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Improve built-in component API docs

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Ines Montani 2019-02-24 13:11:49 +01:00
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27
website/docs/api/dependencyparser.md
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@ -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"}

27
website/docs/api/entityrecognizer.md
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@ -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"}

29
website/docs/api/tagger.md
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@ -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"}

8
website/docs/api/textcategorizer.md
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@ -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)
> ```