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* Document scorers in registry and components from #8766 * Update spacy/pipeline/lemmatizer.py Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Update website/docs/api/dependencyparser.md Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Reformat Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
268 lines
15 KiB
Markdown
268 lines
15 KiB
Markdown
---
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title: AttributeRuler
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tag: class
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source: spacy/pipeline/attributeruler.py
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new: 3
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teaser: 'Pipeline component for rule-based token attribute assignment'
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api_string_name: attribute_ruler
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api_trainable: false
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---
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The attribute ruler lets you set token attributes for tokens identified by
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[`Matcher` patterns](/usage/rule-based-matching#matcher). The attribute ruler is
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typically used to handle exceptions for token attributes and to map values
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between attributes such as mapping fine-grained POS tags to coarse-grained POS
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tags. See the [usage guide](/usage/linguistic-features/#mappings-exceptions) for
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examples.
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config).
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> #### Example
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>
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> ```python
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> config = {"validate": True}
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> nlp.add_pipe("attribute_ruler", config=config)
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> ```
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| Setting | Description |
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| ---------- | --------------------------------------------------------------------------------------------- |
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| `validate` | Whether patterns should be validated (passed to the `Matcher`). Defaults to `False`. ~~bool~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/attributeruler.py
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```
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## AttributeRuler.\_\_init\_\_ {#init tag="method"}
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Initialize the attribute ruler.
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> #### Example
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>
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> ```python
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> # Construction via add_pipe
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> ruler = nlp.add_pipe("attribute_ruler")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `vocab` | The shared vocabulary to pass to the matcher. ~~Vocab~~ |
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| `name` | Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. ~~str~~ |
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| _keyword-only_ | |
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| `validate` | Whether patterns should be validated (passed to the [`Matcher`](/api/matcher#init)). Defaults to `False`. ~~bool~~ |
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| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"tag`", `"pos"`, `"morph"` and `"lemma"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
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## AttributeRuler.\_\_call\_\_ {#call tag="method"}
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Apply the attribute ruler to a `Doc`, setting token attributes for tokens
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matched by the provided patterns.
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## AttributeRuler.add {#add tag="method"}
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Add patterns to the attribute ruler. The patterns are a list of `Matcher`
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patterns and the attributes are a dict of attributes to set on the matched
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token. If the pattern matches a span of more than one token, the `index` can be
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used to set the attributes for the token at that index in the span. The `index`
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may be negative to index from the end of the span.
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> patterns = [[{"TAG": "VB"}]]
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> attrs = {"POS": "VERB"}
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> ruler.add(patterns=patterns, attrs=attrs)
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> ```
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| Name | Description |
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| ---------- | --------------------------------------------------------------------------------------------------------------------------------- |
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| `patterns` | The `Matcher` patterns to add. ~~Iterable[List[Dict[Union[int, str], Any]]]~~ |
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| `attrs` | The attributes to assign to the target token in the matched span. ~~Dict[str, Any]~~ |
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| `index` | The index of the token in the matched span to modify. May be negative to index from the end of the span. Defaults to `0`. ~~int~~ |
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## AttributeRuler.add_patterns {#add_patterns tag="method"}
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> patterns = [
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> {
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> "patterns": [[{"TAG": "VB"}]], "attrs": {"POS": "VERB"}
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> },
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> {
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> "patterns": [[{"LOWER": "two"}, {"LOWER": "apples"}]],
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> "attrs": {"LEMMA": "apple"},
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> "index": -1
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> },
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> ]
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> ruler.add_patterns(patterns)
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> ```
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Add patterns from a list of pattern dicts. Each pattern dict can specify the
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keys `"patterns"`, `"attrs"` and `"index"`, which match the arguments of
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[`AttributeRuler.add`](/api/attributeruler#add).
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| Name | Description |
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| ---------- | -------------------------------------------------------------------------- |
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| `patterns` | The patterns to add. ~~Iterable[Dict[str, Union[List[dict], dict, int]]]~~ |
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## AttributeRuler.patterns {#patterns tag="property"}
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Get all patterns that have been added to the attribute ruler in the
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`patterns_dict` format accepted by
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[`AttributeRuler.add_patterns`](/api/attributeruler#add_patterns).
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| Name | Description |
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| ----------- | -------------------------------------------------------------------------------------------- |
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| **RETURNS** | The patterns added to the attribute ruler. ~~List[Dict[str, Union[List[dict], dict, int]]]~~ |
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## AttributeRuler.initialize {#initialize tag="method"}
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Initialize the component with data and used before training to load in rules
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from a file. This method is typically called by
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[`Language.initialize`](/api/language#initialize) and lets you customize
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arguments it receives via the
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[`[initialize.components]`](/api/data-formats#config-initialize) block in the
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config.
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> ruler.initialize(lambda: [], nlp=nlp, patterns=patterns)
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> ```
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>
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> ```ini
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> ### config.cfg
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> [initialize.components.attribute_ruler]
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>
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> [initialize.components.attribute_ruler.patterns]
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> @readers = "srsly.read_json.v1"
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> path = "corpus/attribute_ruler_patterns.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 (the training data). Not used by this component. ~~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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| `patterns` | A list of pattern dicts with the keys as the arguments to [`AttributeRuler.add`](/api/attributeruler#add) (`patterns`/`attrs`/`index`) to add as patterns. Defaults to `None`. ~~Optional[Iterable[Dict[str, Union[List[dict], dict, int]]]]~~ |
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| `tag_map` | The tag map that maps fine-grained tags to coarse-grained tags and morphological features. Defaults to `None`. ~~Optional[Dict[str, Dict[Union[int, str], Union[int, str]]]]~~ |
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| `morph_rules` | The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. Defaults to `None`. ~~Optional[Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]]~~ |
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## AttributeRuler.load_from_tag_map {#load_from_tag_map tag="method"}
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Load attribute ruler patterns from a tag map.
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| Name | Description |
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| --------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `tag_map` | The tag map that maps fine-grained tags to coarse-grained tags and morphological features. ~~Dict[str, Dict[Union[int, str], Union[int, str]]]~~ |
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## AttributeRuler.load_from_morph_rules {#load_from_morph_rules tag="method"}
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Load attribute ruler patterns from morph rules.
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| Name | Description |
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| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `morph_rules` | The morph rules that map token text and fine-grained tags to coarse-grained tags, lemmas and morphological features. ~~Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]~~ |
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## AttributeRuler.to_disk {#to_disk tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> ruler.to_disk("/path/to/attribute_ruler")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## AttributeRuler.from_disk {#from_disk tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> ruler.from_disk("/path/to/attribute_ruler")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `AttributeRuler` object. ~~AttributeRuler~~ |
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## AttributeRuler.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> ruler = nlp.add_pipe("attribute_ruler")
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> ruler = ruler.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `AttributeRuler` object. ~~bytes~~ |
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## AttributeRuler.from_bytes {#from_bytes tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> ruler_bytes = ruler.to_bytes()
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> ruler = nlp.add_pipe("attribute_ruler")
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> ruler.from_bytes(ruler_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `AttributeRuler` object. ~~AttributeRuler~~ |
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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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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = ruler.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ---------- | --------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `patterns` | The `Matcher` patterns. You usually don't want to exclude this. |
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| `attrs` | The attributes to set. You usually don't want to exclude this. |
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| `indices` | The token indices. You usually don't want to exclude this. |
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