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218 lines
9.4 KiB
Markdown
218 lines
9.4 KiB
Markdown
---
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title: EntityRuler
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tag: class
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source: spacy/pipeline/entityruler.py
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new: 2.1
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---
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The EntityRuler lets you add spans to the [`Doc.ents`](/api/doc#ents) using
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token-based rules or exact phrase matches. It can be combined with the
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statistical [`EntityRecognizer`](/api/entityrecognizer) to boost accuracy, or
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used on its own to implement a purely rule-based entity recognition system.
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After initialization, the component is typically added to the processing
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pipeline using [`nlp.add_pipe`](/api/language#add_pipe).
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## EntityRuler.\_\_init\_\_ {#init tag="method"}
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Initialize the entity ruler. If patterns are supplied here, they need to be a
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list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either
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be a token pattern (list) or a phrase pattern (string). For example:
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`{'label': 'ORG', 'pattern': 'Apple'}`.
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> #### Example
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>
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> ```python
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> # Construction via create_pipe
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> ruler = nlp.create_pipe("entityruler")
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>
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> # Construction from class
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> from spacy.pipeline import EntityRuler
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> ruler = EntityRuler(nlp, overwrite_ents=True)
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> ```
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| Name | Type | Description |
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| --------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `nlp` | `Language` | The shared nlp object to pass the vocab to the matchers and process phrase patterns. |
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| `patterns` | iterable | Optional patterns to load in. |
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| `phrase_matcher_attr` | int / unicode | Optional attr to pass to the internal [`PhraseMatcher`](/api/phtasematcher). defaults to `None` |
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| `overwrite_ents` | bool | If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. Defaults to `False`. |
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| `**cfg` | - | Other config parameters. If pipeline component is loaded as part of a model pipeline, this will include all keyword arguments passed to `spacy.load`. |
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| **RETURNS** | `EntityRuler` | The newly constructed object. |
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## EntityRuler.\_\len\_\_ {#len tag="method"}
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The number of all patterns added to the entity ruler.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> assert len(ruler) == 0
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> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
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> assert len(ruler) == 1
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> ```
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| Name | Type | Description |
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| ----------- | ---- | ----------------------- |
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| **RETURNS** | int | The number of patterns. |
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## EntityRuler.\_\_contains\_\_ {#contains tag="method"}
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Whether a label is present in the patterns.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
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> assert "ORG" in ruler
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> assert not "PERSON" in ruler
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> ```
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| Name | Type | Description |
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| ----------- | ------- | -------------------------------------------- |
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| `label` | unicode | The label to check. |
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| **RETURNS** | bool | Whether the entity ruler contains the label. |
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## EntityRuler.\_\_call\_\_ {#call tag="method"}
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Find matches in the `Doc` and add them to the `doc.ents`. Typically, this
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happens automatically after the component has been added to the pipeline using
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[`nlp.add_pipe`](/api/language#add_pipe). If the entity ruler was initialized
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with `overwrite_ents=True`, existing entities will be replaced if they overlap
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with the matches.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}])
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> nlp.add_pipe(ruler)
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>
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> doc = nlp("A text about Apple.")
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> ents = [(ent.text, ent.label_) for ent in doc.ents]
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> assert ents == [("Apple", "ORG")]
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------------------------------------------ |
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| `doc` | `Doc` | The `Doc` object to process, e.g. the `Doc` in the pipeline. |
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| **RETURNS** | `Doc` | The modified `Doc` with added entities, if available. |
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## EntityRuler.add_patterns {#add_patterns tag="method"}
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Add patterns to the entity ruler. A pattern can either be a token pattern (list
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of dicts) or a phrase pattern (string). For more details, see the usage guide on
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[rule-based matching](/usage/rule-based-matching).
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> #### Example
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>
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> ```python
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> patterns = [
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> {"label": "ORG", "pattern": "Apple"},
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> {"label": "GPE", "pattern": [{"lower": "san"}, {"lower": "francisco"}]}
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> ]
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> ruler = EntityRuler(nlp)
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> ruler.add_patterns(patterns)
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> ```
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| Name | Type | Description |
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| ---------- | ---- | -------------------- |
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| `patterns` | list | The patterns to add. |
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## EntityRuler.to_disk {#to_disk tag="method"}
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Save the entity ruler patterns to a directory. The patterns will be saved as
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newline-delimited JSON (JSONL). If a file with the suffix `.jsonl` is provided,
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only the patterns are saved as JSONL. If a directory name is provided, a
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`patterns.jsonl` and `cfg` file with the component configuration is exported.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> ruler.to_disk("/path/to/patterns.jsonl") # saves patterns only
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> ruler.to_disk("/path/to/entity_ruler") # saves patterns and config
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> ```
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| Name | Type | Description |
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| ------ | ---------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
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| `path` | unicode / `Path` | A path to a JSONL file or directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. |
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## EntityRuler.from_disk {#from_disk tag="method"}
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Load the entity ruler from a file. Expects either a file containing
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newline-delimited JSON (JSONL) with one entry per line, or a directory
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containing a `patterns.jsonl` file and a `cfg` file with the component
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configuration.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> ruler.from_disk("/path/to/patterns.jsonl") # loads patterns only
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> ruler.from_disk("/path/to/entity_ruler") # loads patterns and config
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> ```
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| Name | Type | Description |
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| ----------- | ---------------- | ---------------------------------------------------------------------------------------- |
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| `path` | unicode / `Path` | A path to a JSONL file or directory. Paths may be either strings or `Path`-like objects. |
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| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
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## EntityRuler.to_bytes {#to_bytes tag="method"}
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Serialize the entity ruler patterns to a bytestring.
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> #### Example
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>
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> ```python
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> ruler = EntityRuler(nlp)
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> ruler_bytes = ruler.to_bytes()
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------ |
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| **RETURNS** | bytes | The serialized patterns. |
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## EntityRuler.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 = EntityRuler(nlp)
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> ruler.from_bytes(ruler_bytes)
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> ```
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| Name | Type | Description |
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| ---------------- | ------------- | ---------------------------------- |
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| `patterns_bytes` | bytes | The bytestring to load. |
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| **RETURNS** | `EntityRuler` | The modified `EntityRuler` object. |
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## EntityRuler.labels {#labels tag="property"}
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All labels present in the match patterns.
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| Name | Type | Description |
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| ----------- | ----- | ------------------ |
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| **RETURNS** | tuple | The string labels. |
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## EntityRuler.patterns {#patterns tag="property"}
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Get all patterns that were added to the entity ruler.
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| Name | Type | Description |
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| ----------- | ---- | -------------------------------------------------- |
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| **RETURNS** | list | The original patterns, one dictionary per pattern. |
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## Attributes {#attributes}
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| Name | Type | Description |
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| ----------------- | ------------------------------------- | ---------------------------------------------------------------- |
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| `matcher` | [`Matcher`](/api/matcher) | The underlying matcher used to process token patterns. |
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| `phrase_matcher` | [`PhraseMatcher`](/api/phtasematcher) | The underlying phrase matcher, used to process phrase patterns. |
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| `token_patterns` | dict | The token patterns present in the entity ruler, keyed by label. |
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| `phrase_patterns` | dict | The phrase patterns present in the entity ruler, keyed by label. |
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