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Update docs [ci skip]

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This commit is contained in:
Ines Montani 2020-10-09 12:04:52 +02:00
parent 7c52def5da
commit e50dc2c1c9
6 changed files with 17 additions and 11 deletions
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2
website/docs/api/cli.md
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@ -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>

4
website/docs/usage/embeddings-transformers.md
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@ -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

12
website/docs/usage/linguistic-features.md
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@ -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`
arguments on [`spacy.load`](/api/top-level#spacy.load) to enable the senter and
disable the parser.
sentence boundaries and no parser, you can use the `exclude` or `disable`
argument on [`spacy.load`](/api/top-level#spacy.load) to load the pipeline
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 == "...":

3
website/docs/usage/processing-pipelines.md
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@ -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")

2
website/docs/usage/rule-based-matching.md
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@ -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)

5
website/docs/usage/v3.md
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@ -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
in your config and see validation errors if the argument values don't match.
lets you register **custom functions with typed arguments**, reference them in
your config and see validation errors if the argument values don't match.
<Infobox title="Details & Documentation" emoji="📖" list>