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Update docs [ci skip]
This commit is contained in:
Ines Montani
parent
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commit
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@ -274,7 +274,7 @@ architectures into your training config.
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| `get_spans` | `Callable` | Function that takes a batch of [`Doc`](/api/doc) object and returns lists of [`Span`](/api) objects to process by the transformer. [See here](/api/transformer#span_getters) for built-in options and examples. |
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| `tokenizer_config` | `Dict[str, Any]` | Tokenizer settings passed to [`transformers.AutoTokenizer`](https://huggingface.co/transformers/model_doc/auto.html#transformers.AutoTokenizer). |
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### spacy-transformers.Tok2VecListener.v1 {#Tok2VecListener}
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### spacy-transformers.Tok2VecListener.v1 {#transformers-Tok2VecListener}
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> #### Example Config
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>
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@ -43,7 +43,7 @@ $ python -m spacy download [model] [--direct] [pip args]
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| Argument | Type | Description |
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| ------------------------------------- | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | positional | Model name, e.g. `en_core_web_sm`.. |
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| `model` | positional | Model name, e.g. [`en_core_web_sm`](/models/en#en_core_web_sm). |
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| `--direct`, `-d` | flag | Force direct download of exact model version. |
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| pip args <Tag variant="new">2.1</Tag> | - | Additional installation options to be passed to `pip install` when installing the model package. For example, `--user` to install to the user home directory or `--no-deps` to not install model dependencies. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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@ -182,10 +182,10 @@ run [`spacy pretrain`](/api/cli#pretrain).
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> ```
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The main data format used in spaCy v3.0 is a **binary format** created by
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serializing a [`DocBin`](/api/docbin) object, which represents a collection of
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`Doc` objects. This means that you can train spaCy models using the same format
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it outputs: annotated `Doc` objects. The binary format is extremely **efficient
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in storage**, especially when packing multiple documents together.
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serializing a [`DocBin`](/api/docbin), which represents a collection of `Doc`
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objects. This means that you can train spaCy models using the same format it
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outputs: annotated `Doc` objects. The binary format is extremely **efficient in
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storage**, especially when packing multiple documents together.
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Typically, the extension for these binary files is `.spacy`, and they are used
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as input format for specifying a [training corpus](/api/corpus) and for spaCy's
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@ -142,14 +142,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/dependencyparser#call) and
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## DependencyParser.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> parser = nlp.add_pipe("parser")
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> optimizer = parser.begin_training(pipeline=nlp.pipeline)
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> optimizer = parser.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -142,14 +142,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/entitylinker#call) and
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## EntityLinker.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> entity_linker = nlp.add_pipe("entity_linker", last=True)
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> optimizer = entity_linker.begin_training(pipeline=nlp.pipeline)
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> optimizer = entity_linker.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -131,14 +131,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/entityrecognizer#call) and
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## EntityRecognizer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> optimizer = ner.begin_training(pipeline=nlp.pipeline)
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> optimizer = ner.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -200,12 +200,28 @@ more efficient than processing texts one-by-one.
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## Language.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the pipeline for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples can either be the full training data or a representative sample. They
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are used to **initialize the models** of trainable pipeline components and are
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passed each component's [`begin_training`](/api/pipe#begin_training) method, if
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available. Initialization includes validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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<Infobox variant="warning" title="Changed in v3.0">
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The `Language.update` method now takes a **function** that is called with no
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arguments and returns a sequence of [`Example`](/api/example) objects instead of
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tuples of `Doc` and `GoldParse` objects.
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</Infobox>
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> #### Example
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>
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> ```python
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> get_examples = lambda: examples
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> optimizer = nlp.begin_training(get_examples)
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> ```
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@ -276,7 +292,7 @@ and custom registered functions if needed. See the
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| `component_cfg` | `Dict[str, dict]` | Optional dictionary of keyword arguments for components, keyed by component names. Defaults to `None`. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## Language.rehearse {#rehearse tag="method,experimental"}
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## Language.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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@ -302,6 +318,13 @@ the "catastrophic forgetting" problem. This feature is experimental.
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Evaluate a model's pipeline components.
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<Infobox variant="warning" title="Changed in v3.0">
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The `Language.update` method now takes a batch of [`Example`](/api/example)
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objects instead of tuples of `Doc` and `GoldParse` objects.
