Update docs [ci skip]

website/docs/api/cli.md

@@ -643,7 +643,7 @@ Debug a Thinc [`Model`](https://thinc.ai/docs/api-model) by running it on a
 sample text and checking how it updates its internal weights and parameters.
 
 ```cli
-$ python -m spacy debug model [config_path] [component] [--layers] [-DIM] [-PAR] [-GRAD] [-ATTR] [-P0] [-P1] [-P2] [P3] [--gpu-id]
+$ python -m spacy debug model [config_path] [component] [--layers] [--dimensions] [--parameters] [--gradients] [--attributes] [--print-step0] [--print-step1] [--print-step2] [--print-step3] [--gpu-id]
 ```
 
 <Accordion title="Example outputs" spaced>

website/docs/usage/embeddings-transformers.md

@@ -232,7 +232,9 @@ transformers as subnetworks directly, you can also use them via the
 
 The `Transformer` component sets the
 [`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
-which lets you access the transformers outputs at runtime.
+which lets you access the transformers outputs at runtime. The trained
+transformer-based [pipelines](/models) provided by spaCy end on `_trf`, e.g.
+[`en_core_web_trf`](/models/en#en_core_web_trf).
 
 ```cli
 $ python -m spacy download en_core_web_trf

website/docs/usage/linguistic-features.md

@@ -1656,9 +1656,10 @@ because it only requires annotated sentence boundaries rather than full
 dependency parses. spaCy's [trained pipelines](/models) include both a parser
 and a trained sentence segmenter, which is
 [disabled](/usage/processing-pipelines#disabling) by default. If you only need
-sentence boundaries and no parser, you can use the `enable` and `disable`
+sentence boundaries and no parser, you can use the `exclude` or `disable`
-arguments on [`spacy.load`](/api/top-level#spacy.load) to enable the senter and
+argument on [`spacy.load`](/api/top-level#spacy.load) to load the pipeline
-disable the parser.
+without the parser and then enable the sentence recognizer explicitly with
+[`nlp.enable_pipe`](/api/language#enable_pipe).
 
 > #### senter vs. parser
 >
@@ -1670,7 +1671,8 @@ disable the parser.
 ### {executable="true"}
 import spacy
 
-nlp = spacy.load("en_core_web_sm", enable=["senter"], disable=["parser"])
+nlp = spacy.load("en_core_web_sm", exclude=["parser"])
+nlp.enable_pipe("senter")
 doc = nlp("This is a sentence. This is another sentence.")
 for sent in doc.sents:
     print(sent.text)
@@ -1734,7 +1736,7 @@ nlp = spacy.load("en_core_web_sm")
 doc = nlp(text)
 print("Before:", [sent.text for sent in doc.sents])
 
-@Language.component("set_custom_coundaries")
+@Language.component("set_custom_boundaries")
 def set_custom_boundaries(doc):
     for token in doc[:-1]:
         if token.text == "...":

website/docs/usage/processing-pipelines.md

@@ -1159,7 +1159,8 @@ class DebugComponent:
         self.logger.info(f"Pipeline: {nlp.pipe_names}")
 
     def __call__(self, doc: Doc) -> Doc:
-        self.logger.debug(f"Doc: {len(doc)} tokens, is_tagged: {doc.is_tagged}")
+        is_tagged = doc.has_annotation("TAG")
+        self.logger.debug(f"Doc: {len(doc)} tokens, is tagged: {is_tagged}")
         return doc
 
 nlp = spacy.load("en_core_web_sm")

website/docs/usage/rule-based-matching.md

@@ -838,7 +838,7 @@ nlp = spacy.load("en_core_web_sm")
 matcher = Matcher(nlp.vocab)
 
 # Add pattern for valid hashtag, i.e. '#' plus any ASCII token
-matcher.add("HASHTAG", None, [{"ORTH": "#"}, {"IS_ASCII": True}])
+matcher.add("HASHTAG", [[{"ORTH": "#"}, {"IS_ASCII": True}]])
 
 # Register token extension
 Token.set_extension("is_hashtag", default=False)

website/docs/usage/v3.md

@@ -285,6 +285,7 @@ add to your pipeline and customize for your use case:
 | [`Lemmatizer`](/api/lemmatizer)                 | Standalone component for rule-based and lookup lemmatization.                                                                                                                                                           |
 | [`AttributeRuler`](/api/attributeruler)         | Component for setting token attributes using match patterns.                                                                                                                                                            |
 | [`Transformer`](/api/transformer)               | Component for using [transformer models](/usage/embeddings-transformers) in your pipeline, accessing outputs and aligning tokens. Provided via [`spacy-transformers`](https://github.com/explosion/spacy-transformers). |
+| [`TrainablePipe`](/api/pipe)                    | Base class for trainable pipeline components.                                                                                                                                                                           |
 
 <Infobox title="Details & Documentation" emoji="📖" list>
 
@@ -396,8 +397,8 @@ type-check model definitions.
 For data validation, spaCy v3.0 adopts
 [`pydantic`](https://github.com/samuelcolvin/pydantic). It also powers the data
 validation of Thinc's [config system](https://thinc.ai/docs/usage-config), which
-lets you register **custom functions with typed arguments**, reference them
+lets you register **custom functions with typed arguments**, reference them in
-in your config and see validation errors if the argument values don't match.
+your config and see validation errors if the argument values don't match.
 
 <Infobox title="Details & Documentation" emoji="📖" list>