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Update adding language / training docs (see #966)
Add data examples and more info on training and CLI commands
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@ -27,9 +27,10 @@ p
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| #[a(href="#brown-clusters") Brown clusters] and
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| #[a(href="#word-vectors") word vectors].
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+item
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| #[strong Set up] a #[a(href="#model-directory") model direcory] and #[strong train] the #[a(href="#train-tagger-parser") tagger and parser].
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p
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| Once you have the tokenizer and vocabulary, you can
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| #[+a("/docs/usage/training") train the tagger, parser and entity recognizer].
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| For some languages, you may also want to develop a solution for
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| lemmatization and morphological analysis.
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@ -406,12 +407,111 @@ p
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| by linear models, while the word vectors are useful for lexical
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| similarity models and deep learning.
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+h(3, "word-frequencies") Word frequencies
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p
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| To generate the word frequencies from a large, raw corpus, you can use the
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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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| 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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| tab-separated word frequencies file with three columns:
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+list("numbers")
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+item The number of times the word occurred in your language sample.
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+item The number of distinct documents the word occurred in.
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+item The word itself.
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p
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| An example word frequencies file could look like this:
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+code("es_word_freqs.txt", "text").
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6361109 111 Aunque
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23598543 111 aunque
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10097056 111 claro
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193454 111 aro
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7711123 111 viene
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12812323 111 mal
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23414636 111 momento
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2014580 111 felicidad
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233865 111 repleto
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15527 111 eto
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235565 111 deliciosos
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17259079 111 buena
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71155 111 Anímate
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37705 111 anímate
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33155 111 cuéntanos
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2389171 111 cuál
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961576 111 típico
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p
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| You should make sure you use the spaCy tokenizer for your
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| language to segment the text for your word frequencies. This will ensure
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| that the frequencies refer to the same segmentation standards you'll be
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| using at run-time. For instance, spaCy's English tokenizer segments
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| "can't" into two tokens. If we segmented the text by whitespace to
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| produce the frequency counts, we'll have incorrect frequency counts for
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| the tokens "ca" and "n't".
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+h(3, "brown-clusters") Training the Brown clusters
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p
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| spaCy's tagger, parser and entity recognizer are designed to use
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| distributional similarity features provided by the
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| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
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| You should train a model with between 500 and 1000 clusters. A minimum
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| frequency threshold of 10 usually works well.
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p
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| An example clusters file could look like this:
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+code("es_clusters.data", "text").
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0000 Vestigial 1
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0000 Vesturland 1
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0000 Veyreau 1
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0000 Veynes 1
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0000 Vexilografía 1
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0000 Vetrigne 1
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0000 Vetónica 1
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0000 Asunden 1
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0000 Villalambrús 1
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0000 Vichuquén 1
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0000 Vichtis 1
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0000 Vichigasta 1
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0000 VAAH 1
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0000 Viciebsk 1
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0000 Vicovaro 1
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0000 Villardeveyo 1
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0000 Vidala 1
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0000 Videoguard 1
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0000 Vedás 1
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0000 Videocomunicado 1
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0000 VideoCrypt 1
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+h(3, "word-vectors") Training the word vectors
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p
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| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
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| algorithms let you train useful word similarity models from unlabelled
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| text. This is a key part of using
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| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
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| labelled data. The vectors are also useful by themselves – they power
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| the #[code .similarity()] methods in spaCy. For best results, you should
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| pre-process the text with spaCy before training the Word2vec model. This
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| ensures your tokenization will match.
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p
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| You can use our
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| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
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| which pre-processes the text with your language-specific tokenizer and
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| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
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| The #[code vectors.bin] file should consist of one word and vector per line.
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+h(2, "model-directory") Setting up a model directory
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p
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| Once you've collected the word frequencies, Brown clusters and word
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| vectors files, you can use the
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| #[+src(gh("spacy-dev-resources", "training/init.py")) init.py]
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| script from our
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| #[+a(gh("spacy-dev-resources")) developer resources], or use the new
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| #[+a("/docs/usage/cli#model") #[code model] command] to create a data
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| directory:
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@ -438,49 +538,20 @@ p
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| loaded. By default, the command expects to be able to find your language
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| class using #[code spacy.util.get_lang_class(lang_id)].
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+h(3, "word-frequencies") Word frequencies
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+h(2, "train-tagger-parser") Training the tagger and parser
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p
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| The #[+a("/docs/usage/cli#model") #[code model] command] expects a
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| tab-separated word frequencies file with three columns:
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+list("numbers")
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+item The number of times the word occurred in your language sample.
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+item The number of distinct documents the word occurred in.
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+item The word itself.
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| You can now train the model using a corpus for your language annotated
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| with #[+a("http://universaldependencies.org/") Universal Dependencies].
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| If your corpus uses the connlu format, you can use the
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| #[+a("/docs/usage/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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p
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| You should make sure you use the spaCy tokenizer for your
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| language to segment the text for your word frequencies. This will ensure
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| that the frequencies refer to the same segmentation standards you'll be
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| using at run-time. For instance, spaCy's English tokenizer segments
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| "can't" into two tokens. If we segmented the text by whitespace to
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| produce the frequency counts, we'll have incorrect frequency counts for
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| the tokens "ca" and "n't".
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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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| #[+a("/docs/usage/cli#train") #[code train] command]:
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+h(3, "brown-clusters") Training the Brown clusters
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p
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| spaCy's tagger, parser and entity recognizer are designed to use
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| distributional similarity features provided by the
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| #[+a("https://github.com/percyliang/brown-cluster") Brown clustering algorithm].
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| You should train a model with between 500 and 1000 clusters. A minimum
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| frequency threshold of 10 usually works well.
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+h(3, "word-vectors") Training the word vectors
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p
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| #[+a("https://en.wikipedia.org/wiki/Word2vec") Word2vec] and related
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| algorithms let you train useful word similarity models from unlabelled
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| text. This is a key part of using
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| #[+a("/docs/usage/deep-learning") deep learning] for NLP with limited
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| labelled data. The vectors are also useful by themselves – they power
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| the #[code .similarity()] methods in spaCy. For best results, you should
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| pre-process the text with spaCy before training the Word2vec model. This
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| ensures your tokenization will match.
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p
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| You can use our
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| #[+src(gh("spacy-dev-resources", "training/word_vectors.py")) word vectors training script],
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| which pre-processes the text with your language-specific tokenizer and
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| trains the model using #[+a("https://radimrehurek.com/gensim/") Gensim].
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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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