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Fix broken links in docs
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@ -6,7 +6,7 @@
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+h(2, "matcher", "https://github.com/" + SOCIAL.github + "/spaCy/blob/master/spacy/matcher.pyx")
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+h(2, "matcher", "https://github.com/" + SOCIAL.github + "/spaCy/blob/master/spacy/matcher.pyx")
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| #[+tag class] Matcher
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| #[+tag class] Matcher
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p A full example can be found #[a(href="https://github.com/" + SOCIAL.github + "blob/master/examples/matcher_example.py") here].
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p A full example can be found #[a(href="https://github.com/" + SOCIAL.github + "/spaCy/blob/master/examples/matcher_example.py") here].
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+table(["Usage", "Description"])
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+table(["Usage", "Description"])
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+row
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+row
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@ -19,7 +19,7 @@ p I'll start with some quick code examples, that describe how to train each mode
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tagger.model.end_training()
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tagger.model.end_training()
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/examples/training/train_tagger.py") Full example]
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/blob/master/examples/training/train_tagger.py") Full example]
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+h(2, "train-entity") Training the named entity recognizer
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+h(2, "train-entity") Training the named entity recognizer
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@ -37,7 +37,7 @@ p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/examples/training/train_t
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entity.model.end_training()
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entity.model.end_training()
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/examples/training/train_ner.y") Full example]
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/blob/master/examples/training/train_ner.y") Full example]
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+h(2, "train-entity") Training the dependency parser
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+h(2, "train-entity") Training the dependency parser
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@ -54,7 +54,7 @@ p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/examples/training/train_n
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parser.model.end_training()
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parser.model.end_training()
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/examples/training/train_parser.py") Full example]
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p #[+a("https://github.com/" + SOCIAL.github + "/spaCy/blob/master/examples/training/train_parser.py") Full example]
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+h(2, 'feature-templates') Customising the feature extraction
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+h(2, 'feature-templates') Customising the feature extraction
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