diff --git a/website/docs/api/dependencyparser.md b/website/docs/api/dependencyparser.md index 84ea707f0..b08e6139a 100644 --- a/website/docs/api/dependencyparser.md +++ b/website/docs/api/dependencyparser.md @@ -37,17 +37,19 @@ shortcut for this and instantiate the component using its string name and > parser.from_disk("/path/to/model") > ``` -| Name | Type | Description | -| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | `Vocab` | The shared vocabulary. | -| `model` | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | -| `**cfg` | - | Configuration parameters. | -| **RETURNS** | `DependencyParser` | The newly constructed object. | +| Name | Type | Description | +| ----------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | `Vocab` | The shared vocabulary. | +| `model` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | +| `**cfg` | - | Configuration parameters. | +| **RETURNS** | `DependencyParser` | The newly constructed object. | ## DependencyParser.\_\_call\_\_ {#call tag="method"} Apply the pipe to one document. The document is modified in place, and returned. -Both [`__call__`](/api/dependencyparser#call) and +This usually happens under the hood when you call the `nlp` object on a text and +all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/dependencyparser#call) and [`pipe`](/api/dependencyparser#pipe) delegate to the [`predict`](/api/dependencyparser#predict) and [`set_annotations`](/api/dependencyparser#set_annotations) methods. @@ -57,6 +59,7 @@ Both [`__call__`](/api/dependencyparser#call) and > ```python > parser = DependencyParser(nlp.vocab) > doc = nlp(u"This is a sentence.") +> # This usually happens under the hood > processed = parser(doc) > ``` @@ -82,11 +85,11 @@ Apply the pipe to a stream of documents. Both > pass > ``` -| Name | Type | Description | -| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- | -| `stream` | iterable | A stream of documents. | -| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | -| **YIELDS** | `Doc` | Processed documents in the order of the original text. | +| Name | Type | Description | +| ------------ | -------- | ------------------------------------------------------ | +| `stream` | iterable | A stream of documents. | +| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | +| **YIELDS** | `Doc` | Processed documents in the order of the original text. | ## DependencyParser.predict {#predict tag="method"} diff --git a/website/docs/api/entityrecognizer.md b/website/docs/api/entityrecognizer.md index e24d2b408..43de2c15c 100644 --- a/website/docs/api/entityrecognizer.md +++ b/website/docs/api/entityrecognizer.md @@ -37,17 +37,19 @@ shortcut for this and instantiate the component using its string name and > ner.from_disk("/path/to/model") > ``` -| Name | Type | Description | -| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | `Vocab` | The shared vocabulary. | -| `model` | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | -| `**cfg` | - | Configuration parameters. | -| **RETURNS** | `EntityRecognizer` | The newly constructed object. | +| Name | Type | Description | +| ----------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | `Vocab` | The shared vocabulary. | +| `model` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | +| `**cfg` | - | Configuration parameters. | +| **RETURNS** | `EntityRecognizer` | The newly constructed object. | ## EntityRecognizer.\_\_call\_\_ {#call tag="method"} Apply the pipe to one document. The document is modified in place, and returned. -Both [`__call__`](/api/entityrecognizer#call) and +This usually happens under the hood when you call the `nlp` object on a text and +all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/entityrecognizer#call) and [`pipe`](/api/entityrecognizer#pipe) delegate to the [`predict`](/api/entityrecognizer#predict) and [`set_annotations`](/api/entityrecognizer#set_annotations) methods. @@ -57,6 +59,7 @@ Both [`__call__`](/api/entityrecognizer#call) and > ```python > ner = EntityRecognizer(nlp.vocab) > doc = nlp(u"This is a sentence.") +> # This usually happens under the hood > processed = ner(doc) > ``` @@ -82,11 +85,11 @@ Apply the pipe to a stream of documents. Both > pass > ``` -| Name | Type | Description | -| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- | -| `stream` | iterable | A stream of documents. | -| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | -| **YIELDS** | `Doc` | Processed documents in the order of the original text. | +| Name | Type | Description | +| ------------ | -------- | ------------------------------------------------------ | +| `stream` | iterable | A stream of documents. | +| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | +| **YIELDS** | `Doc` | Processed documents in the order of the original text. | ## EntityRecognizer.predict {#predict tag="method"} diff --git a/website/docs/api/tagger.md b/website/docs/api/tagger.md index e2d7c257f..fccb7cfd0 100644 --- a/website/docs/api/tagger.md +++ b/website/docs/api/tagger.md @@ -37,18 +37,20 @@ shortcut for this and instantiate the component using its string name and > tagger.from_disk("/path/to/model") > ``` -| Name | Type | Description | -| ----------- | ------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -| `vocab` | `Vocab` | The shared vocabulary. | -| `model` | `thinc.neural.Model` or `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | -| `**cfg` | - | Configuration parameters. | -| **RETURNS** | `Tagger` | The newly constructed object. | +| Name | Type | Description | +| ----------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | +| `vocab` | `Vocab` | The shared vocabulary. | +| `model` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. | +| `**cfg` | - | Configuration parameters. | +| **RETURNS** | `Tagger` | The newly constructed object. | ## Tagger.\_\_call\_\_ {#call tag="method"} Apply the pipe to one document. The document is modified in place, and returned. -Both [`__call__`](/api/tagger#call) and [`pipe`](/api/tagger#pipe) delegate to -the [`predict`](/api/tagger#predict) and +This usually happens under the hood when you call the `nlp` object on a text and +all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/tagger#call) and [`pipe`](/api/tagger#pipe) delegate to the +[`predict`](/api/tagger#predict) and [`set_annotations`](/api/tagger#set_annotations) methods. > #### Example @@ -56,6 +58,7 @@ the [`predict`](/api/tagger#predict) and > ```python > tagger = Tagger(nlp.vocab) > doc = nlp(u"This is a sentence.") +> # This usually happens under the hood > processed = tagger(doc) > ``` @@ -79,11 +82,11 @@ Apply the pipe to a stream of documents. Both [`__call__`](/api/tagger#call) and > pass > ``` -| Name | Type | Description | -| ------------ | -------- | -------------------------------------------------------------------------------------------------------------- | -| `stream` | iterable | A stream of documents. | -| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | -| **YIELDS** | `Doc` | Processed documents in the order of the original text. | +| Name | Type | Description | +| ------------ | -------- | ------------------------------------------------------ | +| `stream` | iterable | A stream of documents. | +| `batch_size` | int | The number of texts to buffer. Defaults to `128`. | +| **YIELDS** | `Doc` | Processed documents in the order of the original text. | ## Tagger.predict {#predict tag="method"} diff --git a/website/docs/api/textcategorizer.md b/website/docs/api/textcategorizer.md index a1fa4c763..cdb826c44 100644 --- a/website/docs/api/textcategorizer.md +++ b/website/docs/api/textcategorizer.md @@ -48,9 +48,10 @@ shortcut for this and instantiate the component using its string name and ## TextCategorizer.\_\_call\_\_ {#call tag="method"} Apply the pipe to one document. The document is modified in place, and returned. -Both [`__call__`](/api/textcategorizer#call) and -[`pipe`](/api/textcategorizer#pipe) delegate to the -[`predict`](/api/textcategorizer#predict) and +This usually happens under the hood when you call the `nlp` object on a text and +all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/textcategorizer#call) and [`pipe`](/api/textcategorizer#pipe) +delegate to the [`predict`](/api/textcategorizer#predict) and [`set_annotations`](/api/textcategorizer#set_annotations) methods. > #### Example @@ -58,6 +59,7 @@ Both [`__call__`](/api/textcategorizer#call) and > ```python > textcat = TextCategorizer(nlp.vocab) > doc = nlp(u"This is a sentence.") +> # This usually happens under the hood > processed = textcat(doc) > ```