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</Infobox>
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> #### Example
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>
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> ```python
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@ -121,15 +121,21 @@ applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and
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## Morphologizer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> morphologizer = nlp.add_pipe("morphologizer")
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> nlp.pipeline.append(morphologizer)
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> optimizer = morphologizer.begin_training(pipeline=nlp.pipeline)
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> optimizer = morphologizer.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -9,8 +9,8 @@ components like the [`EntityRecognizer`](/api/entityrecognizer) or
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[`TextCategorizer`](/api/textcategorizer) inherit from it and it defines the
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interface that components should follow to function as trainable components in a
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spaCy pipeline. See the docs on
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[writing trainable components](/usage/processing-pipelines#trainable) for how to
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use the `Pipe` base class to implement custom components.
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[writing trainable components](/usage/processing-pipelines#trainable-components)
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for how to use the `Pipe` base class to implement custom components.
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> #### Why is Pipe implemented in Cython?
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>
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@ -106,14 +106,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/pipe#call) and
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## Pipe.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> pipe = nlp.add_pipe("your_custom_pipe")
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> optimizer = pipe.begin_training(pipeline=nlp.pipeline)
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> optimizer = pipe.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -200,7 +206,7 @@ This method needs to be overwritten with your own custom `update` method.
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## Pipe.rehearse {#rehearse tag="method,experimental"}
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## Pipe.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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|
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@ -116,14 +116,20 @@ and [`pipe`](/api/sentencerecognizer#pipe) delegate to the
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## SentenceRecognizer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> senter = nlp.add_pipe("senter")
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> optimizer = senter.begin_training(pipeline=nlp.pipeline)
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> optimizer = senter.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -193,7 +199,7 @@ Delegates to [`predict`](/api/sentencerecognizer#predict) and
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## SentenceRecognizer.rehearse {#rehearse tag="method,experimental"}
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## SentenceRecognizer.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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|
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@ -114,14 +114,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/tagger#call) and
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## Tagger.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
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examples are used to **initialize the model** of the component and can either be
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the full training data or a representative sample. Initialization includes
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validating the network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> tagger = nlp.add_pipe("tagger")
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> optimizer = tagger.begin_training(pipeline=nlp.pipeline)
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> optimizer = tagger.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -191,7 +197,7 @@ Delegates to [`predict`](/api/tagger#predict) and
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## Tagger.rehearse {#rehearse tag="method,experimental"}
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## Tagger.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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|
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@ -122,14 +122,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
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## TextCategorizer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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Initialize the component for training and return an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
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function that returns an iterable of [`Example`](/api/example) objects. The data
|
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examples are used to **initialize the model** of the component and can either be
|
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the full training data or a representative sample. Initialization includes
|
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validating the network,
|
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> optimizer = textcat.begin_training(pipeline=nlp.pipeline)
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> optimizer = textcat.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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@ -199,7 +205,7 @@ Delegates to [`predict`](/api/textcategorizer#predict) and
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## TextCategorizer.rehearse {#rehearse tag="method,experimental"}
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## TextCategorizer.rehearse {#rehearse tag="method,experimental" new="3"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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|
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@ -125,14 +125,20 @@ and [`set_annotations`](/api/tok2vec#set_annotations) methods.
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## Tok2Vec.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
|
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Initialize the component for training and return an
|
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[`Optimizer`](https://thinc.ai/docs/api-optimizers). `get_examples` should be a
|
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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,
|
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
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setting up the label scheme based on the data.
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> #### Example
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>
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> ```python
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> tok2vec = nlp.add_pipe("tok2vec")
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> optimizer = tok2vec.begin_training(pipeline=nlp.pipeline)
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> optimizer = tok2vec.begin_training(lambda: [], pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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|
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@ -159,14 +159,20 @@ applied to the `Doc` in order. Both [`__call__`](/api/transformer#call) and
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## Transformer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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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,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme based on the data.
