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Update docs for pipeline initialize() methods (#11221)

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* Update documentation for dependency parser

* Update documentation for trainable_lemmatizer

* Update documentation for entity_linker

* Update documentation for ner

* Update documentation for morphologizer

* Update documentation for senter

* Update documentation for spancat

* Update documentation for tagger

* Update documentation for textcat

* Update documentation for tok2vec

* Run prettier on edited files

* Apply similar changes in transformer docs

* Remove need to say annotated example explicitly

I removed the need to say "Must contain at least one annotated Example"
because it's often a given that Examples will contain some gold-standard
annotation.

* Run prettier on transformer docs
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12
website/docs/api/dependencyparser.md
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@ -158,10 +158,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
## DependencyParser.initialize {#initialize tag="method" new="3"} ## DependencyParser.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -179,7 +179,7 @@ This method was previously called `begin_training`.
> >
> ```python > ```python
> parser = nlp.add_pipe("parser") > parser = nlp.add_pipe("parser")
> parser.initialize(lambda: [], nlp=nlp) > parser.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -193,7 +193,7 @@ This method was previously called `begin_training`.
| Name | Description | | Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ |

12
website/docs/api/edittreelemmatizer.md
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@ -141,10 +141,10 @@ and [`pipe`](/api/edittreelemmatizer#pipe) delegate to the
## EditTreeLemmatizer.initialize {#initialize tag="method" new="3"} ## EditTreeLemmatizer.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -156,7 +156,7 @@ config.
> >
> ```python > ```python
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer") > lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
> lemmatizer.initialize(lambda: [], nlp=nlp) > lemmatizer.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -170,7 +170,7 @@ config.
| Name | Description | | Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |

22
website/docs/api/entitylinker.md
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@ -185,10 +185,10 @@ with the current vocab.
## EntityLinker.initialize {#initialize tag="method" new="3"} ## EntityLinker.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize). by [`Language.initialize`](/api/language#initialize).
@ -208,15 +208,15 @@ This method was previously called `begin_training`.
> >
> ```python > ```python
> entity_linker = nlp.add_pipe("entity_linker") > entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.initialize(lambda: [], nlp=nlp, kb_loader=my_kb) > entity_linker.initialize(lambda: examples, nlp=nlp, kb_loader=my_kb)
> ``` > ```
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ | | `kb_loader` | Function that creates a [`KnowledgeBase`](/api/kb) from a `Vocab` instance. ~~Callable[[Vocab], KnowledgeBase]~~ |
## EntityLinker.predict {#predict tag="method"} ## EntityLinker.predict {#predict tag="method"}

12
website/docs/api/entityrecognizer.md
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@ -154,10 +154,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
## EntityRecognizer.initialize {#initialize tag="method" new="3"} ## EntityRecognizer.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -175,7 +175,7 @@ This method was previously called `begin_training`.
> >
> ```python > ```python
> ner = nlp.add_pipe("ner") > ner = nlp.add_pipe("ner")
> ner.initialize(lambda: [], nlp=nlp) > ner.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -189,7 +189,7 @@ This method was previously called `begin_training`.
| Name | Description | | Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Dict[str, Dict[str, int]]]~~ |

12
website/docs/api/morphologizer.md
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@ -147,10 +147,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and
## Morphologizer.initialize {#initialize tag="method"} ## Morphologizer.initialize {#initialize tag="method"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -162,7 +162,7 @@ config.
> >
> ```python > ```python
> morphologizer = nlp.add_pipe("morphologizer") > morphologizer = nlp.add_pipe("morphologizer")
> morphologizer.initialize(lambda: [], nlp=nlp) > morphologizer.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -176,7 +176,7 @@ config.
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[dict]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[dict]~~ |

20
website/docs/api/sentencerecognizer.md
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@ -132,10 +132,10 @@ and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
## SentenceRecognizer.initialize {#initialize tag="method"} ## SentenceRecognizer.initialize {#initialize tag="method"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize). by [`Language.initialize`](/api/language#initialize).
@ -144,14 +144,14 @@ by [`Language.initialize`](/api/language#initialize).
> >
> ```python > ```python
> senter = nlp.add_pipe("senter") > senter = nlp.add_pipe("senter")
> senter.initialize(lambda: [], nlp=nlp) > senter.initialize(lambda: examples, nlp=nlp)
> ``` > ```
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## SentenceRecognizer.predict {#predict tag="method"} ## SentenceRecognizer.predict {#predict tag="method"}

