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Ines Montani
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@ -176,12 +176,12 @@ This method was previously called `begin_training`.
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> path = "corpus/labels/parser.json
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component. 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]~~ |
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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]]]~~ |
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## DependencyParser.predict {#predict tag="method"}
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@ -433,6 +433,24 @@ The labels currently added to the component.
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## DependencyParser.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`DependencyParser.initialize`](/api/dependencyparser#initialize) to initialize
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the model with a pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = parser.label_data
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> parser.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Dict[str, Dict[str, Dict[str, int]]]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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@ -165,12 +165,12 @@ This method was previously called `begin_training`.
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> path = "corpus/labels/ner.json
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component. 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]~~ |
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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]]]~~ |
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## EntityRecognizer.predict {#predict tag="method"}
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@ -421,6 +421,24 @@ The labels currently added to the component.
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## EntityRecognizer.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`EntityRecognizer.initialize`](/api/entityrecognizer#initialize) to initialize
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the model with a pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = ner.label_data
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> ner.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Dict[str, Dict[str, Dict[str, int]]]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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@ -147,12 +147,12 @@ config.
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> path = "corpus/labels/morphologizer.json
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component. 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]~~ |
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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]~~ |
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## Morphologizer.predict {#predict tag="method"}
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@ -377,6 +377,24 @@ coarse-grained POS as the feature `POS`.
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## Morphologizer.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`Morphologizer.initialize`](/api/morphologizer#initialize) to initialize the
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model with a pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = morphologizer.label_data
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> morphologizer.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~dict~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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@ -148,12 +148,12 @@ This method was previously called `begin_training`.
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> path = "corpus/labels/tagger.json
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component. 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[list]~~ |
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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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## Tagger.predict {#predict tag="method"}
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@ -411,6 +411,24 @@ The labels currently added to the component.
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## Tagger.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`Tagger.initialize`](/api/tagger#initialize) to initialize the model with a
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pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = tagger.label_data
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> tagger.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Tuple[str, ...]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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|
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@ -29,7 +29,6 @@ architectures and their arguments and hyperparameters.
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> ```python
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> from spacy.pipeline.textcat import DEFAULT_TEXTCAT_MODEL
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> config = {
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> "labels": [],
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> "threshold": 0.5,
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> "model": DEFAULT_TEXTCAT_MODEL,
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> }
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@ -38,7 +37,6 @@ architectures and their arguments and hyperparameters.
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| Setting | Description |
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| ---------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `labels` | A list of categories to learn. If empty, the model infers the categories from the data. Defaults to `[]`. ~~Iterable[str]~~ |
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| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
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| `positive_label` | The positive label for a binary task with exclusive classes, None otherwise and by default. ~~Optional[str]~~ |
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| `model` | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
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@ -61,7 +59,7 @@ architectures and their arguments and hyperparameters.
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>
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> # Construction from class
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> from spacy.pipeline import TextCategorizer
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> textcat = TextCategorizer(nlp.vocab, model, labels=[], threshold=0.5, positive_label="POS")
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> textcat = TextCategorizer(nlp.vocab, model, threshold=0.5, positive_label="POS")
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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@ -74,7 +72,6 @@ shortcut for this and instantiate the component using its string name and
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| `model` | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `labels` | The labels to use. ~~Iterable[str]~~ |
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| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
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| `positive_label` | The positive label for a binary task with exclusive classes, None otherwise. ~~Optional[str]~~ |
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@ -161,12 +158,12 @@ This method was previously called `begin_training`.
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> path = "corpus/labels/textcat.json
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `labels` | The label information to add to the component. 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]~~ |
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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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## TextCategorizer.predict {#predict tag="method"}
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@ -425,6 +422,24 @@ The labels currently added to the component.