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> trf = nlp.add_pipe("transformer")
|
||||
> optimizer = trf.begin_training(pipeline=nlp.pipeline)
|
||||
> optimizer = trf.begin_training(lambda: [], pipeline=nlp.pipeline)
|
||||
> ```
|
||||
|
||||
| Name | Type | Description |
|
||||
|
|
|
|||
|
|
@ -45,9 +45,9 @@ three components:
|
|||
2. **Genre:** Type of text the model is trained on, e.g. `web` or `news`.
|
||||
3. **Size:** Model size indicator, `sm`, `md` or `lg`.
|
||||
|
||||
For example, `en_core_web_sm` is a small English model trained on written web
|
||||
text (blogs, news, comments), that includes vocabulary, vectors, syntax and
|
||||
entities.
|
||||
For example, [`en_core_web_sm`](/models/en#en_core_web_sm) is a small English
|
||||
model trained on written web text (blogs, news, comments), that includes
|
||||
vocabulary, vectors, syntax and entities.
|
||||
|
||||
### Model versioning {#model-versioning}
|
||||
|
||||
|
|
|
|||
|
|
@ -687,13 +687,13 @@ give you everything you need to train fully custom models with
|
|||
|
||||
</Infobox>
|
||||
|
||||
<!-- TODO: maybe add something about why the Example class is great and its benefits, and how it's passed around, holds the alignment etc -->
|
||||
|
||||
The [`Example`](/api/example) object contains annotated training data, also
|
||||
called the **gold standard**. It's initialized with a [`Doc`](/api/doc) object
|
||||
that will hold the predictions, and another `Doc` object that holds the
|
||||
gold-standard annotations. Here's an example of a simple `Example` for
|
||||
part-of-speech tags:
|
||||
gold-standard annotations. It also includes the **alignment** between those two
|
||||
documents if they differ in tokenization. The `Example` class ensures that spaCy
|
||||
can rely on one **standardized format** that's passed through the pipeline.
|
||||
Here's an example of a simple `Example` for part-of-speech tags:
|
||||
|
||||
```python
|
||||
words = ["I", "like", "stuff"]
|
||||
|
|
@ -744,7 +744,8 @@ example = Example.from_dict(doc, {"entities": ["U-ORG", "O", "U-TECHNOLOGY", "O"
|
|||
|
||||
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
|
||||
annotations:
|
||||
annotations. For more details, see the
|
||||
[migration guide](/usage/v3#migrating-training).
|
||||
|
||||
```diff
|
||||
- gold = GoldParse(doc, entities=entities)
|
||||
|
|
|
|||
|
|
@ -14,12 +14,49 @@ menu:
|
|||
|
||||
### New training workflow and config system {#features-training}
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
- **Usage:** [Training models](/usage/training)
|
||||
- **Thinc:** [Thinc's config system](https://thinc.ai/docs/usage-config),
|
||||
[`Config`](https://thinc.ai/docs/api-config#config)
|
||||
- **CLI:** [`train`](/api/cli#train), [`pretrain`](/api/cli#pretrain),
|
||||
[`evaluate`](/api/cli#evaluate)
|
||||
- **API:** [Config format](/api/data-formats#config),
|
||||
[`registry`](/api/top-level#registry)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### Transformer-based pipelines {#features-transformers}
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
- **Usage:** [Transformers](/usage/transformers),
|
||||
[Training models](/usage/training)
|
||||
- **API:** [`Transformer`](/api/transformer),
|
||||
[`TransformerData`](/api/transformer#transformerdata),
|
||||
[`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
|
||||
- **Architectures: ** [TransformerModel](/api/architectures#TransformerModel),
|
||||
[Tok2VecListener](/api/architectures#transformers-Tok2VecListener),
|
||||
[Tok2VecTransformer](/api/architectures#Tok2VecTransformer)
|
||||
- **Models:** [`en_core_bert_sm`](/models/en)
|
||||
- **Implementation:**
|
||||
[`spacy-transformers`](https://github.com/explosion/spacy-transformers)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### Custom models using any framework {#feautres-custom-models}
|
||||
|
||||
### Manage end-to-end workflows with projects {#features-projects}
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
- **Usage:** [spaCy projects](/usage/projects),
|
||||
[Training models](/usage/training)
|
||||
- **CLI:** [`project`](/api/cli#project), [`train`](/api/cli#train)
|
||||
- **Templates:** [`projects`](https://github.com/explosion/projects)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### New built-in pipeline components {#features-pipeline-components}
|
||||
|
||||
| Name | Description |
|
||||
|
|
@ -30,14 +67,48 @@ menu:
|
|||
| [`AttributeRuler`](/api/attributeruler) | Component for setting token attributes using match patterns. |
|
||||
| [`Transformer`](/api/transformer) | Component for using [transformer models](/usage/transformers) in your pipeline, accessing outputs and aligning tokens. Provided via [`spacy-transformers`](https://github.com/explosion/spacy-transformers). |
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
- **Usage:** [Processing pipelines](/usage/processing-pipelines)
|
||||
- **API:** [Built-in pipeline components](/api#architecture-pipeline)
|
||||
- **Implementation:**
|
||||
[`spacy/pipeline`](https://github.com/explosion/spaCy/tree/develop/spacy/pipeline)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### New and improved pipeline component APIs {#features-components}
|
||||
|
||||
- `Language.factory`, `Language.component`
|
||||
- `Language.analyze_pipes`
|
||||
- Adding components from other models
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
- **Usage:** [Custom components](/usage/processing-pipelines#custom_components),
|
||||
[Defining components during training](/usage/training#config-components)
|
||||
- **API:** [`Language`](/api/language)
|
||||
- **Implementation:**
|
||||
[`spacy/language.py`](https://github.com/explosion/spaCy/tree/develop/spacy/language.py)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### Type hints and type-based data validation {#features-types}
|
||||
|
||||
> #### Example
|
||||
>
|
||||
> ```python
|
||||
> from spacy.language import Language
|
||||
> from pydantic import StrictBool
|
||||
>
|
||||
> @Language.factory("my_component")
|
||||
> def create_my_component(
|
||||
> nlp: Language,
|
||||
> name: str,
|
||||
> custom: StrictBool
|
||||
> ):
|
||||
> ...
