mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-16 14:47:16 +03:00
28de85737f
* add label smoothing * use True/False instead of floats * add entropy to debug data * formatting * docs * change test to check difference in distributions * Update website/docs/api/tagger.mdx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * Update spacy/pipeline/tagger.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * bool -> float * update docs * fix seed * black * update tests to use label_smoothing = 0.0 * set default to 0.0, update quickstart * Update spacy/pipeline/tagger.pyx Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> * update morphologizer, tagger test * fix morph docs * add url to docs --------- Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
442 lines
25 KiB
Plaintext
442 lines
25 KiB
Plaintext
---
|
|
title: Morphologizer
|
|
tag: class
|
|
source: spacy/pipeline/morphologizer.pyx
|
|
version: 3
|
|
teaser: 'Pipeline component for predicting morphological features'
|
|
api_base_class: /api/tagger
|
|
api_string_name: morphologizer
|
|
api_trainable: true
|
|
---
|
|
|
|
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.
|
|
|
|
## Assigned Attributes {id="assigned-attributes"}
|
|
|
|
Predictions are saved to `Token.morph` and `Token.pos`.
|
|
|
|
| Location | Value |
|
|
| ------------- | ----------------------------------------- |
|
|
| `Token.pos` | The UPOS part of speech (hash). ~~int~~ |
|
|
| `Token.pos_` | The UPOS part of speech. ~~str~~ |
|
|
| `Token.morph` | Morphological features. ~~MorphAnalysis~~ |
|
|
|
|
## Config and implementation {id="config"}
|
|
|
|
The default config is defined by the pipeline component factory and describes
|
|
how the component should be configured. You can override its settings via the
|
|
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
|
|
[`config.cfg` for training](/usage/training#config). See the
|
|
[model architectures](/api/architectures) documentation for details on the
|
|
architectures and their arguments and hyperparameters.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> from spacy.pipeline.morphologizer import DEFAULT_MORPH_MODEL
|
|
> config = {"model": DEFAULT_MORPH_MODEL}
|
|
> nlp.add_pipe("morphologizer", config=config)
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `model` | The model to use. Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
|
|
| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
|
|
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
|
|
| `label_smoothing` <Tag variant="new">3.6</Tag> | [Label smoothing](https://arxiv.org/abs/1906.02629) factor. Defaults to `0.0`. ~~float~~ |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/morphologizer.pyx
|
|
```
|
|
|
|
## Morphologizer.\_\_init\_\_ {id="init",tag="method"}
|
|
|
|
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.add_pipe`](/api/language#add_pipe).
|
|
|
|
The `overwrite` and `extend` settings determine how existing annotation is
|
|
handled (with the example for existing annotation `A=B|C=D` + predicted
|
|
annotation `C=E|X=Y`):
|
|
|
|
- `overwrite=True, extend=True`: overwrite values of existing features, add any
|
|
new features (`A=B|C=D` + `C=E|X=Y` → `A=B|C=E|X=Y`)
|
|
- `overwrite=True, extend=False`: overwrite completely, removing any existing
|
|
features (`A=B|C=D` + `C=E|X=Y` → `C=E|X=Y`)
|
|
- `overwrite=False, extend=True`: keep values of existing features, add any new
|
|
features (`A=B|C=D` + `C=E|X=Y` → `A=B|C=D|X=Y`)
|
|
- `overwrite=False, extend=False`: do not modify the existing annotation if set
|
|
(`A=B|C=D` + `C=E|X=Y` → `A=B|C=D`)
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via add_pipe with default model
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
>
|
|
> # Construction via create_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_morphologizer"}}
|
|
> morphologizer = nlp.add_pipe("morphologizer", config=config)
|
|
>
|
|
> # Construction from class
|
|
> from spacy.pipeline import Morphologizer
|
|
> morphologizer = Morphologizer(nlp.vocab, model)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| `overwrite` <Tag variant="new">3.2</Tag> | Whether the values of existing features are overwritten. Defaults to `True`. ~~bool~~ |
|
|
| `extend` <Tag variant="new">3.2</Tag> | Whether existing feature types (whose values may or may not be overwritten depending on `overwrite`) are preserved. Defaults to `False`. ~~bool~~ |
|
|
| `scorer` <Tag variant="new">3.2</Tag> | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attributes `"pos"` and `"morph"` and [`Scorer.score_token_attr_per_feat`](/api/scorer#score_token_attr_per_feat) for the attribute `"morph"`. ~~Optional[Callable]~~ |
|
|
|
|
## Morphologizer.\_\_call\_\_ {id="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
|
|
> doc = nlp("This is a sentence.")
