mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
494 lines
26 KiB
Markdown
494 lines
26 KiB
Markdown
---
|
|
title: SpanCategorizer
|
|
tag: class,experimental
|
|
source: spacy/pipeline/spancat.py
|
|
new: 3.1
|
|
teaser: 'Pipeline component for labeling potentially overlapping spans of text'
|
|
api_base_class: /api/pipe
|
|
api_string_name: spancat
|
|
api_trainable: true
|
|
---
|
|
|
|
A span categorizer consists of two parts: a [suggester function](#suggesters)
|
|
that proposes candidate spans, which may or may not overlap, and a labeler model
|
|
that predicts zero or more labels for each candidate.
|
|
|
|
Predicted spans will be saved in a [`SpanGroup`](/api/spangroup) on the doc.
|
|
Individual span scores can be found in `spangroup.attrs["scores"]`.
|
|
|
|
## Assigned Attributes {#assigned-attributes}
|
|
|
|
Predictions will be saved to `Doc.spans[spans_key]` as a
|
|
[`SpanGroup`](/api/spangroup). The scores for the spans in the `SpanGroup` will
|
|
be saved in `SpanGroup.attrs["scores"]`.
|
|
|
|
`spans_key` defaults to `"sc"`, but can be passed as a parameter.
|
|
|
|
| Location | Value |
|
|
| -------------------------------------- | -------------------------------------------------------- |
|
|
| `Doc.spans[spans_key]` | The annotated spans. ~~SpanGroup~~ |
|
|
| `Doc.spans[spans_key].attrs["scores"]` | The score for each span in the `SpanGroup`. ~~Floats1d~~ |
|
|
|
|
## 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.spancat import DEFAULT_SPANCAT_MODEL
|
|
> config = {
|
|
> "threshold": 0.5,
|
|
> "spans_key": "labeled_spans",
|
|
> "max_positive": None,
|
|
> "model": DEFAULT_SPANCAT_MODEL,
|
|
> "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
|
> }
|
|
> nlp.add_pipe("spancat", config=config)
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. Defaults to [`ngram_suggester`](#ngram_suggester). ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
|
|
| `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. Defaults to [SpanCategorizer](/api/architectures#SpanCategorizer). ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
|
|
| `spans_key` | Key of the [`Doc.spans`](/api/doc#spans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
|
|
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
|
|
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
|
|
| `scorer` | The scoring method. Defaults to [`Scorer.score_spans`](/api/scorer#score_spans) for `Doc.spans[spans_key]` with overlapping spans allowed. ~~Optional[Callable]~~ |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/spancat.py
|
|
```
|
|
|
|
## SpanCategorizer.\_\_init\_\_ {#init tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via add_pipe with default model
|
|
> spancat = nlp.add_pipe("spancat")
|
|
>
|
|
> # Construction via add_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_spancat"}}
|
|
> parser = nlp.add_pipe("spancat", config=config)
|
|
>
|
|
> # Construction from class
|
|
> from spacy.pipeline import SpanCategorizer
|
|
> spancat = SpanCategorizer(nlp.vocab, model, suggester)
|
|
> ```
|
|
|
|
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#create_pipe).
|
|
|
|
| Name | Description |
|
|
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
|
| `vocab` | The shared vocabulary. ~~Vocab~~ |
|
|
| `model` | A model instance that is given a a list of documents and `(start, end)` indices representing candidate span offsets. The model predicts a probability for each category for each span. ~~Model[Tuple[List[Doc], Ragged], Floats2d]~~ |
|
|
| `suggester` | A function that [suggests spans](#suggesters). Spans are returned as a ragged array with two integer columns, for the start and end positions. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
|
|
| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
|
|
| _keyword-only_ | |
|
|
| `spans_key` | Key of the [`Doc.spans`](/api/doc#sans) dict to save the spans under. During initialization and training, the component will look for spans on the reference document under the same key. Defaults to `"sc"`. ~~str~~ |
|
|
| `threshold` | Minimum probability to consider a prediction positive. Spans with a positive prediction will be saved on the Doc. Defaults to `0.5`. ~~float~~ |
|
|
| `max_positive` | Maximum number of labels to consider positive per span. Defaults to `None`, indicating no limit. ~~Optional[int]~~ |
|
|
|
|
## SpanCategorizer.\_\_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/spancategorizer#call) and [`pipe`](/api/spancategorizer#pipe)
|
|
delegate to the [`predict`](/api/spancategorizer#predict) and
|
|
[`set_annotations`](/api/spancategorizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> doc = nlp("This is a sentence.")