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## TextCategorizer.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`TextCategorizer.initialize`](/api/textcategorizer#initialize) to initialize
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the model with a pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = textcat.label_data
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> textcat.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ---------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Tuple[str, ...]~~ |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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|
|
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@ -692,14 +692,14 @@ for writing the log files to [Weights & Biases](https://www.wandb.com/) with the
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[`WandbLogger`](/api/top-level#WandbLogger). The logger function receives a
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**dictionary** with the following keys:
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| Key | Value |
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| -------------- | ---------------------------------------------------------------------------------------------- |
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| `epoch` | How many passes over the data have been completed. ~~int~~ |
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| `step` | How many steps have been completed. ~~int~~ |
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| `score` | The main score from the last evaluation, measured on the dev set. ~~float~~ |
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| `other_scores` | The other scores from the last evaluation, measured on the dev set. ~~Dict[str, Any]~~ |
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| `losses` | The accumulated training losses, keyed by component name. ~~Dict[str, float]~~ |
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| `checkpoints` | A list of previous results, where each result is a (score, step, epoch) tuple. ~~List[Tuple]~~ |
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| Key | Value |
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| -------------- | ----------------------------------------------------------------------------------------------------- |
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| `epoch` | How many passes over the data have been completed. ~~int~~ |
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| `step` | How many steps have been completed. ~~int~~ |
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| `score` | The main score from the last evaluation, measured on the dev set. ~~float~~ |
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| `other_scores` | The other scores from the last evaluation, measured on the dev set. ~~Dict[str, Any]~~ |
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| `losses` | The accumulated training losses, keyed by component name. ~~Dict[str, float]~~ |
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| `checkpoints` | A list of previous results, where each result is a `(score, step)` tuple. ~~List[Tuple[float, int]]~~ |
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You can easily implement and plug in your own logger that records the training
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results in a custom way, or sends them to an experiment management tracker of
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@ -819,7 +819,84 @@ def MyModel(output_width: int) -> Model[List[Doc], List[Floats2d]]:
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### Customizing the initialization {#initialization}
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<Infobox title="This section is still under construction" emoji="🚧" variant="warning">
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When you start training a new model from scratch,
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[`spacy train`](/api/cli#train) will call
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[`nlp.initialize`](/api/language#initialize) to initialize the pipeline for
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training. This process typically includes the following:
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> #### config.cfg (excerpt)
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>
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> ```ini
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> [initialize]
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> vectors = ${paths.vectors}
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> init_tok2vec = ${paths.init_tok2vec}
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>
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> [initialize.components]
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> # Settings for components
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> ```
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1. Load in **data resources** defined in the `[initialize]` config, including
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**word vectors** and
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[pretrained](/usage/embeddings-transformers/#pretraining) **tok2vec
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weights**.
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2. Call the `initialize` methods of the tokenizer (if implemented, e.g. for
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[Chinese](/usage/models#chinese)) and pipeline components with a callback to
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access the training data, the current `nlp` object and any **custom
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arguments** defined in the `[initialize]` config.
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3. In **pipeline components**: if needed, use the data to
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[infer missing shapes](/usage/layers-architectures#thinc-shape-inference) and
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set up the label scheme if no labels are provided. Components may also load
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other data like lookup tables or dictionaries.
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The initialization step allows the config to define **all settings** required
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for the pipeline, while keeping a separation between settings and functions that
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should only be used **before training** to set up the initial pipeline, and
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logic and configuration that needs to be available **at runtime**. Without that
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separation, TODO:
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#### Initializing labels {#initialization-labels}
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Built-in pipeline components like the
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[`EntityRecognizer`](/api/entityrecognizer) or
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[`DependencyParser`](/api/dependencyparser) need to know their available labels
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and associated internal meta information to initialize their model weights.
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Using the `get_examples` callback provided on initialization, they're able to
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**read the labels off the training data** automatically, which is very
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convenient – but it can also slow down the training process to compute this
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information on every run.
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The [`init labels`](/api/cli#init-labels) command lets you auto-generate JSON
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files containing the label data for all supported components. You can then pass
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in the labels in the `[initialize]` settings for the respective components to
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allow them to initialize faster.
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> #### config.cfg
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>
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> ```ini
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> [initialize.components.ner]
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>
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> [initialize.components.ner.labels]
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> @readers = "spacy.read_labels.v1"
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> path = "corpus/labels/ner.json
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> ```
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```cli
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$ python -m spacy init labels config.cfg ./corpus --paths.train ./corpus/train.spacy
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```
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|
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Under the hood, the command delegates to the `label_data` property of the
|
||||
pipeline components, for instance
|
||||
[`EntityRecognizer.label_data`](/api/entityrecognizer#label_data).
|
||||
|
||||
<Infobox variant="warning" title="Important note">
|
||||
|
||||
The JSON format differs for each component and some components need additional
|
||||
meta information about their labels. The format exported by
|
||||
[`init labels`](/api/cli#init-labels) matches what the components need, so you
|
||||
should always let spaCy **auto-generate the labels** for you.
|
||||
|
||||
</Infobox>
|
||||
|
||||
## Data utilities {#data}
|
||||
|
|
@ -1298,8 +1375,8 @@ of being dropped.
|
|||
|
||||
> - [`nlp`](/api/language): The `nlp` object with the pipeline components and
|
||||
> their models.
|
||||
> - [`nlp.initialize`](/api/language#initialize): Start the training and return
|
||||
> an optimizer to update the component model weights.
|
||||
> - [`nlp.initialize`](/api/language#initialize): Initialize the pipeline and
|
||||
> return an optimizer to update the component model weights.
|
||||
> - [`Optimizer`](https://thinc.ai/docs/api-optimizers): Function that holds
|
||||
> state between updates.
|
||||
> - [`nlp.update`](/api/language#update): Update component models with examples.
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user