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35
website/docs/api/data-formats.md
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@ -190,8 +190,6 @@ process that are used when you run [`spacy train`](/api/cli#train).
| `eval_frequency` | How often to evaluate during training (steps). Defaults to `200`. ~~int~~ |
| `frozen_components` | Pipeline component names that are "frozen" and shouldn't be updated during training. See [here](/usage/training#config-components) for details. Defaults to `[]`. ~~List[str]~~ |
| `gpu_allocator` | Library for cupy to route GPU memory allocation to. Can be `"pytorch"` or `"tensorflow"`. Defaults to variable `${system.gpu_allocator}`. ~~str~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. ~~Optional[str]~~ |
| `lookups` | Additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `null`. ~~Optional[Lookups]~~ |
| `max_epochs` | Maximum number of epochs to train for. Defaults to `0`. ~~int~~ |
| `max_steps` | Maximum number of update steps to train for. Defaults to `20000`. ~~int~~ |
| `optimizer` | The optimizer. The learning rate schedule and other settings can be configured as part of the optimizer. Defaults to [`Adam`](https://thinc.ai/docs/api-optimizers#adam). ~~Optimizer~~ |
@ -200,7 +198,6 @@ process that are used when you run [`spacy train`](/api/cli#train).
| `score_weights` | Score names shown in metrics mapped to their weight towards the final weighted score. See [here](/usage/training#metrics) for details. Defaults to `{}`. ~~Dict[str, float]~~ |
| `seed` | The random seed. Defaults to variable `${system.seed}`. ~~int~~ |
| `train_corpus` | Dot notation of the config location defining the train corpus. Defaults to `corpora.train`. ~~str~~ |
| `vectors` | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vocab`](/api/cli#init-vocab). Defaults to `null`. ~~Optional[str]~~ |
### pretraining {#config-pretraining tag="section,optional"}
@ -220,6 +217,38 @@ used when you run [`spacy pretrain`](/api/cli#pretrain).
| `component` | Component to find the layer to pretrain. Defaults to `"tok2vec"`. ~~str~~ |
| `layer` | The layer to pretrain. If empty, the whole component model will be used. ~~str~~ |
### initialize {#config-initialize tag="section"}
This config block lets you define resources for **initializing the pipeline**.
It's used by [`Language.initialize`](/api/language#initialize) and typically
called right before training (but not at runtime). The section allows you to
specify local file paths or custom functions to load data resources from,
without requiring them at runtime when you load the trained pipeline back in.
> #### Example
>
> ```ini
> [initialize]
> vectors = "/path/to/vectors_nlp"
> init_tok2vec = "/path/to/pretrain.bin"
>
> [initialize_components]
>
> [initialize.components.my_component]
> data_path = "/path/to/component_data"
> ```
<!-- TODO: -->
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `components` | Additional arguments passed to the `initialize` method of a pipeline component, keyed by component name. If type annotations are available on the method, the config will be validated against them. The `initialize` methods will always receive the `get_examples` callback and the current `nlp` object. ~~Dict[str, Dict[str, Any]]~~ |
| `init_tok2vec` | Optional path to pretrained tok2vec weights created with [`spacy pretrain`](/api/cli#pretrain). Defaults to variable `${paths.init_tok2vec}`. ~~Optional[str]~~ |
| `lookups` | Additional lexeme and vocab data from [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). Defaults to `null`. ~~Optional[Lookups]~~ |
| `tokenizer` | Additional arguments passed to the `initialize` method of the specified tokenizer. Can be used for languages like Chinese that depend on dictionaries or trained models for tokenization. If type annotations are available on the method, the config will be validated against them. The `initialize` method will always receive the `get_examples` callback and the current `nlp` object. ~~Dict[str, Any]~~ |
| `vectors` | Name or path of pipeline containing pretrained word vectors to use, e.g. created with [`init vocab`](/api/cli#init-vocab). Defaults to `null`. ~~Optional[str]~~ |
| `vocab_data` | Path to JSONL-formatted [vocabulary file](/api/data-formats#vocab-jsonl) to initialize vocabulary. ~~Optional[str]~~ |
## Training data {#training}
### Binary training format {#binary-training new="3"}

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@ -142,14 +142,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
## DependencyParser.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -161,16 +161,14 @@ This method was previously called `begin_training`.
>
> ```python
> parser = nlp.add_pipe("parser")
> optimizer = parser.initialize(lambda: [], pipeline=nlp.pipeline)
> parser.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## DependencyParser.predict {#predict tag="method"}

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@ -141,14 +141,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
## EntityLinker.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -159,17 +159,15 @@ This method was previously called `begin_training`.
> #### Example
>
> ```python
> entity_linker = nlp.add_pipe("entity_linker", last=True)
> optimizer = entity_linker.initialize(lambda: [], pipeline=nlp.pipeline)
> entity_linker = nlp.add_pipe("entity_linker")
> entity_linker.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## EntityLinker.predict {#predict tag="method"}

