mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-30 23:47:31 +03:00
85778dfcf4
* Add edit tree lemmatizer Co-authored-by: Daniël de Kok <me@danieldk.eu> * Hide edit tree lemmatizer labels * Use relative imports * Switch to single quotes in error message * Type annotation fixes Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Reformat edit_tree_lemmatizer with black * EditTreeLemmatizer.predict: take Iterable Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com> * Validate edit trees during deserialization This change also changes the serialized representation. Rather than mirroring the deep C structure, we use a simple flat union of the match and substitution node types. * Move edit_trees to _edit_tree_internals * Fix invalid edit tree format error message * edit_tree_lemmatizer: remove outdated TODO comment * Rename factory name to trainable_lemmatizer * Ignore type instead of casting truths to List[Union[Ints1d, Floats2d, List[int], List[str]]] for thinc v8.0.14 * Switch to Tagger.v2 * Add documentation for EditTreeLemmatizer * docs: Fix 3.2 -> 3.3 somewhere * trainable_lemmatizer documentation fixes * docs: EditTreeLemmatizer is in edit_tree_lemmatizer.py Co-authored-by: Daniël de Kok <me@danieldk.eu> Co-authored-by: Daniël de Kok <me@github.danieldk.eu> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
410 lines
24 KiB
Markdown
410 lines
24 KiB
Markdown
---
|
|
title: EditTreeLemmatizer
|
|
tag: class
|
|
source: spacy/pipeline/edit_tree_lemmatizer.py
|
|
new: 3.3
|
|
teaser: 'Pipeline component for lemmatization'
|
|
api_base_class: /api/pipe
|
|
api_string_name: trainable_lemmatizer
|
|
api_trainable: true
|
|
---
|
|
|
|
A trainable component for assigning base forms to tokens. This lemmatizer uses
|
|
**edit trees** to transform tokens into base forms. The lemmatization model
|
|
predicts which edit tree is applicable to a token. The edit tree data structure
|
|
and construction method used by this lemmatizer were proposed in
|
|
[Joint Lemmatization and Morphological Tagging with Lemming](https://aclanthology.org/D15-1272.pdf)
|
|
(Thomas Müller et al., 2015).
|
|
|
|
For a lookup and rule-based lemmatizer, see [`Lemmatizer`](/api/lemmatizer).
|
|
|
|
## Assigned Attributes {#assigned-attributes}
|
|
|
|
Predictions are assigned to `Token.lemma`.
|
|
|
|
| Location | Value |
|
|
| -------------- | ------------------------- |
|
|
| `Token.lemma` | The lemma (hash). ~~int~~ |
|
|
| `Token.lemma_` | The lemma. ~~str~~ |
|
|
|
|
## Config and implementation {#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.edit_tree_lemmatizer import DEFAULT_EDIT_TREE_LEMMATIZER_MODEL
|
|
> config = {"model": DEFAULT_EDIT_TREE_LEMMATIZER_MODEL}
|
|
> nlp.add_pipe("trainable_lemmatizer", config=config, name="lemmatizer")
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| --------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `model` | A model instance that predicts the edit tree probabilities. The output vectors should match the number of edit trees in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
| `backoff` | ~~Token~~ attribute to use when no applicable edit tree is found. Defaults to `orth`. ~~str~~ |
|
|
| `min_tree_freq` | Minimum frequency of an edit tree in the training set to be used. Defaults to `3`. ~~int~~ |
|
|
| `overwrite` | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
|
|
| `top_k` | The number of most probable edit trees to try before resorting to `backoff`. Defaults to `1`. ~~int~~ |
|
|
| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"lemma"`. ~~Optional[Callable]~~ |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/edit_tree_lemmatizer.py
|
|
```
|
|
|
|
## EditTreeLemmatizer.\_\_init\_\_ {#init tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via add_pipe with default model
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
>
|
|
> # Construction via create_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_tagger"}}
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", config=config, name="lemmatizer")
|
|
>
|
|
> # Construction from class
|
|
> from spacy.pipeline import EditTreeLemmatizer
|
|
> lemmatizer = EditTreeLemmatizer(nlp.vocab, model)
|
|
> ```
|
|
|
|
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).
|
|
|
|
| Name | Description |
|
|
| --------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `model` | A model instance that predicts the edit tree probabilities. The output vectors should match the number of edit trees in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| `backoff` | ~~Token~~ attribute to use when no applicable edit tree is found. Defaults to `orth`. ~~str~~ |
|
|
| `min_tree_freq` | Minimum frequency of an edit tree in the training set to be used. Defaults to `3`. ~~int~~ |
|
|
| `overwrite` | Whether existing annotation is overwritten. Defaults to `False`. ~~bool~~ |
|
|
| `top_k` | The number of most probable edit trees to try before resorting to `backoff`. Defaults to `1`. ~~int~~ |
|
|
| `scorer` | The scoring method. Defaults to [`Scorer.score_token_attr`](/api/scorer#score_token_attr) for the attribute `"lemma"`. ~~Optional[Callable]~~ |
|
|
|
|
## EditTreeLemmatizer.\_\_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/edittreelemmatizer#call) and
|
|
[`pipe`](/api/edittreelemmatizer#pipe) delegate to the
|
|
[`predict`](/api/edittreelemmatizer#predict) and
|
|
[`set_annotations`](/api/edittreelemmatizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> doc = nlp("This is a sentence.")
