cli#package") CLI command]
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| module. spaCy comes with a handy #[+a("/docs/api/cli#package") #[code package]]
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| to create all required files and directories.
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| CLI command to create all required files and directories.
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+code(false, "bash").
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+code(false, "bash").
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python -m spacy package /home/me/data/en_technology /home/me/my_models
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python -m spacy package /home/me/data/en_technology /home/me/my_models
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