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> # This usually happens under the hood
|
|
> processed = morphologizer(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## Morphologizer.pipe {id="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 = nlp.add_pipe("morphologizer")
|
|
> for doc in morphologizer.pipe(docs, batch_size=50):
|
|
> pass
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------- |
|
|
| `stream` | A stream of documents. ~~Iterable[Doc]~~ |
|
|
| _keyword-only_ | |
|
|
| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
|
|
| **YIELDS** | The processed documents in order. ~~Doc~~ |
|
|
|
|
## Morphologizer.initialize {id="initialize",tag="method"}
|
|
|
|
Initialize the component for training. `get_examples` should be a function that
|
|
returns an iterable of [`Example`](/api/example) objects. **At least one example
|
|
should be supplied.** The data examples are used to **initialize the model** of
|
|
the component and can either be the full training data or a representative
|
|
sample. Initialization includes validating the network,
|
|
[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
|
|
setting up the label scheme based on the data. This method is typically called
|
|
by [`Language.initialize`](/api/language#initialize) and lets you customize
|
|
arguments it receives via the
|
|
[`[initialize.components]`](/api/data-formats#config-initialize) block in the
|
|
config.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> morphologizer.initialize(lambda: examples, nlp=nlp)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.morphologizer]
|
|
>
|
|
> [initialize.components.morphologizer.labels]
|
|
> @readers = "spacy.read_labels.v1"
|
|
> path = "corpus/labels/morphologizer.json
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. Must contain at least one `Example`. ~~Callable[[], Iterable[Example]]~~ |
|
|
| _keyword-only_ | |
|
|
| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
|
|
| `labels` | The label information to add to the component, as provided by the [`label_data`](#label_data) property after initialization. To generate a reusable JSON file from your data, you should run the [`init labels`](/api/cli#init-labels) command. If no labels are provided, the `get_examples` callback is used to extract the labels from the data, which may be a lot slower. ~~Optional[dict]~~ |
|
|
|
|
## Morphologizer.predict {id="predict",tag="method"}
|
|
|
|
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
|
|
modifying them.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> scores = morphologizer.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------- |
|
|
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
|
| **RETURNS** | The model's prediction for each document. |
|
|
|
|
## Morphologizer.set_annotations {id="set_annotations",tag="method"}
|
|
|
|
Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> scores = morphologizer.predict([doc1, doc2])
|
|
> morphologizer.set_annotations([doc1, doc2], scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | ------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `scores` | The scores to set, produced by `Morphologizer.predict`. |
|
|
|
|
## Morphologizer.update {id="update",tag="method"}
|
|
|
|
Learn from a batch of [`Example`](/api/example) objects containing the
|
|
predictions and gold-standard annotations, and update the component's model.