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> # This usually happens under the hood
|
|
> processed = lemmatizer(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## EditTreeLemmatizer.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/edittreelemmatizer#call)
|
|
and [`pipe`](/api/edittreelemmatizer#pipe) delegate to the
|
|
[`predict`](/api/edittreelemmatizer#predict) and
|
|
[`set_annotations`](/api/edittreelemmatizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> for doc in lemmatizer.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~~ |
|
|
|
|
## EditTreeLemmatizer.initialize {#initialize tag="method" new="3"}
|
|
|
|
Initialize the component for training. `get_examples` should be a function that
|
|
returns an iterable of [`Example`](/api/example) objects. 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
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> lemmatizer.initialize(lambda: [], nlp=nlp)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.lemmatizer]
|
|
>
|
|
> [initialize.components.lemmatizer.labels]
|
|
> @readers = "spacy.read_labels.v1"
|
|
> path = "corpus/labels/lemmatizer.json
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~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[Iterable[str]]~~ |
|
|
|
|
## EditTreeLemmatizer.predict {#predict tag="method"}
|
|
|
|
Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
|
|
modifying them.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> tree_ids = lemmatizer.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------- |
|
|
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
|
| **RETURNS** | The model's prediction for each document. |
|
|
|
|
## EditTreeLemmatizer.set_annotations {#set_annotations tag="method"}
|
|
|
|
Modify a batch of [`Doc`](/api/doc) objects, using pre-computed tree
|
|
identifiers.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> tree_ids = lemmatizer.predict([doc1, doc2])
|
|
> lemmatizer.set_annotations([doc1, doc2], tree_ids)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------- | ------------------------------------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `tree_ids` | The identifiers of the edit trees to apply, produced by `EditTreeLemmatizer.predict`. |
|
|
|
|
## EditTreeLemmatizer.update {#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/edittreelemmatizer#predict) and
|
|
[`get_loss`](/api/edittreelemmatizer#get_loss).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> optimizer = nlp.initialize()
|
|
> losses = lemmatizer.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]~~ |
|
|
|
|
## EditTreeLemmatizer.get_loss {#get_loss tag="method"}
|
|
|
|
Find the loss and gradient of loss for the batch of documents and their
|
|
predicted scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> scores = lemmatizer.model.begin_update([eg.predicted for eg in examples])
|
|
> loss, d_loss = lemmatizer.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]~~ |
|
|
|
|
## EditTreeLemmatizer.create_optimizer {#create_optimizer tag="method"}
|
|
|
|
Create an optimizer for the pipeline component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> optimizer = lemmatizer.create_optimizer()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------- |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## EditTreeLemmatizer.use_params {#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
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> with lemmatizer.use_params(optimizer.averages):
|
|
> lemmatizer.to_disk("/best_model")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | -------------------------------------------------- |
|
|
| `params` | The parameter values to use in the model. ~~dict~~ |
|
|
|
|
## EditTreeLemmatizer.to_disk {#to_disk tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> lemmatizer.to_disk("/path/to/lemmatizer")
|
|
> ```
|
|
|
|
| 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]~~ |
|
|
|
|
## EditTreeLemmatizer.from_disk {#from_disk tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> lemmatizer.from_disk("/path/to/lemmatizer")
|
|
> ```
|
|
|
|
| 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 `EditTreeLemmatizer` object. ~~EditTreeLemmatizer~~ |
|
|
|
|
## EditTreeLemmatizer.to_bytes {#to_bytes tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> lemmatizer_bytes = lemmatizer.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 `EditTreeLemmatizer` object. ~~bytes~~ |
|
|
|
|
## EditTreeLemmatizer.from_bytes {#from_bytes tag="method"}
|
|
|
|
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> lemmatizer_bytes = lemmatizer.to_bytes()
|
|
> lemmatizer = nlp.add_pipe("trainable_lemmatizer", name="lemmatizer")
|
|
> lemmatizer.from_bytes(lemmatizer_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 `EditTreeLemmatizer` object. ~~EditTreeLemmatizer~~ |
|
|
|
|
## EditTreeLemmatizer.labels {#labels tag="property"}
|
|
|
|
The labels currently added to the component.
|
|
|
|
<Infobox variant="warning" title="Interpretability of the labels">
|
|
|
|
The `EditTreeLemmatizer` labels are not useful by themselves, since they are
|
|
identifiers of edit trees.
|
|
|
|
</Infobox>
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------ |
|
|
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## EditTreeLemmatizer.label_data {#label_data tag="property" new="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
|
|
[`EditTreeLemmatizer.initialize`](/api/edittreelemmatizer#initialize) to
|
|
initialize the model with a pre-defined label set.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> labels = lemmatizer.label_data
|
|
> lemmatizer.initialize(lambda: [], nlp=nlp, labels=labels)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------------------------------------- |
|
|
| **RETURNS** | The label data added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## 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 = lemmatizer.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. |
|
|
| `trees` | The edit trees. You usually don't want to exclude this. |
|