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New batch of proofs

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Just tiny fixes to the docs as a proofreader
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walterhenry 2020-10-14 16:37:57 +02:00
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website/docs/usage/101/_named-entities.md
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@ -1,7 +1,7 @@
A named entity is a "real-world object" that's assigned a name – for example, a A named entity is a "real-world object" that's assigned a name – for example, a
person, a country, a product or a book title. spaCy can **recognize various person, a country, a product or a book title. spaCy can **recognize various
types of named entities in a document, by asking the model for a types of named entities in a document, by asking the model for a
**prediction\*\*. Because models are statistical and strongly depend on the prediction**. Because models are statistical and strongly depend on the
examples they were trained on, this doesn't always work _perfectly_ and might examples they were trained on, this doesn't always work _perfectly_ and might
need some tuning later, depending on your use case. need some tuning later, depending on your use case.

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website/docs/usage/101/_tokenization.md
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@ -45,6 +45,6 @@ marks.
While punctuation rules are usually pretty general, tokenizer exceptions While punctuation rules are usually pretty general, tokenizer exceptions
strongly depend on the specifics of the individual language. This is why each strongly depend on the specifics of the individual language. This is why each
[available language](/usage/models#languages) has its own subclass like [available language](/usage/models#languages) has its own subclass, like
`English` or `German`, that loads in lists of hard-coded data and exception `English` or `German`, that loads in lists of hard-coded data and exception
rules. rules.

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website/docs/usage/linguistic-features.md
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@ -641,7 +641,7 @@ print("After", doc.ents) # [London]
#### Setting entity annotations in Cython {#setting-cython} #### Setting entity annotations in Cython {#setting-cython}
Finally, you can always write to the underlying struct, if you compile a Finally, you can always write to the underlying struct if you compile a
[Cython](http://cython.org/) function. This is easy to do, and allows you to [Cython](http://cython.org/) function. This is easy to do, and allows you to
write efficient native code. write efficient native code.
@ -765,15 +765,15 @@ import Tokenization101 from 'usage/101/\_tokenization.md'
<Accordion title="Algorithm details: How spaCy's tokenizer works" id="how-tokenizer-works" spaced> <Accordion title="Algorithm details: How spaCy's tokenizer works" id="how-tokenizer-works" spaced>
spaCy introduces a novel tokenization algorithm, that gives a better balance spaCy introduces a novel tokenization algorithm that gives a better balance
between performance, ease of definition, and ease of alignment into the original between performance, ease of definition and ease of alignment into the original
string. string.
After consuming a prefix or suffix, we consult the special cases again. We want After consuming a prefix or suffix, we consult the special cases again. We want
the special cases to handle things like "don't" in English, and we want the same the special cases to handle things like "don't" in English, and we want the same
rule to work for "(don't)!". We do this by splitting off the open bracket, then rule to work for "(don't)!". We do this by splitting off the open bracket, then
the exclamation, then the close bracket, and finally matching the special case. the exclamation, then the closed bracket, and finally matching the special case.
Here's an implementation of the algorithm in Python, optimized for readability Here's an implementation of the algorithm in Python optimized for readability
rather than performance: rather than performance:
```python ```python
@ -847,7 +847,7 @@ The algorithm can be summarized as follows:
#2. #2.
6. If we can't consume a prefix or a suffix, look for a URL match. 6. If we can't consume a prefix or a suffix, look for a URL match.
7. If there's no URL match, then look for a special case. 7. If there's no URL match, then look for a special case.
8. Look for "infixes" — stuff like hyphens etc. and split the substring into 8. Look for "infixes" – stuff like hyphens etc. and split the substring into
tokens on all infixes. tokens on all infixes.
9. Once we can't consume any more of the string, handle it as a single token. 9. Once we can't consume any more of the string, handle it as a single token.
@ -864,10 +864,10 @@ intact (abbreviations like "U.S.").
<Accordion title="Should I change the language data or add custom tokenizer rules?" id="lang-data-vs-tokenizer"> <Accordion title="Should I change the language data or add custom tokenizer rules?" id="lang-data-vs-tokenizer">
Tokenization rules that are specific to one language, but can be **generalized Tokenization rules that are specific to one language, but can be **generalized
across that language** should ideally live in the language data in across that language**, should ideally live in the language data in
[`spacy/lang`](%%GITHUB_SPACY/spacy/lang) – we always appreciate pull requests! [`spacy/lang`](%%GITHUB_SPACY/spacy/lang) – we always appreciate pull requests!