|
||||
> ```
|
||||
|
||||
spaCy v3.0 officially drops support for Python 2 and now requires **Python
|
||||
3.6+**. This also means that the code base can take full advantage of
|
||||
[type hints](https://docs.python.org/3/library/typing.html). spaCy's user-facing
|
||||
|
|
@ -54,13 +125,36 @@ validation of Thinc's [config system](https://thinc.ai/docs/usage-config), which
|
|||
lets you to register **custom functions with typed arguments**, reference them
|
||||
in your config and see validation errors if the argument values don't match.
|
||||
|
||||
### CLI
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
| Name | Description |
|
||||
| --------------------------------------- | -------------------------------------------------------------------------------------------------------- |
|
||||
| [`init config`](/api/cli#init-config) | Initialize a [training config](/usage/training) file for a blank language or auto-fill a partial config. |
|
||||
| [`debug config`](/api/cli#debug-config) | Debug a [training config](/usage/training) file and show validation errors. |
|
||||
| [`project`](/api/cli#project) | Subcommand for cloning and running [spaCy projects](/usage/projects). |
|
||||
- **Usage: **
|
||||
[Component type hints and validation](/usage/processing-pipelines#type-hints),
|
||||
[Training with custom code](/usage/training#custom-code)
|
||||
- **Thinc: **
|
||||
[Type checking in Thinc](https://thinc.ai/docs/usage-type-checking),
|
||||
[Thinc's config system](https://thinc.ai/docs/usage-config)
|
||||
|
||||
</Infobox>
|
||||
|
||||
### New methods, attributes and commands
|
||||
|
||||
The following methods, attributes and commands are new in spaCy v3.0.
|
||||
|
||||
| Name | Description |
|
||||
| ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| [`Token.lex`](/api/token#attributes) | Access a token's [`Lexeme`](/api/lexeme). |
|
||||
| [`Language.select_pipes`](/api/language#select_pipes) | Contextmanager for enabling or disabling specific pipeline components for a block. |
|
||||
| [`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 pretrained model 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.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. |
|
||||
| [`Pipe.score`](/api/pipe#score) | Method on trainable pipeline components that returns a dictionary of evaluation scores. |
|
||||
| [`registry`](/api/top-level#registry) | Function registry to map functions to string names that can be referenced in [configs](/usage/training#config). |
|
||||
| [`init config`](/api/cli#init-config) | CLI command for initializing a [training config](/usage/training) file for a blank language or auto-filling a partial config. |
|
||||
| [`debug config`](/api/cli#debug-config) | CLI command for debugging a [training config](/usage/training) file and showing validation errors. |
|
||||
| [`project`](/api/cli#project) | Suite of CLI commands for cloning, running and managing [spaCy projects](/usage/projects). |
|
||||
|
||||
## Backwards Incompatibilities {#incompat}
|
||||
|
||||
|
|
@ -70,12 +164,21 @@ usability. The following section lists the relevant changes to the user-facing
|
|||
API. For specific examples of how to rewrite your code, check out the
|
||||
[migration guide](#migrating).
