Merge pull request #6257 from walterhenry/develop-proof

A few tiny typo fixes to push through with release of nightly

website/docs/usage/training.md

@@ -863,7 +863,7 @@ The initialization step allows the config to define **all settings** required
 for the pipeline, while keeping a separation between settings and functions that
 should only be used **before training** to set up the initial pipeline, and
 logic and configuration that needs to be available **at runtime**. Without that
-separation, it would be very difficult to use the came, reproducible config file
+separation, it would be very difficult to use the same, reproducible config file
 because the component settings required for training (load data from an external
 file) wouldn't match the component settings required at runtime (load what's
 included with the saved `nlp` object and don't depend on external file).
@@ -958,7 +958,7 @@ data assets, track changes and share your end-to-end processes with your team.
 </Infobox>
 
 The binary `.spacy` format is a serialized [`DocBin`](/api/docbin) containing
-one or more [`Doc`](/api/doc) objects. It's is extremely **efficient in
+one or more [`Doc`](/api/doc) objects. It's extremely **efficient in
 storage**, especially when packing multiple documents together. You can also
 create `Doc` objects manually, so you can write your own custom logic to convert
 and store existing annotations for use in spaCy.
@@ -1300,7 +1300,7 @@ mapping so they know which worker owns which parameter.
 As training proceeds, every worker will be computing gradients for **all** of
 the model parameters. When they compute gradients for parameters they don't own,
 they'll **send them to the worker** that does own that parameter, along with a
-version identifier so that the owner can decide whether the discard the
+version identifier so that the owner can decide whether to discard the
 gradient. Workers use the gradients they receive and the ones they compute
 locally to update the parameters they own, and then broadcast the updated array
 and a new version ID to the other workers.
@@ -1375,7 +1375,7 @@ example = Example.from_dict(doc, {"entities": ["U-ORG", "O", "U-TECHNOLOGY", "O"
 <Infobox title="Migrating from v2.x" variant="warning">
 
 As of v3.0, the [`Example`](/api/example) object replaces the `GoldParse` class.
-It can be constructed in a very similar way, from a `Doc` and a dictionary of
+It can be constructed in a very similar way – from a `Doc` and a dictionary of
 annotations. For more details, see the
 [migration guide](/usage/v3#migrating-training).
 
@@ -1426,7 +1426,7 @@ The [`nlp.update`](/api/language#update) method takes the following arguments:
 | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `examples` | [`Example`](/api/example) objects. The `update` method takes a sequence of them, so you can batch up your training examples.                                           |
 | `drop`     | Dropout rate. Makes it harder for the model to just memorize the data.                                                                                                 |
-| `sgd`      | An [`Optimizer`](https://thinc.ai/docs/api-optimizers) object, which updated the model's weights. If not set, spaCy will create a new one and save it for further use. |
+| `sgd`      | An [`Optimizer`](https://thinc.ai/docs/api-optimizers) object, which updates the model's weights. If not set, spaCy will create a new one and save it for further use. |
 
 <Infobox title="Migrating from v2.x" variant="warning">
 

website/docs/usage/v3.md

@@ -440,7 +440,7 @@ The following methods, attributes and commands are new in spaCy v3.0.
 | [`Language.analyze_pipes`](/api/language#analyze_pipes)                                                                         | [Analyze](/usage/processing-pipelines#analysis) components and their interdependencies.                                                                                                          |
 | [`Language.resume_training`](/api/language#resume_training)                                                                     | Experimental: continue training a trained pipeline and initialize "rehearsal" for components that implement a `rehearse` method to prevent catastrophic forgetting.                              |
 | [`@Language.factory`](/api/language#factory), [`@Language.component`](/api/language#component)                                  | Decorators for [registering](/usage/processing-pipelines#custom-components) pipeline component factories and simple stateless component functions.                                               |
-| [`Language.has_factory`](/api/language#has_factory)                                                                             | Check whether a component factory is registered on a language class.s                                                                                                                            |
+| [`Language.has_factory`](/api/language#has_factory)                                                                             | Check whether a component factory is registered on a language class.                                                                                                                            |
 | [`Language.get_factory_meta`](/api/language#get_factory_meta), [`Language.get_pipe_meta`](/api/language#get_factory_meta)       | Get the [`FactoryMeta`](/api/language#factorymeta) with component metadata for a factory or instance name.                                                                                       |
 | [`Language.config`](/api/language#config)                                                                                       | The [config](/usage/training#config) used to create the current `nlp` object. An instance of [`Config`](https://thinc.ai/docs/api-config#config) and can be saved to disk and used for training. |
 | [`Language.components`](/api/language#attributes), [`Language.component_names`](/api/language#attributes)                       | All available components and component names, including disabled components that are not run as part of the pipeline.                                                                            |
@@ -1032,7 +1032,7 @@ change your names and imports:
 Thanks to everyone who's been contributing to the spaCy ecosystem by developing
 and maintaining one of the many awesome [plugins and extensions](/universe).
 We've tried to make it as easy as possible for you to upgrade your packages for
-spaCy v3. The most common use case for plugins is providing pipeline components
+spaCy v3.0. The most common use case for plugins is providing pipeline components
 and extension attributes. When migrating your plugin, double-check the
 following:
 

website/docs/usage/visualizers.md

@@ -149,7 +149,7 @@ import DisplacyEntCustomHtml from 'images/displacy-ent-custom.html'
 The above example uses a little trick: Since the background color values are
 added as the `background` style attribute, you can use any
 [valid background value](https://tympanus.net/codrops/css_reference/background/)
-or shorthand — including gradients and even images!
+or shorthand – including gradients and even images!
 
 ### Adding titles to documents {#ent-titles}