Anything that's specific to a domain or text type – like financial trading Anything that's specific to a domain or text type – like financial trading
abbreviations, or Bavarian youth slang – should be added as a special case rule abbreviations or Bavarian youth slang – should be added as a special case rule
to your tokenizer instance. If you're dealing with a lot of customizations, it to your tokenizer instance. If you're dealing with a lot of customizations, it
might make sense to create an entirely custom subclass. might make sense to create an entirely custom subclass.
@ -1110,7 +1110,7 @@ tokenized `Doc`.
![The processing pipeline](../images/pipeline.svg) ![The processing pipeline](../images/pipeline.svg)
To overwrite the existing tokenizer, you need to replace `nlp.tokenizer` with a To overwrite the existing tokenizer, you need to replace `nlp.tokenizer` with a
custom function that takes a text, and returns a [`Doc`](/api/doc). custom function that takes a text and returns a [`Doc`](/api/doc).
> #### Creating a Doc > #### Creating a Doc
> >
@ -1229,7 +1229,7 @@ tokenizer** it will be using at runtime. See the docs on
#### Training with custom tokenization {#custom-tokenizer-training new="3"} #### Training with custom tokenization {#custom-tokenizer-training new="3"}
spaCy's [training config](/usage/training#config) describe the settings, spaCy's [training config](/usage/training#config) describes the settings,
hyperparameters, pipeline and tokenizer used for constructing and training the hyperparameters, pipeline and tokenizer used for constructing and training the
pipeline. The `[nlp.tokenizer]` block refers to a **registered function** that pipeline. The `[nlp.tokenizer]` block refers to a **registered function** that
takes the `nlp` object and returns a tokenizer. Here, we're registering a takes the `nlp` object and returns a tokenizer. Here, we're registering a
@ -1465,7 +1465,7 @@ filtered_spans = filter_spans(spans)
The [`retokenizer.split`](/api/doc#retokenizer.split) method allows splitting The [`retokenizer.split`](/api/doc#retokenizer.split) method allows splitting
one token into two or more tokens. This can be useful for cases where one token into two or more tokens. This can be useful for cases where
tokenization rules alone aren't sufficient. For example, you might want to split tokenization rules alone aren't sufficient. For example, you might want to split
"its" into the tokens "it" and "is" — but not the possessive pronoun "its". You "its" into the tokens "it" and "is" – but not the possessive pronoun "its". You
can write rule-based logic that can find only the correct "its" to split, but by can write rule-based logic that can find only the correct "its" to split, but by
that time, the `Doc` will already be tokenized. that time, the `Doc` will already be tokenized.
@ -1513,7 +1513,7 @@ the token indices after splitting.
| `"York"` | `doc[2]` | Attach this token to `doc[1]` in the original `Doc`, i.e. "in". | | `"York"` | `doc[2]` | Attach this token to `doc[1]` in the original `Doc`, i.e. "in". |
If you don't care about the heads (for example, if you're only running the If you don't care about the heads (for example, if you're only running the
tokenizer and not the parser), you can each subtoken to itself: tokenizer and not the parser), you can attach each subtoken to itself:
```python ```python
### {highlight="3"} ### {highlight="3"}
@ -1879,7 +1879,7 @@ assert nlp.vocab.vectors.n_keys > n_vectors # but not the total entries
[`Vocab.prune_vectors`](/api/vocab#prune_vectors) reduces the current vector [`Vocab.prune_vectors`](/api/vocab#prune_vectors) reduces the current vector
table to a given number of unique entries, and returns a dictionary containing table to a given number of unique entries, and returns a dictionary containing
the removed words, mapped to `(string, score)` tuples, where `string` is the the removed words, mapped to `(string, score)` tuples, where `string` is the
entry the removed word was mapped to, and `score` the similarity score between entry the removed word was mapped to and `score` the similarity score between
the two words. the two words.