|
||||
|
||||
### Compatibility {#incompat-compat}
|
||||
<Infobox variant="warning">
|
||||
|
||||
- spaCy now requires **Python 3.6+**.
|
||||
Note that spaCy v3.0 now requires **Python 3.6+**.
|
||||
|
||||
</Infobox>
|
||||
|
||||
### API changes {#incompat-api}
|
||||
|
||||
- Model symlinks, the `link` command and shortcut names are now deprecated.
|
||||
There can be many [different models](/models) and not just one "English
|
||||
model", so you should always use the full model name like
|
||||
[`en_core_web_sm`](/models/en) explicitly.
|
||||
- The [`train`](/api/cli#train) and [`pretrain`](/api/cli#pretrain) commands now
|
||||
only take a `config.cfg` file containing the full
|
||||
[training config](/usage/training#config).
|
||||
- [`Language.add_pipe`](/api/language#add_pipe) now takes the **string name** of
|
||||
the component factory instead of the component function.
|
||||
- **Custom pipeline components** now needs to be decorated with the
|
||||
|
|
@ -87,6 +190,20 @@ API. For specific examples of how to rewrite your code, check out the
|
|||
- The `Language.disable_pipes` contextmanager has been replaced by
|
||||
[`Language.select_pipes`](/api/language#select_pipes), which can explicitly
|
||||
disable or enable components.
|
||||
- The [`Language.update`](/api/language#update),
|
||||
[`Language.evaluate`](/api/language#evaluate) and
|
||||
[`Pipe.update`](/api/pipe#update) methods now all take batches of
|
||||
[`Example`](/api/example) objects instead of `Doc` and `GoldParse` objects, or
|
||||
raw text and a dictionary of annotations.
|
||||
[`Language.begin_training`](/api/language#begin_training) and
|
||||
[`Pipe.begin_training`](/api/pipe#begin_training) now take a function that
|
||||
returns a sequence of `Example` objects to initialize the model instead of a
|
||||
list of tuples.
|
||||
- [`Matcher.add`](/api/matcher#add),
|
||||
[`PhraseMatcher.add`](/api/phrasematcher#add) and
|
||||
[`DependencyMatcher.add`](/api/dependencymatcher#add) now only accept a list
|
||||
of patterns as the second argument (instead of a variable number of
|
||||
arguments). The `on_match` callback becomes an optional keyword argument.
|
||||
|
||||
### Removed or renamed API {#incompat-removed}
|
||||
|
||||
|
|
@ -96,6 +213,7 @@ API. For specific examples of how to rewrite your code, check out the
|
|||
| `GoldParse` | [`Example`](/api/example) |
|
||||
| `GoldCorpus` | [`Corpus`](/api/corpus) |
|
||||
| `spacy debug-data` | [`spacy debug data`](/api/cli#debug-data) |
|
||||
| `spacy profile` | [`spacy debug profile`](/api/cli#debug-profile) |
|
||||
| `spacy link`, `util.set_data_path`, `util.get_data_path` | not needed, model symlinks are deprecated |
|
||||
|
||||
The following deprecated methods, attributes and arguments were removed in v3.0.
|
||||
|
|
@ -121,7 +239,7 @@ on them.
|
|||
Model symlinks and shortcuts like `en` are now officially deprecated. There are
|
||||
[many different models](/models) with different capabilities and not just one
|
||||
"English model". In order to download and load a model, you should always use
|
||||
its full name – for instance, `en_core_web_sm`.
|
||||
its full name – for instance, [`en_core_web_sm`](/models/en#en_core_web_sm).
|
||||
|
||||
```diff
|
||||
- python -m spacy download en
|
||||
|
|
@ -224,6 +342,51 @@ and you typically shouldn't have to use it in your code.
|
|||
+ parser = nlp.add_pipe("parser")
|
||||
```
|
||||
|
||||
If you need to add a component from an existing pretrained model, you can now
|
||||
use the `source` argument on [`nlp.add_pipe`](/api/language#add_pipe). This will
|
||||
check that the component is compatible, and take care of porting over all
|
||||
config. During training, you can also reference existing pretrained components
|
||||
in your [config](/usage/training#config-components) and decide whether or not
|
||||
they should be updated with more data.