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@ -131,14 +131,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
## EntityRecognizer.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -150,16 +150,14 @@ This method was previously called `begin_training`.
>
> ```python
> ner = nlp.add_pipe("ner")
> optimizer = ner.initialize(lambda: [], pipeline=nlp.pipeline)
> ner.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## EntityRecognizer.predict {#predict tag="method"}

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@ -204,12 +204,19 @@ more efficient than processing texts one-by-one.
## Language.initialize {#initialize tag="method"}
Initialize the pipeline for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples can either be the full training data or a representative sample. They
are used to **initialize the models** of trainable pipeline components and are
passed each component's [`initialize`](/api/pipe#initialize) method, if
available. Initialization includes validating the network,
[`Optimizer`](https://thinc.ai/docs/api-optimizers). Under the hood, it uses the
settings defined in the [`[initialize]`](/api/data-formats#config-initialize)
config block to set up the vocabulary, load in vectors and tok2vec weights and
pass optional arguments to the `initialize` methods implemented by pipeline
components or the tokenizer. This method is typically called automatically when
you run [`spacy train`](/api/cli#train).
`get_examples` should be a function that returns an iterable of
[`Example`](/api/example) objects. The data examples can either be the full
training data or a representative sample. They are used to **initialize the
models** of trainable pipeline components and are passed each component's
[`initialize`](/api/pipe#initialize) method, if available. Initialization
includes validating the network,
[inferring missing shapes](/usage/layers-architectures#thinc-shape-inference)
and setting up the label scheme based on the data.

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@ -119,30 +119,27 @@ applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and
## Morphologizer.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
> #### Example
>
> ```python
> morphologizer = nlp.add_pipe("morphologizer")
> nlp.pipeline.append(morphologizer)
> optimizer = morphologizer.initialize(lambda: [], pipeline=nlp.pipeline)
> morphologizer.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Morphologizer.predict {#predict tag="method"}

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@ -100,14 +100,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/pipe#call) and
## Pipe.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -119,16 +119,14 @@ This method was previously called `begin_training`.
>
> ```python
> pipe = nlp.add_pipe("your_custom_pipe")
> optimizer = pipe.initialize(lambda: [], pipeline=nlp.pipeline)
> pipe.initialize(lambda: [], pipeline=nlp.pipeline)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Pipe.predict {#predict tag="method"}

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@ -116,29 +116,27 @@ and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
## SentenceRecognizer.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
> #### Example
>
> ```python
> senter = nlp.add_pipe("senter")
> optimizer = senter.initialize(lambda: [], pipeline=nlp.pipeline)
> senter.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## SentenceRecognizer.predict {#predict tag="method"}

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@ -114,14 +114,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/tagger#call) and
## Tagger.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -133,16 +133,14 @@ This method was previously called `begin_training`.
>
> ```python
> tagger = nlp.add_pipe("tagger")
> optimizer = tagger.initialize(lambda: [], pipeline=nlp.pipeline)
> tagger.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Tagger.predict {#predict tag="method"}

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@ -127,14 +127,14 @@ applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
## TextCategorizer.initialize {#initialize tag="method"}
Initialize the component for training and return an
[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
function that returns an iterable of [`Example`](/api/example) objects. The data
examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
Initialize the component for training. `get_examples` should be a function that
returns an iterable of [`Example`](/api/example) objects. The data examples are
used to **initialize the model** of the component and can either be the full
training data or a representative sample. Initialization includes validating the
network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
@ -146,16 +146,14 @@ This method was previously called `begin_training`.
>
> ```python
> textcat = nlp.add_pipe("textcat")
> optimizer = textcat.initialize(lambda: [], pipeline=nlp.pipeline)
> textcat.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## TextCategorizer.predict {#predict tag="method"}

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@ -132,22 +132,21 @@ examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
> #### Example
>
> ```python
> tok2vec = nlp.add_pipe("tok2vec")
> optimizer = tok2vec.initialize(lambda: [], pipeline=nlp.pipeline)
> tok2vec.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Tok2Vec.predict {#predict tag="method"}

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@ -167,22 +167,21 @@ examples are used to **initialize the model** of the component and can either be
the full training data or a representative sample. Initialization includes
validating the network,
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
setting up the label scheme based on the data.
setting up the label scheme based on the data. This method is typically called
by [`Language.initialize`](/api/language#initialize).
> #### Example
>
> ```python
> trf = nlp.add_pipe("transformer")
> optimizer = trf.initialize(lambda: [], pipeline=nlp.pipeline)
> trf.initialize(lambda: [], nlp=nlp)
> ```
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------- |
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
| _keyword-only_ | |
| `pipeline` | Optional list of pipeline components that this component is part of. ~~Optional[List[Tuple[str, Callable[[Doc], Doc]]]]~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| **RETURNS** | The optimizer. ~~Optimizer~~ |
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
## Transformer.predict {#predict tag="method"}