```python ```python

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website/docs/usage/processing-pipelines.md
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@ -128,7 +128,7 @@ should be created. spaCy will then do the following:
2. Iterate over the **pipeline names** and look up each component name in the 2. Iterate over the **pipeline names** and look up each component name in the
`[components]` block. The `factory` tells spaCy which `[components]` block. The `factory` tells spaCy which
[component factory](#custom-components-factories) to use for adding the [component factory](#custom-components-factories) to use for adding the
component with with [`add_pipe`](/api/language#add_pipe). The settings are component with [`add_pipe`](/api/language#add_pipe). The settings are
passed into the factory. passed into the factory.
3. Make the **model data** available to the `Language` class by calling 3. Make the **model data** available to the `Language` class by calling
[`from_disk`](/api/language#from_disk) with the path to the data directory. [`from_disk`](/api/language#from_disk) with the path to the data directory.
@ -325,7 +325,7 @@ to remove pipeline components from an existing pipeline, the
[`rename_pipe`](/api/language#rename_pipe) method to rename them, or the [`rename_pipe`](/api/language#rename_pipe) method to rename them, or the
[`replace_pipe`](/api/language#replace_pipe) method to replace them with a [`replace_pipe`](/api/language#replace_pipe) method to replace them with a
custom component entirely (more details on this in the section on custom component entirely (more details on this in the section on
[custom components](#custom-components). [custom components](#custom-components)).
```python ```python
nlp.remove_pipe("parser") nlp.remove_pipe("parser")
@ -384,7 +384,7 @@ vectors available – otherwise, it won't be able to make the same predictions.
> >
> Instead of providing a `factory`, component blocks in the training > Instead of providing a `factory`, component blocks in the training
> [config](/usage/training#config) can also define a `source`. The string needs > [config](/usage/training#config) can also define a `source`. The string needs
> to be a loadable spaCy pipeline package or path. The > to be a loadable spaCy pipeline package or path.
> >
> ```ini > ```ini
> [components.ner] > [components.ner]
@ -417,7 +417,7 @@ print(nlp.pipe_names)
### Analyzing pipeline components {#analysis new="3"} ### Analyzing pipeline components {#analysis new="3"}
The [`nlp.analyze_pipes`](/api/language#analyze_pipes) method analyzes the The [`nlp.analyze_pipes`](/api/language#analyze_pipes) method analyzes the
components in the current pipeline and outputs information about them, like the components in the current pipeline and outputs information about them like the
attributes they set on the [`Doc`](/api/doc) and [`Token`](/api/token), whether attributes they set on the [`Doc`](/api/doc) and [`Token`](/api/token), whether
they retokenize the `Doc` and which scores they produce during training. It will they retokenize the `Doc` and which scores they produce during training. It will
also show warnings if components require values that aren't set by previous also show warnings if components require values that aren't set by previous
@ -511,7 +511,7 @@ doesn't, the pipeline analysis won't catch that.
## Creating custom pipeline components {#custom-components} ## Creating custom pipeline components {#custom-components}
A pipeline component is a function that receives a `Doc` object, modifies it and A pipeline component is a function that receives a `Doc` object, modifies it and
returns it – – for example, by using the current weights to make a prediction returns it – for example, by using the current weights to make a prediction
and set some annotation on the document. By adding a component to the pipeline, and set some annotation on the document. By adding a component to the pipeline,
you'll get access to the `Doc` at any point **during processing** – instead of you'll get access to the `Doc` at any point **during processing** – instead of
only being able to modify it afterwards. only being able to modify it afterwards.
