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Typo fixes
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@ -512,7 +512,7 @@ nlp = spacy.load("en_core_web_sm", disable=["parser"])
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spaCy features an extremely fast statistical entity recognition system, that
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assigns labels to contiguous spans of tokens. The default
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[trained pipelines](/models) can indentify a variety of named and numeric
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[trained pipelines](/models) can identify a variety of named and numeric
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entities, including companies, locations, organizations and products. You can
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add arbitrary classes to the entity recognition system, and update the model
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with new examples.
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@ -550,7 +550,7 @@ on a token, it will return an empty string.
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> - `I` – Token is **inside** a multi-token entity.
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> - `L` – Token is the **last** token of a multi-token entity.
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> - `U` – Token is a single-token **unit** entity.
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> - `O` – Toke is **outside** an entity.
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> - `O` – Token is **outside** an entity.
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```python
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### {executable="true"}
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@ -1498,7 +1498,7 @@ that time, the `Doc` will already be tokenized.
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This process of splitting a token requires more settings, because you need to
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specify the text of the individual tokens, optional per-token attributes and how
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the should be attached to the existing syntax tree. This can be done by
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the tokens should be attached to the existing syntax tree. This can be done by
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supplying a list of `heads` – either the token to attach the newly split token
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to, or a `(token, subtoken)` tuple if the newly split token should be attached
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to another subtoken. In this case, "New" should be attached to "York" (the
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