16
website/docs/api/spancategorizer.md
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@ -56,7 +56,7 @@ architectures and their arguments and hyperparameters.
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ | | `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
| `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ | | `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ | | `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ | | `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ | | `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ | | `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
@ -93,7 +93,7 @@ shortcut for this and instantiate the component using its string name and
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ | | `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ | | `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `spans_key` | Key of the [`Doc.spans`](/api/doc#sans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ | | `spans_key` | Key of the [`Doc.spans`](/api/doc#sans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ | | `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ | | `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
@ -147,10 +147,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/spancategorizer#call) and
## SpanCategorizer.initialize {#initialize tag="method"} ## SpanCategorizer.initialize {#initialize tag="method"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -162,7 +162,7 @@ config.
> >
> ```python > ```python
> spancat = nlp.add_pipe("spancat") > spancat = nlp.add_pipe("spancat")
> spancat.initialize(lambda: [], nlp=nlp) > spancat.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -176,7 +176,7 @@ config.
| Name | Description | | Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |

12
website/docs/api/tagger.md
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@ -130,10 +130,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/tagger#call) and
## Tagger.initialize {#initialize tag="method" new="3"} ## Tagger.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -151,7 +151,7 @@ This method was previously called `begin_training`.
> >
> ```python > ```python
> tagger = nlp.add_pipe("tagger") > tagger = nlp.add_pipe("tagger")
> tagger.initialize(lambda: [], nlp=nlp) > tagger.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -165,7 +165,7 @@ This method was previously called `begin_training`.
| Name | Description | | Name | Description |
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |

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@ -176,10 +176,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
## TextCategorizer.initialize {#initialize tag="method" new="3"} ## TextCategorizer.initialize {#initialize tag="method" new="3"}
Initialize the component for training. `get_examples` should be a function that Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are returns an iterable of [`Example`](/api/example) objects. **At least one example
used to **initialize the model** of the component and can either be the full should be supplied.** The data examples are used to **initialize the model** of
training data or a representative sample. Initialization includes validating the the component and can either be the full training data or a representative
network, sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize) and lets you customize by [`Language.initialize`](/api/language#initialize) and lets you customize
@ -197,7 +197,7 @@ This method was previously called `begin_training`.
> >
> ```python > ```python
> textcat = nlp.add_pipe("textcat") > textcat = nlp.add_pipe("textcat")
> textcat.initialize(lambda: [], nlp=nlp) > textcat.initialize(lambda: examples, nlp=nlp)
> ``` > ```
> >
> ```ini > ```ini
@ -212,7 +212,7 @@ This method was previously called `begin_training`.
| Name | Description | | Name | Description |
| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ | | `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[Iterable[str]]~~ |

20
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@ -127,10 +127,10 @@ and [`set_annotations`](/api/tok2vec#set_annotations) methods.
Initialize the component for training and return an Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a [`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data function that returns an iterable of [`Example`](/api/example) objects. **At
examples are used to **initialize the model** of the component and can either be least one example should be supplied.** The data examples are used to
the full training data or a representative sample. Initialization includes **initialize the model** of the component and can either be the full training
validating the network, data or a representative sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize). by [`Language.initialize`](/api/language#initialize).
@ -139,14 +139,14 @@ by [`Language.initialize`](/api/language#initialize).
> >
> ```python > ```python
> tok2vec = nlp.add_pipe("tok2vec") > tok2vec = nlp.add_pipe("tok2vec")
> tok2vec.initialize(lambda: [], nlp=nlp) > tok2vec.initialize(lambda: examples, nlp=nlp)
> ``` > ```
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Tok2Vec.predict {#predict tag="method"} ## Tok2Vec.predict {#predict tag="method"}

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@ -175,10 +175,10 @@ applied to the `Doc` in order. Both [`__call__`](/api/transformer#call) and
Initialize the component for training and return an Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a [`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data function that returns an iterable of [`Example`](/api/example) objects. **At
examples are used to **initialize the model** of the component and can either be least one example should be supplied.** The data examples are used to
the full training data or a representative sample. Initialization includes **initialize the model** of the component and can either be the full training
validating the network, data or a representative sample. Initialization includes validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and [inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data. This method is typically called setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize). by [`Language.initialize`](/api/language#initialize).
@ -187,14 +187,14 @@ by [`Language.initialize`](/api/language#initialize).
> >
> ```python > ```python
> trf = nlp.add_pipe("transformer") > trf = nlp.add_pipe("transformer")
> trf.initialize(lambda: iter([]), nlp=nlp) > trf.initialize(lambda: examples, nlp=nlp)
> ``` > ```
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ | | `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | | | _keyword-only_ | |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ | | `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Transformer.predict {#predict tag="method"} ## Transformer.predict {#predict tag="method"}