@ -702,7 +702,7 @@ nlp.add_pipe("my_component", config={"some_setting": False})
<Accordion title="How is @Language.factory different from @Language.component?" id="factories-decorator-component"> <Accordion title="How is @Language.factory different from @Language.component?" id="factories-decorator-component">
The [`@Language.component`](/api/language#component) decorator is essentially a The [`@Language.component`](/api/language#component) decorator is essentially a
**shortcut** for stateless pipeline component that don't need any settings. This **shortcut** for stateless pipeline components that don't need any settings. This
means you don't have to always write a function that returns your function if means you don't have to always write a function that returns your function if
there's no state to be passed through – spaCy can just take care of this for there's no state to be passed through – spaCy can just take care of this for
you. The following two code examples are equivalent: you. The following two code examples are equivalent:
@ -888,7 +888,7 @@ components in pipelines that you [train](/usage/training). To make sure spaCy
knows where to find your custom `@misc` function, you can pass in a Python file knows where to find your custom `@misc` function, you can pass in a Python file
via the argument `--code`. If someone else is using your component, all they via the argument `--code`. If someone else is using your component, all they
have to do to customize the data is to register their own function and swap out have to do to customize the data is to register their own function and swap out
the name. Registered functions can also take **arguments** by the way that can the name. Registered functions can also take **arguments**, by the way, that can
be defined in the config as well – you can read more about this in the docs on be defined in the config as well – you can read more about this in the docs on
[training with custom code](/usage/training#custom-code). [training with custom code](/usage/training#custom-code).
@ -963,7 +963,7 @@ doc = nlp("This is a text...")
### Language-specific factories {#factories-language new="3"} ### Language-specific factories {#factories-language new="3"}
There are many use case where you might want your pipeline components to be There are many use cases where you might want your pipeline components to be
language-specific. Sometimes this requires entirely different implementation per language-specific. Sometimes this requires entirely different implementation per
language, sometimes the only difference is in the settings or data. spaCy allows language, sometimes the only difference is in the settings or data. spaCy allows
you to register factories of the **same name** on both the `Language` base you to register factories of the **same name** on both the `Language` base
@ -1028,8 +1028,8 @@ plug fully custom machine learning components into your pipeline. You'll need
the following: the following:
1. **Model:** A Thinc [`Model`](https://thinc.ai/docs/api-model) instance. This 1. **Model:** A Thinc [`Model`](https://thinc.ai/docs/api-model) instance. This
can be a model using implemented in can be a model implemented in
[Thinc](/usage/layers-architectures#thinc), or a [Thinc](/usage/layers-architectures#thinc) or a
[wrapped model](/usage/layers-architectures#frameworks) implemented in [wrapped model](/usage/layers-architectures#frameworks) implemented in
PyTorch, TensorFlow, MXNet or a fully custom solution. The model must take a PyTorch, TensorFlow, MXNet or a fully custom solution. The model must take a
list of [`Doc`](/api/doc) objects as input and can have any type of output. list of [`Doc`](/api/doc) objects as input and can have any type of output.
@ -1354,7 +1354,7 @@ to `Doc.user_span_hooks` and `Doc.user_token_hooks`.
> >
> The hooks live on the `Doc` object because the `Span` and `Token` objects are > The hooks live on the `Doc` object because the `Span` and `Token` objects are
> created lazily, and don't own any data. They just proxy to their parent `Doc`. > created lazily, and don't own any data. They just proxy to their parent `Doc`.
> This turns out to be convenient here — we only have to worry about installing > This turns out to be convenient here – we only have to worry about installing
> hooks in one place. > hooks in one place.
| Name | Customizes | | Name | Customizes |

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website/docs/usage/projects.md
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@ -73,7 +73,7 @@ python -m spacy project clone some_example_project
By default, the project will be cloned into the current working directory. You By default, the project will be cloned into the current working directory. You
can specify an optional second argument to define the output directory. The can specify an optional second argument to define the output directory. The
`--repo` option lets you define a custom repo to clone from, if you don't want `--repo` option lets you define a custom repo to clone from if you don't want
to use the spaCy [`projects`](https://github.com/explosion/projects) repo. You to use the spaCy [`projects`](https://github.com/explosion/projects) repo. You
can also use any private repo you have access to with Git. can also use any private repo you have access to with Git.
@ -109,7 +109,7 @@ $ python -m spacy project assets
Asset URLs can be a number of different protocols: HTTP, HTTPS, FTP, SSH, and Asset URLs can be a number of different protocols: HTTP, HTTPS, FTP, SSH, and
even cloud storage such as GCS and S3. You can also fetch assets using git, by even cloud storage such as GCS and S3. You can also fetch assets using git, by
replacing the `url` string with a `git` block. spaCy will use Git's "sparse replacing the `url` string with a `git` block. spaCy will use Git's "sparse
checkout" feature, to avoid download the whole repository. checkout" feature to avoid downloading the whole repository.