|
||||
|
||||
> #### config.cfg (excerpt)
|
||||
>
|
||||
> ```ini
|
||||
> [components.ner]
|
||||
> source = "en_core_web_sm"
|
||||
> component = "ner"
|
||||
> ```
|
||||
|
||||
```diff
|
||||
source_nlp = spacy.load("en_core_web_sm")
|
||||
nlp = spacy.blank("en")
|
||||
- ner = source_nlp.get_pipe("ner")
|
||||
- nlp.add_pipe(ner)
|
||||
+ nlp.add_pipe("ner", source=source_nlp)
|
||||
```
|
||||
|
||||
### Adding match patterns {#migrating-matcher}
|
||||
|
||||
The [`Matcher.add`](/api/matcher#add),
|
||||
[`PhraseMatcher.add`](/api/phrasematcher#add) and
|
||||
[`DependencyMatcher.add`](/api/dependencymatcher#add) methods now only accept a
|
||||
**list of patterns** as the second argument (instead of a variable number of
|
||||
arguments). The `on_match` callback becomes an optional keyword argument.
|
||||
|
||||
```diff
|
||||
matcher = Matcher(nlp.vocab)
|
||||
patterns = [[{"TEXT": "Google"}, {"TEXT": "Now"}], [{"TEXT": "GoogleNow"}]]
|
||||
- matcher.add("GoogleNow", on_match, *patterns)
|
||||
+ matcher.add("GoogleNow", patterns, on_match=on_match)
|
||||
```
|
||||
|
||||
```diff
|
||||
matcher = PhraseMatcher(nlp.vocab)
|
||||
patterns = [nlp("health care reform"), nlp("healthcare reform")]
|
||||
- matcher.add("HEALTH", on_match, *patterns)
|
||||
+ matcher.add("HEALTH", patterns, on_match=on_match)
|
||||
```
|
||||
|
||||
### Training models {#migrating-training}
|
||||
|
||||
To train your models, you should now pretty much always use the
|
||||
|
|
@ -233,15 +396,20 @@ use a [flexible config file](/usage/training#config) that describes all training
|
|||
settings and hyperparameters, as well as your pipeline, model components and
|
||||
architectures to use. The `--code` argument lets you pass in code containing
|
||||
[custom registered functions](/usage/training#custom-code) that you can
|
||||
reference in your config.
|
||||
reference in your config. To get started, check out the
|
||||
[quickstart widget](/usage/training#quickstart).
|
||||
|
||||
#### Binary .spacy training data format {#migrating-training-format}
|
||||
|
||||
spaCy now uses a new
|
||||
[binary training data format](/api/data-formats#binary-training), which is much
|
||||
smaller and consists of `Doc` objects, serialized via the
|
||||
[`DocBin`](/api/docbin). You can convert your existing JSON-formatted data using
|
||||
the [`spacy convert`](/api/cli#convert) command, which outputs `.spacy` files:
|
||||
spaCy v3.0 uses a new
|
||||
[binary training data format](/api/data-formats#binary-training) created by
|
||||
serializing a [`DocBin`](/api/docbin), which represents a collection of `Doc`
|
||||
objects. This means that you can train spaCy models using the same format it
|
||||
outputs: annotated `Doc` objects. The binary format is extremely **efficient in
|
||||
storage**, especially when packing multiple documents together.
|
||||
|
||||
You can convert your existing JSON-formatted data using the
|
||||
[`spacy convert`](/api/cli#convert) command, which outputs `.spacy` files:
|
||||
|
||||
```bash
|
||||
$ python -m spacy convert ./training.json ./output
|
||||
|
|
@ -273,13 +441,72 @@ workflows, from data preprocessing to training and packaging your model.
|
|||
|
||||
</Project>
|
||||
|
||||
#### Migrating training scripts to CLI command and config {#migrating-training-scripts}
|
||||
|
||||
<!-- TODO: write -->
|
||||
|
||||
#### Training via the Python API {#migrating-training-python}
|
||||
|
||||
<!-- TODO: this should explain the GoldParse -> Example stuff -->
|
||||
For most use cases, you **shouldn't** have to write your own training scripts
|
||||
anymore. Instead, you can use [`spacy train`](/api/cli#train) with a
|
||||
[config file](/usage/training#config) and custom
|
||||
[registered functions](/usage/training#custom-code) if needed. You can even
|
||||
register callbacks that can modify the `nlp` object at different stages of its
|
||||
lifecycle to fully customize it before training.