|
|
Delegates to [`predict`](/api/morphologizer#predict) and
|
|
[`get_loss`](/api/morphologizer#get_loss).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> optimizer = nlp.initialize()
|
|
> losses = morphologizer.update(examples, sgd=optimizer)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
|
|
| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
|
|
| _keyword-only_ | |
|
|
| `drop` | The dropout rate. ~~float~~ |
|
|
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
|
|
| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
|
|
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
|
|
|
|
## Morphologizer.get_loss {id="get_loss",tag="method"}
|
|
|
|
Find the loss and gradient of loss for the batch of documents and their
|
|
predicted scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> scores = morphologizer.predict([eg.predicted for eg in examples])
|
|
> loss, d_loss = morphologizer.get_loss(examples, scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | --------------------------------------------------------------------------- |
|
|
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
|
|
| `scores` | Scores representing the model's predictions. |
|
|
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
|
|
|
|
## Morphologizer.create_optimizer {id="create_optimizer",tag="method"}
|
|
|
|
Create an optimizer for the pipeline component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> optimizer = morphologizer.create_optimizer()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------- |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## Morphologizer.use_params {id="use_params",tag="method, contextmanager"}
|
|
|
|
Modify the pipe's model, to use the given parameter values. At the end of the
|
|
context, the original parameters are restored.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> with morphologizer.use_params(optimizer.averages):
|
|
> morphologizer.to_disk("/best_model")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | -------------------------------------------------- |
|
|
| `params` | The parameter values to use in the model. ~~dict~~ |
|
|
|
|
## Morphologizer.add_label {id="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`.
|
|
Raises an error if the output dimension is already set, or if the model has
|
|
already been fully [initialized](#initialize). Note that you don't have to call
|
|
this method if you provide a **representative data sample** to the
|
|
[`initialize`](#initialize) method. In this case, all labels found in the sample
|
|
will be automatically added to the model, and the output dimension will be
|
|
[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> morphologizer.add_label("Mood=Ind|POS=VERB|Tense=Past|VerbForm=Fin")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------- |
|
|
| `label` | The label to add. ~~str~~ |
|
|
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
|
|
|
|
## Morphologizer.to_disk {id="to_disk",tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> morphologizer.to_disk("/path/to/morphologizer")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `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]~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
|
|
## Morphologizer.from_disk {id="from_disk",tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> morphologizer.from_disk("/path/to/morphologizer")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ----------------------------------------------------------------------------------------------- |
|
|
| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The modified `Morphologizer` object. ~~Morphologizer~~ |
|
|
|
|
## Morphologizer.to_bytes {id="to_bytes",tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> morphologizer = nlp.add_pipe("morphologizer")
|
|
> morphologizer_bytes = morphologizer.to_bytes()
|
|
> ```
|
|
|
|
Serialize the pipe to a bytestring.
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------- |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The serialized form of the `Morphologizer` object. ~~bytes~~ |
|
|
|
|
## Morphologizer.from_bytes {id="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 = nlp.add_pipe("morphologizer")
|
|
> morphologizer.from_bytes(morphologizer_bytes)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------- |
|
|
| `bytes_data` | The data to load from. ~~bytes~~ |
|
|
| _keyword-only_ | |
|
|
| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
|
|
| **RETURNS** | The `Morphologizer` object. ~~Morphologizer~~ |
|
|
|
|
## Morphologizer.labels {id="labels",tag="property"}
|
|
|
|
The labels currently added to the component in the Universal Dependencies
|
|
[FEATS](https://universaldependencies.org/format.html#morphological-annotation)
|
|
format. 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 | Description |
|
|
| ----------- | ------------------------------------------------------ |
|
|
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## Morphologizer.label_data {id="label_data",tag="property",version="3"}
|
|
|
|
The labels currently added to the component and their internal meta information.
|
|
This is the data generated by [`init labels`](/api/cli#init-labels) and used by
|
|
[`Morphologizer.initialize`](/api/morphologizer#initialize) to initialize the
|
|
model with a pre-defined label set.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> labels = morphologizer.label_data
|
|
> morphologizer.initialize(lambda: [], nlp=nlp, labels=labels)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------- |
|
|
| **RETURNS** | The label data added to the component. ~~dict~~ |
|
|
|
|
## Serialization fields {id="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. |
|