### 3. Run a command {#run} ### 3. Run a command {#run}
@ -201,7 +201,7 @@ $ python -m spacy project push
``` ```
The `remotes` section in your `project.yml` lets you assign names to the The `remotes` section in your `project.yml` lets you assign names to the
different storages. To download state from a remote storage, you can use the different storages. To download a state from a remote storage, you can use the
[`spacy project pull`](/api/cli#project-pull) command. For more details, see the [`spacy project pull`](/api/cli#project-pull) command. For more details, see the
docs on [remote storage](#remote). docs on [remote storage](#remote).
@ -315,7 +315,7 @@ company-internal and not available over the internet. In that case, you can
specify the destination paths and a checksum, and leave out the URL. When your specify the destination paths and a checksum, and leave out the URL. When your
teammates clone and run your project, they can place the files in the respective teammates clone and run your project, they can place the files in the respective
directory themselves. The [`project assets`](/api/cli#project-assets) command directory themselves. The [`project assets`](/api/cli#project-assets) command
will alert about missing files and mismatched checksums, so you can ensure that will alert you about missing files and mismatched checksums, so you can ensure that
others are running your project with the same data. others are running your project with the same data.
### Dependencies and outputs {#deps-outputs} ### Dependencies and outputs {#deps-outputs}
@ -363,8 +363,7 @@ graphs based on the dependencies and outputs, and won't re-run previous steps
automatically. For instance, if you only run the command `train` that depends on automatically. For instance, if you only run the command `train` that depends on
data created by `preprocess` and those files are missing, spaCy will show an data created by `preprocess` and those files are missing, spaCy will show an
error – it won't just re-run `preprocess`. If you're looking for more advanced error – it won't just re-run `preprocess`. If you're looking for more advanced
data management, check out the [Data Version Control (DVC) integration](#dvc) data management, check out the [Data Version Control (DVC) integration](#dvc). If you're planning on integrating your spaCy project with DVC, you
integration. If you're planning on integrating your spaCy project with DVC, you
can also use `outputs_no_cache` instead of `outputs` to define outputs that can also use `outputs_no_cache` instead of `outputs` to define outputs that
won't be cached or tracked. won't be cached or tracked.
@ -508,7 +507,7 @@ commands:
When your custom project is ready and you want to share it with others, you can When your custom project is ready and you want to share it with others, you can
use the [`spacy project document`](/api/cli#project-document) command to use the [`spacy project document`](/api/cli#project-document) command to
**auto-generate** a pretty, Markdown-formatted `README` file based on your **auto-generate** a pretty, markdown-formatted `README` file based on your
project's `project.yml`. It will list all commands, workflows and assets defined project's `project.yml`. It will list all commands, workflows and assets defined
in the project and include details on how to run the project, as well as links in the project and include details on how to run the project, as well as links
to the relevant spaCy documentation to make it easy for others to get started to the relevant spaCy documentation to make it easy for others to get started

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website/docs/usage/rule-based-matching.md
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@ -55,7 +55,7 @@ abstract representations of the tokens you're looking for, using lexical
attributes, linguistic features predicted by the model, operators, set attributes, linguistic features predicted by the model, operators, set
membership and rich comparison. For example, you can find a noun, followed by a membership and rich comparison. For example, you can find a noun, followed by a
verb with the lemma "love" or "like", followed by an optional determiner and verb with the lemma "love" or "like", followed by an optional determiner and
another token that's at least ten characters long. another token that's at least 10 characters long.
</Accordion> </Accordion>
@ -491,7 +491,7 @@ you prefer.