|
||||
|
||||
If you do decide to use the [internal training API](/usage/training#api) from
|
||||
Python, you should only need a few small modifications to convert your scripts
|
||||
from spaCy v2.x to v3.x. The [`Example.from_dict`](/api/example#from_dict)
|
||||
classmethod takes a reference `Doc` and a
|
||||
[dictionary of annotations](/api/data-formats#dict-input), similar to the
|
||||
"simple training style" in spaCy v2.x:
|
||||
|
||||
```diff
|
||||
### Migrating Doc and GoldParse
|
||||
doc = nlp.make_doc("Mark Zuckerberg is the CEO of Facebook")
|
||||
entities = [(0, 15, "PERSON"), (30, 38, "ORG")]
|
||||
- gold = GoldParse(doc, entities=entities)
|
||||
+ example = Example.from_dict(doc, {"entities": entities})
|
||||
```
|
||||
|
||||
```diff
|
||||
### Migrating simple training style
|
||||
text = "Mark Zuckerberg is the CEO of Facebook"
|
||||
annotations = {"entities": [(0, 15, "PERSON"), (30, 38, "ORG")]}
|
||||
+ doc = nlp.make_doc(text)
|
||||
+ example = Example.from_dict(doc, annotations)
|
||||
```
|
||||
|
||||
The [`Language.update`](/api/language#update),
|
||||
[`Language.evaluate`](/api/language#evaluate) and
|
||||
[`Pipe.update`](/api/pipe#update) methods now all take batches of
|
||||
[`Example`](/api/example) objects instead of `Doc` and `GoldParse` objects, or
|
||||
raw text and a dictionary of annotations.
|
||||
|
||||
```python
|
||||
### Training loop {highlight="11"}
|
||||
TRAIN_DATA = [
|
||||
("Who is Shaka Khan?", {"entities": [(7, 17, "PERSON")]}),
|
||||
("I like London.", {"entities": [(7, 13, "LOC")]}),
|
||||
]
|
||||
nlp.begin_training()
|
||||
for i in range(20):
|
||||
random.shuffle(TRAIN_DATA)
|
||||
for batch in minibatch(TRAIN_DATA):
|
||||
examples = []
|
||||
for text, annots in batch:
|
||||
examples.append(Example.from_dict(nlp.make_doc(text), annots))
|
||||
nlp.update(examples)
|
||||
```
|
||||
|
||||
[`Language.begin_training`](/api/language#begin_training) and
|
||||
[`Pipe.begin_training`](/api/pipe#begin_training) now take a function that
|
||||
returns a sequence of `Example` objects to initialize the model instead of a
|
||||
list of tuples. The data examples are used to **initialize the models** of
|
||||
trainable pipeline components, which includes validating the network,
|
||||
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
||||
setting up the label scheme.
|
||||
|
||||
```diff
|
||||
- nlp.begin_training(examples)
|
||||
+ nlp.begin_training(lambda: examples)
|
||||
```
|
||||
|
||||
#### Packaging models {#migrating-training-packaging}
|
||||
|
||||
|
|
|
|||
|
|
@ -23,6 +23,7 @@ import { ReactComponent as MoonIcon } from '../images/icons/moon.svg'
|
|||
import { ReactComponent as ClipboardIcon } from '../images/icons/clipboard.svg'
|
||||
import { ReactComponent as NetworkIcon } from '../images/icons/network.svg'
|
||||
import { ReactComponent as DownloadIcon } from '../images/icons/download.svg'
|
||||
import { ReactComponent as PackageIcon } from '../images/icons/package.svg'
|
||||
|
||||
import classes from '../styles/icon.module.sass'
|
||||
|
||||
|
|
@ -49,6 +50,7 @@ const icons = {
|
|||
clipboard: ClipboardIcon,
|
||||
network: NetworkIcon,
|
||||
download: DownloadIcon,
|
||||
package: PackageIcon,
|
||||
}
|
||||
|
||||
export default function Icon({ name, width = 20, height, inline = false, variant, className }) {
|
||||
|
|
|
|||
|
|
@ -5,8 +5,17 @@ import classNames from 'classnames'
|
|||
import Icon from './icon'
|
||||
import classes from '../styles/infobox.module.sass'
|
||||
|
||||
export default function Infobox({ title, emoji, id, variant = 'default', className, children }) {
|
||||