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> # This usually happens under the hood
|
|
> processed = spancat(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## SpanCategorizer.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/spancategorizer#call) and
|
|
[`pipe`](/api/spancategorizer#pipe) delegate to the
|
|
[`predict`](/api/spancategorizer#predict) and
|
|
[`set_annotations`](/api/spancategorizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> for doc in spancat.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~~ |
|
|
|
|
## SpanCategorizer.initialize {#initialize tag="method"}
|
|
|
|
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
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.initialize(lambda: [], nlp=nlp)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.spancat]
|
|
>
|
|
> [initialize.components.spancat.labels]
|
|
> @readers = "spacy.read_labels.v1"
|
|
> path = "corpus/labels/spancat.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]]~~ |
|
|
|
|
## SpanCategorizer.predict {#predict tag="method"}
|
|
|
|
Apply the component's model to a batch of [`Doc`](/api/doc) objects without
|
|
modifying them.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> scores = spancat.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------- |
|
|
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
|
| **RETURNS** | The model's prediction for each document. |
|
|
|
|
## SpanCategorizer.set_annotations {#set_annotations tag="method"}
|
|
|
|
Modify a batch of [`Doc`](/api/doc) objects using pre-computed scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> scores = spancat.predict(docs)
|
|
> spancat.set_annotations(docs, scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | --------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `scores` | The scores to set, produced by `SpanCategorizer.predict`. |
|
|
|
|
## SpanCategorizer.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/spancategorizer#predict) and
|
|
[`get_loss`](/api/spancategorizer#get_loss).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> optimizer = nlp.initialize()
|
|
> losses = spancat.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]~~ |
|
|
|
|
## SpanCategorizer.set_candidates {#set_candidates tag="method", new="3.3"}
|
|
|
|
Use the suggester to add a list of [`Span`](/api/span) candidates to a list of
|
|
[`Doc`](/api/doc) objects. This method is intended to be used for debugging
|
|
purposes.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.set_candidates(docs, "candidates")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------------- | -------------------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `candidates_key` | Key of the Doc.spans dict to save the candidate spans under. ~~str~~ |
|
|
|
|
## SpanCategorizer.get_loss {#get_loss tag="method"}
|
|
|
|
Find the loss and gradient of loss for the batch of documents and their
|
|
predicted scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> scores = spancat.predict([eg.predicted for eg in examples])
|
|
> loss, d_loss = spancat.get_loss(examples, scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------------- | --------------------------------------------------------------------------- |
|
|
| `examples` | The batch of examples. ~~Iterable[Example]~~ |
|
|
| `spans_scores` | Scores representing the model's predictions. ~~Tuple[Ragged, Floats2d]~~ |
|
|
| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
|
|
|
|
## SpanCategorizer.create_optimizer {#create_optimizer tag="method"}
|
|
|
|
Create an optimizer for the pipeline component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> optimizer = spancat.create_optimizer()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------- |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## SpanCategorizer.use_params {#use_params tag="method, contextmanager"}
|
|
|
|
Modify the pipe's model to use the given parameter values.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> with spancat.use_params(optimizer.averages):
|
|
> spancat.to_disk("/best_model")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | -------------------------------------------------- |
|
|
| `params` | The parameter values to use in the model. ~~dict~~ |
|
|
|
|
## SpanCategorizer.add_label {#add_label tag="method"}
|
|
|
|
Add a new label to the pipe. 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
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.add_label("MY_LABEL")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------- |
|
|
| `label` | The label to add. ~~str~~ |
|
|
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
|
|
|
|
## SpanCategorizer.to_disk {#to_disk tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.to_disk("/path/to/spancat")
|
|
> ```
|
|
|
|
| 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]~~ |
|
|
|
|
## SpanCategorizer.from_disk {#from_disk tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.from_disk("/path/to/spancat")
|
|
> ```
|
|
|
|
| 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 `SpanCategorizer` object. ~~SpanCategorizer~~ |
|
|
|
|
## SpanCategorizer.to_bytes {#to_bytes tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat_bytes = spancat.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 `SpanCategorizer` object. ~~bytes~~ |
|
|
|
|
## SpanCategorizer.from_bytes {#from_bytes tag="method"}
|
|
|
|
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat_bytes = spancat.to_bytes()
|
|
> spancat = nlp.add_pipe("spancat")
|
|
> spancat.from_bytes(spancat_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 `SpanCategorizer` object. ~~SpanCategorizer~~ |
|
|
|
|
## SpanCategorizer.labels {#labels tag="property"}
|
|
|
|
The labels currently added to the component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> spancat.add_label("MY_LABEL")
|
|
> assert "MY_LABEL" in spancat.labels
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------ |
|
|
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## SpanCategorizer.label_data {#label_data tag="property"}
|
|
|
|
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
|
|
[`SpanCategorizer.initialize`](/api/spancategorizer#initialize) to initialize
|
|
the model with a pre-defined label set.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> labels = spancat.label_data
|
|
> spancat.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 = spancat.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. |
|
|
|
|
## Suggesters {#suggesters tag="registered functions" source="spacy/pipeline/spancat.py"}
|
|
|
|
### spacy.ngram_suggester.v1 {#ngram_suggester}
|
|
|
|
> #### Example Config
|
|
>
|
|
> ```ini
|
|
> [components.spancat.suggester]
|
|
> @misc = "spacy.ngram_suggester.v1"
|
|
> sizes = [1, 2, 3]
|
|
> ```
|
|
|
|
Suggest all spans of the given lengths. Spans are returned as a ragged array of
|
|
integers. The array has two columns, indicating the start and end position.
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------------------------------------------------------------------------------------------- |
|
|
| `sizes` | The phrase lengths to suggest. For example, `[1, 2]` will suggest phrases consisting of 1 or 2 tokens. ~~List[int]~~ |
|
|
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
|
|
|
|
### spacy.ngram_range_suggester.v1 {#ngram_range_suggester}
|
|
|
|
> #### Example Config
|
|
>
|
|
> ```ini
|
|
> [components.spancat.suggester]
|
|
> @misc = "spacy.ngram_range_suggester.v1"
|
|
> min_size = 2
|
|
> max_size = 4
|
|
> ```
|
|
|
|
Suggest all spans of at least length `min_size` and at most length `max_size`
|
|
(both inclusive). Spans are returned as a ragged array of integers. The array
|
|
has two columns, indicating the start and end position.
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------------------------------------------------------- |
|
|
| `min_size` | The minimal phrase lengths to suggest (inclusive). ~~[int]~~ |
|
|
| `max_size` | The maximal phrase lengths to suggest (exclusive). ~~[int]~~ |
|
|
| **CREATES** | The suggester function. ~~Callable[[Iterable[Doc], Optional[Ops]], Ragged]~~ |
|