From 986f7e4d69d8e545065ed94b76318b3b3636236f Mon Sep 17 00:00:00 2001 From: Adriane Boyd Date: Mon, 20 Jul 2020 12:53:02 +0200 Subject: [PATCH] Initial draft of Morphologizer API docs --- website/docs/api/morphologizer.md | 331 +++++++++++++++++++++++++++++- 1 file changed, 327 insertions(+), 4 deletions(-) diff --git a/website/docs/api/morphologizer.md b/website/docs/api/morphologizer.md index 8761ee903..ab2b1df73 100644 --- a/website/docs/api/morphologizer.md +++ b/website/docs/api/morphologizer.md @@ -5,10 +5,14 @@ source: spacy/pipeline/morphologizer.pyx new: 3 --- -A trainable pipeline component to predict morphological features. This class is -a subclass of `Pipe` and follows the same API. The component is also available -via the string name `"morphologizer"`. After initialization, it is typically -added to the processing pipeline using [`nlp.add_pipe`](/api/language#add_pipe). +A trainable pipeline component to predict morphological features and +coarse-grained POS tags following the Universal Dependencies +[UPOS](https://universaldependencies.org/u/pos/index.html) and +[FEATS](https://universaldependencies.org/format.html#morphological-annotation) +annotation guidelines. This class is a subclass of `Pipe` and follows the same +API. The component is also available via the string name `"morphologizer"`. +After initialization, it is typically added to the processing pipeline using +[`nlp.add_pipe`](/api/language#add_pipe). ## Default config {#config} @@ -21,3 +25,322 @@ custom models, check out the [training config](/usage/training#config) docs. ```python https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/defaults/morphologizer_defaults.cfg ``` + +## Morphologizer.\_\_init\_\_ {#init tag="method"} + +Initialize the morphologizer. + +> #### Example +> +> ```python +> # Construction via create_pipe +> morphologizer = nlp.create_pipe("morphologizer") +> +> # Construction from class +> from spacy.pipeline import Morphologizer +> morphologizer = Morphologizer() +> ``` + + +Create a new pipeline instance. In your application, you would normally use a +shortcut for this and instantiate the component using its string name and +[`nlp.create_pipe`](/api/language#create_pipe). + +| Name | Type | Description | +| ----------- | -------- | ------------------------------------------------------------------------------- | +| `vocab` | `Vocab` | The shared vocabulary. | +| `model` | `Model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. | +| `**cfg` | - | Configuration parameters. | +| **RETURNS** | `Morphologizer` | The newly constructed object. | + +## Morphologizer.\_\_call\_\_ {#call tag="method"} + +Apply the pipe to one document. The document is modified in place, and returned. +This usually happens under the hood when the `nlp` object is called on a text +and all pipeline components are applied to the `Doc` in order. Both +[`__call__`](/api/morphologizer#call) and [`pipe`](/api/morphologizer#pipe) delegate to the +[`predict`](/api/morphologizer#predict) and +[`set_annotations`](/api/morphologizer#set_annotations) methods. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> doc = nlp("This is a sentence.") +> # This usually happens under the hood +> processed = morphologizer(doc) +> ``` + +| Name | Type | Description | +| ----------- | ----- | ------------------------ | +| `doc` | `Doc` | The document to process. | +| **RETURNS** | `Doc` | The processed document. | + +## Morphologizer.pipe {#pipe tag="method"} + +Apply the pipe to a stream of documents. This usually happens under the hood +when the `nlp` object is called on a text and all pipeline components are +applied to the `Doc` in order. Both [`__call__`](/api/morphologizer#call) and +[`pipe`](/api/morphologizer#pipe) delegate to the [`predict`](/api/morphologizer#predict) and +[`set_annotations`](/api/morphologizer#set_annotations) methods. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> for doc in morphologizer.pipe(docs, batch_size=50): +> pass +> ``` + +| Name | Type | Description | +| ------------ | --------------- | ------------------------------------------------------ | +| `stream` | `Iterable[Doc]` | 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. | + +## Morphologizer.predict {#predict tag="method"} + +Apply the pipeline's model to a batch of docs, without modifying them. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> scores = morphologizer.predict([doc1, doc2]) +> ``` + +| Name | Type | Description | +| ----------- | --------------- | ----------------------------------------- | +| `docs` | `Iterable[Doc]` | The documents to predict. | +| **RETURNS** | - | The model's prediction for each document. | + +## Morphologizer.set_annotations {#set_annotations tag="method"} + +Modify a batch of documents, using pre-computed scores. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> scores = morphologizer.predict([doc1, doc2]) +> morphologizer.set_annotations([doc1, doc2], scores) +> ``` + +| Name | Type | Description | +| -------- | --------------- | ------------------------------------------------ | +| `docs` | `Iterable[Doc]` | The documents to modify. | +| `scores` | - | The scores to set, produced by `Morphologizer.predict`. | + +## Morphologizer.update {#update tag="method"} + +Learn from a batch of documents and gold-standard information, updating the +pipe's model. Delegates to [`predict`](/api/morphologizer#predict) and +[`get_loss`](/api/morphologizer#get_loss). + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab, morphologizer_model) +> optimizer = nlp.begin_training() +> losses = morphologizer.update(examples, sgd=optimizer) +> ``` + +| Name | Type | Description | +| ----------------- | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | +| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. | +| _keyword-only_ | | | +| `drop` | float | The dropout rate. | +| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/morphologizer#set_annotations). | +| `sgd` | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. | +| `losses` | `Dict[str, float]` | Optional record of the loss during training. The value keyed by the model's name is updated. | +| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. | + +## Morphologizer.get_loss {#get_loss tag="method"} + +Find the loss and gradient of loss for the batch of documents and their +predicted scores. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> scores = morphologizer.predict([eg.predicted for eg in examples]) +> loss, d_loss = morphologizer.get_loss(examples, scores) +> ``` + +| Name | Type | Description | +| ----------- | ------------------- | --------------------------------------------------- | +| `examples` | `Iterable[Example]` | The batch of examples. | +| `scores` | - | Scores representing the model's predictions. | +| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. | + +## Morphologizer.begin_training {#begin_training tag="method"} + +Initialize the pipe for training, using data examples if available. Return an +[`Optimizer`](https://thinc.ai/docs/api-optimizers) object. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> nlp.pipeline.append(morphologizer) +> optimizer = morphologizer.begin_training(pipeline=nlp.pipeline) +> ``` + +| Name | Type | Description | +| -------------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `get_examples` | `Iterable[Example]` | Optional gold-standard annotations in the form of [`Example`](/api/example) objects. | +| `pipeline` | `List[(str, callable)]` | Optional list of pipeline components that this component is part of. | +| `sgd` | `Optimizer` | An optional [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. Will be created via [`create_optimizer`](/api/morphologizer#create_optimizer) if not set. | +| **RETURNS** | `Optimizer` | An optimizer. | + +## Morphologizer.create_optimizer {#create_optimizer tag="method"} + +Create an optimizer for the pipeline component. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> optimizer = morphologizer.create_optimizer() +> ``` + +| Name | Type | Description | +| ----------- | ----------- | --------------------------------------------------------------- | +| **RETURNS** | `Optimizer` | The [`Optimizer`](https://thinc.ai/docs/api-optimizers) object. | + +## Morphologizer.use_params {#use_params tag="method, contextmanager"} + +Modify the pipe's model, to use the given parameter values. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> with morphologizer.use_params(): +> morphologizer.to_disk("/best_model") +> ``` + +| Name | Type | Description | +| -------- | ---- | ---------------------------------------------------------------------------------------------------------- | +| `params` | - | The parameter values to use in the model. At the end of the context, the original parameters are restored. | + +## Morphologizer.add_label {#add_label tag="method"} + +Add a new label to the pipe. If the `Morphologizer` should set annotations for +both `pos` and `morph`, the label should include the UPOS as the feature `POS`. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin") +> ``` + +| Name | Type | Description | +| -------- | ---- | --------------------------------------------------------------- | +| `label` | str | The label to add. | + +## Morphologizer.to_disk {#to_disk tag="method"} + +Serialize the pipe to disk. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> morphologizer.to_disk("/path/to/morphologizer") +> ``` + +| Name | Type | Description | +| --------- | ------------ | --------------------------------------------------------------------------------------------------------------------- | +| `path` | str / `Path` | A path to a directory, which will be created if it doesn't exist. Paths may be either strings or `Path`-like objects. | +| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. | + +## Morphologizer.from_disk {#from_disk tag="method"} + +Load the pipe from disk. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> morphologizer.from_disk("/path/to/morphologizer") +> ``` + +| Name | Type | Description | +| ----------- | ------------ | -------------------------------------------------------------------------- | +| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. | +| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. | +| **RETURNS** | `Morphologizer` | The modified `Morphologizer` object. | + +## Morphologizer.to_bytes {#to_bytes tag="method"} + +> #### Example +> +> ```python +> morphologizer = Morphologizer(nlp.vocab) +> morphologizer_bytes = morphologizer.to_bytes() +> ``` + +Serialize the pipe to a bytestring. + +| Name | Type | Description | +| ----------- | ----- | ------------------------------------------------------------------------- | +| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. | +| **RETURNS** | bytes | The serialized form of the `Morphologizer` object. | + +## Morphologizer.from_bytes {#from_bytes tag="method"} + +Load the pipe from a bytestring. Modifies the object in place and returns it. + +> #### Example +> +> ```python +> morphologizer_bytes = morphologizer.to_bytes() +> morphologizer = Morphologizer(nlp.vocab) +> morphologizer.from_bytes(morphologizer_bytes) +> ``` + +| Name | Type | Description | +| ------------ | -------- | ------------------------------------------------------------------------- | +| `bytes_data` | bytes | The data to load from. | +| `exclude` | list | String names of [serialization fields](#serialization-fields) to exclude. | +| **RETURNS** | `Morphologizer` | The `Morphologizer` object. | + +## Morphologizer.labels {#labels tag="property"} + +The labels currently added to the component in Universal Dependencies [FEATS +format](https://universaldependencies.org/format.html#morphological-annotation). +Note that even for a blank component, this will always include the internal +empty label `_`. If POS features are used, the labels will include the +coarse-grained POS as the feature `POS`. + +> #### Example +> +> ```python +> morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin") +> assert "Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin" in morphologizer.labels +> ``` + +| Name | Type | Description | +| ----------- | ----- | ---------------------------------- | +| **RETURNS** | tuple | The labels added to the component. | + +## Serialization fields {#serialization-fields} + +During serialization, spaCy will export several data fields used to restore +different aspects of the object. If needed, you can exclude them from +serialization by passing in the string names via the `exclude` argument. + +> #### Example +> +> ```python +> data = morphologizer.to_disk("/path", exclude=["vocab"]) +> ``` + +| Name | Description | +| --------- | ------------------------------------------------------------------------------------------ | +| `vocab` | The shared [`Vocab`](/api/vocab). | +| `cfg` | The config file. You usually don't want to exclude this. | +| `model` | The binary model data. You usually don't want to exclude this. |