export default function Infobox({
|
||||
title,
|
||||
emoji,
|
||||
id,
|
||||
variant = 'default',
|
||||
list = false,
|
||||
className,
|
||||
children,
|
||||
}) {
|
||||
const infoboxClassNames = classNames(classes.root, className, {
|
||||
[classes.list]: !!list,
|
||||
[classes.warning]: variant === 'warning',
|
||||
[classes.danger]: variant === 'danger',
|
||||
})
|
||||
|
|
|
|||
|
|
@ -8,13 +8,21 @@ import Icon from './icon'
|
|||
import classes from '../styles/link.module.sass'
|
||||
import { isString } from './util'
|
||||
|
||||
const internalRegex = /(http(s?)):\/\/(prodi.gy|spacy.io|irl.spacy.io)/gi
|
||||
const internalRegex = /(http(s?)):\/\/(prodi.gy|spacy.io|irl.spacy.io|explosion.ai|course.spacy.io)/gi
|
||||
|
||||
const Whitespace = ({ children }) => (
|
||||
// Ensure that links are always wrapped in spaces
|
||||
<> {children} </>
|
||||
)
|
||||
|
||||
function getIcon(dest) {
|
||||
if (/(github.com)/.test(dest)) return 'code'
|
||||
if (/^\/?api\/architectures#/.test(dest)) return 'network'
|
||||
if (/^\/?api/.test(dest)) return 'docs'
|
||||
if (/^\/?models\/(.+)/.test(dest)) return 'package'
|
||||
return null
|
||||
}
|
||||
|
||||
export default function Link({
|
||||
children,
|
||||
to,
|
||||
|
|
@ -30,22 +38,19 @@ export default function Link({
|
|||
}) {
|
||||
const dest = to || href
|
||||
const external = forceExternal || /(http(s?)):\/\//gi.test(dest)
|
||||
const isApi = !external && !hidden && !hideIcon && /^\/?api/.test(dest)
|
||||
const isArch = !external && !hidden && !hideIcon && /^\/?api\/architectures#/.test(dest)
|
||||
const isSource = external && !hidden && !hideIcon && /(github.com)/.test(dest)
|
||||
const withIcon = isApi || isArch || isSource
|
||||
const icon = getIcon(dest)
|
||||
const withIcon = !hidden && !hideIcon && !!icon
|
||||
const sourceWithText = withIcon && isString(children)
|
||||
const linkClassNames = classNames(classes.root, className, {
|
||||
[classes.hidden]: hidden,
|
||||
[classes.nowrap]: (withIcon && !sourceWithText) || isArch,
|
||||
[classes.nowrap]: (withIcon && !sourceWithText) || icon === 'network',
|
||||
[classes.withIcon]: withIcon,
|
||||
})
|
||||
const Wrapper = ws ? Whitespace : Fragment
|
||||
const icon = isArch ? 'network' : isApi ? 'docs' : isSource ? 'code' : null
|
||||
const content = (
|
||||
<>
|
||||
{sourceWithText ? <span className={classes.sourceText}>{children}</span> : children}
|
||||
{icon && <Icon name={icon} width={16} inline className={classes.icon} />}
|
||||
{withIcon && <Icon name={icon} width={16} inline className={classes.icon} />}
|
||||
</>
|
||||
)
|
||||
|
||||
|
|
|
|||
5
website/src/images/icons/package.svg
Normal file
5
website/src/images/icons/package.svg
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2">
|
||||
<path fill="none" d="M21 16V8a2 2 0 0 0-1-1.73l-7-4a2 2 0 0 0-2 0l-7 4A2 2 0 0 0 3 8v8a2 2 0 0 0 1 1.73l7 4a2 2 0 0 0 2 0l7-4A2 2 0 0 0 21 16z"></path>
|
||||
<polyline fill="none" points="3.27 6.96 12 12.01 20.73 6.96"></polyline>
|
||||
<line fill="none" x1="12" y1="22.08" x2="12" y2="12"></line>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 440 B |
|
|
@ -14,6 +14,21 @@
|
|||
font-size: inherit
|
||||
line-height: inherit
|
||||
|
||||
ul li
|
||||
padding-left: 0.75em
|
||||
|
||||
.list ul li
|
||||
font-size: var(--font-size-sm)
|
||||
list-style: none
|
||||
padding: 0
|
||||
margin: 0 0 0.35rem 0
|
||||
|
||||
&:before
|
||||
all: initial
|
||||
|
||||
a, a span
|
||||
border-bottom: 0 !important
|
||||
|
||||
.title
|
||||
font-weight: bold
|
||||
color: var(--color-theme)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user