| `matcher` | The matcher instance. ~~Matcher~~ | | `matcher` | The matcher instance. ~~Matcher~~ |
| `doc` | The document the matcher was used on. ~~Doc~~ | | `doc` | The document the matcher was used on. ~~Doc~~ |
| `i` | Index of the current match (`matches[i`]). ~~int~~ | | `i` | Index of the current match (`matches[i`]). ~~int~~ |
| `matches` | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. ~~ List[Tuple[int, int int]]~~ | | `matches` | A list of `(match_id, start, end)` tuples, describing the matches. A match tuple describes a span `doc[start:end`]. ~~List[Tuple[int, int int]]~~ |
### Creating spans from matches {#matcher-spans} ### Creating spans from matches {#matcher-spans}
@ -628,7 +628,7 @@ To get a quick overview of the results, you could collect all sentences
containing a match and render them with the containing a match and render them with the
[displaCy visualizer](/usage/visualizers). In the callback function, you'll have [displaCy visualizer](/usage/visualizers). In the callback function, you'll have
access to the `start` and `end` of each match, as well as the parent `Doc`. This access to the `start` and `end` of each match, as well as the parent `Doc`. This
lets you determine the sentence containing the match, `doc[start : end`.sent], lets you determine the sentence containing the match, `doc[start:end].sent`,
and calculate the start and end of the matched span within the sentence. Using and calculate the start and end of the matched span within the sentence. Using
displaCy in ["manual" mode](/usage/visualizers#manual-usage) lets you pass in a displaCy in ["manual" mode](/usage/visualizers#manual-usage) lets you pass in a
list of dictionaries containing the text and entities to render. list of dictionaries containing the text and entities to render.
@ -1451,7 +1451,7 @@ When using a trained
extract information from your texts, you may find that the predicted span only extract information from your texts, you may find that the predicted span only
includes parts of the entity you're looking for. Sometimes, this happens if includes parts of the entity you're looking for. Sometimes, this happens if
statistical model predicts entities incorrectly. Other times, it happens if the statistical model predicts entities incorrectly. Other times, it happens if the
way the entity type way defined in the original training corpus doesn't match way the entity type was defined in the original training corpus doesn't match
what you need for your application. what you need for your application.
> #### Where corpora come from > #### Where corpora come from
@ -1642,7 +1642,7 @@ affiliation is current, we can check the head's part-of-speech tag.
```python ```python
person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"] person_entities = [ent for ent in doc.ents if ent.label_ == "PERSON"]
for ent in person_entities: for ent in person_entities:
# Because the entity is a spans, we need to use its root token. The head # Because the entity is a span, we need to use its root token. The head
# is the syntactic governor of the person, e.g. the verb # is the syntactic governor of the person, e.g. the verb
head = ent.root.head head = ent.root.head
if head.lemma_ == "work": if head.lemma_ == "work":

4
website/docs/usage/saving-loading.md
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@ -448,7 +448,7 @@ entry_points={
} }
``` ```
The factory can also implement other pipeline component like `to_disk` and The factory can also implement other pipeline components like `to_disk` and
`from_disk` for serialization, or even `update` to make the component trainable. `from_disk` for serialization, or even `update` to make the component trainable.
If a component exposes a `from_disk` method and is included in a pipeline, spaCy If a component exposes a `from_disk` method and is included in a pipeline, spaCy
will call it on load. This lets you ship custom data with your pipeline package. will call it on load. This lets you ship custom data with your pipeline package.
@ -666,7 +666,7 @@ care of putting all this together and returning a `Language` object with the
loaded pipeline and data. If your pipeline requires loaded pipeline and data. If your pipeline requires
[custom components](/usage/processing-pipelines#custom-components) or a custom [custom components](/usage/processing-pipelines#custom-components) or a custom
language class, you can also **ship the code with your package** and include it language class, you can also **ship the code with your package** and include it
in the `__init__.py` – for example, to register component before the `nlp` in the `__init__.py` – for example, to register a component before the `nlp`
object is created. object is created.
<Infobox variant="warning" title="Important note on making manual edits"> <Infobox variant="warning" title="Important note on making manual edits">

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website/docs/usage/training.md
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@ -489,7 +489,7 @@ or TensorFlow, make **custom modifications** to the `nlp` object, create custom
optimizers or schedules, or **stream in data** and preprocesses it on the fly optimizers or schedules, or **stream in data** and preprocesses it on the fly
while training. while training.
Each custom function can have any numbers of arguments that are passed in via Each custom function can have any number of arguments that are passed in via
the [config](#config), just the built-in functions. If your function defines the [config](#config), just the built-in functions. If your function defines
**default argument values**, spaCy is able to auto-fill your config when you run **default argument values**, spaCy is able to auto-fill your config when you run
[`init fill-config`](/api/cli#init-fill-config). If you want to make sure that a [`init fill-config`](/api/cli#init-fill-config). If you want to make sure that a