mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-28 02:46:35 +03:00
e680efc7cc
* bump to 3.0.0rc4 * do set_annotations in component update calls * update docs and remove set_annotations flag * fix EL test
457 lines
22 KiB
Markdown
457 lines
22 KiB
Markdown
---
|
|
title: TextCategorizer
|
|
tag: class
|
|
source: spacy/pipeline/textcat.py
|
|
new: 2
|
|
teaser: 'Pipeline component for single-label text classification'
|
|
api_base_class: /api/pipe
|
|
api_string_name: textcat
|
|
api_trainable: true
|
|
---
|
|
|
|
The text categorizer predicts **categories over a whole document**. It can learn
|
|
one or more labels, and the labels are mutually exclusive - there is exactly one
|
|
true label per document.
|
|
|
|
## 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.textcat import DEFAULT_SINGLE_TEXTCAT_MODEL
|
|
> config = {
|
|
> "threshold": 0.5,
|
|
> "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
|
|
> }
|
|
> nlp.add_pipe("textcat", config=config)
|
|
> ```
|
|
|
|
| Setting | Description |
|
|
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
|
| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
|
|
| `model` | A model instance that predicts scores for each category. Defaults to [TextCatEnsemble](/api/architectures#TextCatEnsemble). ~~Model[List[Doc], List[Floats2d]]~~ |
|
|
|
|
```python
|
|
%%GITHUB_SPACY/spacy/pipeline/textcat.py
|
|
```
|
|
|
|
## TextCategorizer.\_\_init\_\_ {#init tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> # Construction via add_pipe with default model
|
|
> textcat = nlp.add_pipe("textcat")
|
|
>
|
|
> # Construction via add_pipe with custom model
|
|
> config = {"model": {"@architectures": "my_textcat"}}
|
|
> parser = nlp.add_pipe("textcat", config=config)
|
|
>
|
|
> # Construction from class
|
|
> from spacy.pipeline import TextCategorizer
|
|
> textcat = TextCategorizer(nlp.vocab, model, threshold=0.5)
|
|
> ```
|
|
|
|
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` | The Thinc [`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_ | |
|
|
| `threshold` | Cutoff to consider a prediction "positive", relevant when printing accuracy results. ~~float~~ |
|
|
|
|
## TextCategorizer.\_\_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/textcategorizer#call) and [`pipe`](/api/textcategorizer#pipe)
|
|
delegate to the [`predict`](/api/textcategorizer#predict) and
|
|
[`set_annotations`](/api/textcategorizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> doc = nlp("This is a sentence.")
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> # This usually happens under the hood
|
|
> processed = textcat(doc)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | -------------------------------- |
|
|
| `doc` | The document to process. ~~Doc~~ |
|
|
| **RETURNS** | The processed document. ~~Doc~~ |
|
|
|
|
## TextCategorizer.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/textcategorizer#call) and
|
|
[`pipe`](/api/textcategorizer#pipe) delegate to the
|
|
[`predict`](/api/textcategorizer#predict) and
|
|
[`set_annotations`](/api/textcategorizer#set_annotations) methods.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> for doc in textcat.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~~ |
|
|
|
|
## TextCategorizer.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.
|
|
|
|
<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
|
|
|
|
This method was previously called `begin_training`.
|
|
|
|
</Infobox>
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat.initialize(lambda: [], nlp=nlp)
|
|
> ```
|
|
>
|
|
> ```ini
|
|
> ### config.cfg
|
|
> [initialize.components.textcat]
|
|
> positive_label = "POS"
|
|
>
|
|
> [initialize.components.textcat.labels]
|
|
> @readers = "spacy.read_labels.v1"
|
|
> path = "corpus/labels/textcat.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]]~~ |
|
|
| `positive_label` | The positive label for a binary task with exclusive classes, None otherwise and by default. ~~Optional[str]~~ |
|
|
|
|
## TextCategorizer.predict {#predict tag="method"}
|
|
|
|
Apply the component's model to a batch of [`Doc`](/api/doc) objects without
|
|
modifying them.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> scores = textcat.predict([doc1, doc2])
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------- |
|
|
| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
|
|
| **RETURNS** | The model's prediction for each document. |
|
|
|
|
## TextCategorizer.set_annotations {#set_annotations tag="method"}
|
|
|
|
Modify a batch of [`Doc`](/api/doc) objects using pre-computed scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> scores = textcat.predict(docs)
|
|
> textcat.set_annotations(docs, scores)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | --------------------------------------------------------- |
|
|
| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
|
|
| `scores` | The scores to set, produced by `TextCategorizer.predict`. |
|
|
|
|
## TextCategorizer.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/textcategorizer#predict),
|
|
[`get_loss`](/api/textcategorizer#get_loss) and
|
|
[`set_annotations`](/api/textcategorizer#set_annotations).
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> optimizer = nlp.initialize()
|
|
> losses = textcat.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]~~ |
|
|
|
|
## TextCategorizer.rehearse {#rehearse tag="method,experimental" new="3"}
|
|
|
|
Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
|
|
current model to make predictions similar to an initial model to try to address
|
|
the "catastrophic forgetting" problem. This feature is experimental.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> optimizer = nlp.resume_training()
|
|
> losses = textcat.rehearse(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]~~ |
|
|
|
|
## TextCategorizer.get_loss {#get_loss tag="method"}
|
|
|
|
Find the loss and gradient of loss for the batch of documents and their
|
|
predicted scores.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> scores = textcat.predict([eg.predicted for eg in examples])
|
|
> loss, d_loss = textcat.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]~~ |
|
|
|
|
## TextCategorizer.score {#score tag="method" new="3"}
|
|
|
|
Score a batch of examples.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> scores = textcat.score(examples)
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ---------------- | -------------------------------------------------------------------------------------------------------------------- |
|
|
| `examples` | The examples to score. ~~Iterable[Example]~~ |
|
|
| _keyword-only_ | |
|
|
| **RETURNS** | The scores, produced by [`Scorer.score_cats`](/api/scorer#score_cats). ~~Dict[str, Union[float, Dict[str, float]]]~~ |
|
|
|
|
## TextCategorizer.create_optimizer {#create_optimizer tag="method"}
|
|
|
|
Create an optimizer for the pipeline component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> optimizer = textcat.create_optimizer()
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ---------------------------- |
|
|
| **RETURNS** | The optimizer. ~~Optimizer~~ |
|
|
|
|
## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
|
|
|
|
Modify the pipe's model to use the given parameter values.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> with textcat.use_params(optimizer.averages):
|
|
> textcat.to_disk("/best_model")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| -------- | -------------------------------------------------- |
|
|
| `params` | The parameter values to use in the model. ~~dict~~ |
|
|
|
|
## TextCategorizer.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
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat.add_label("MY_LABEL")
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ----------------------------------------------------------- |
|
|
| `label` | The label to add. ~~str~~ |
|
|
| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
|
|
|
|
## TextCategorizer.to_disk {#to_disk tag="method"}
|
|
|
|
Serialize the pipe to disk.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat.to_disk("/path/to/textcat")
|
|
> ```
|
|
|
|
| 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]~~ |
|
|
|
|
## TextCategorizer.from_disk {#from_disk tag="method"}
|
|
|
|
Load the pipe from disk. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat.from_disk("/path/to/textcat")
|
|
> ```
|
|
|
|
| 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 `TextCategorizer` object. ~~TextCategorizer~~ |
|
|
|
|
## TextCategorizer.to_bytes {#to_bytes tag="method"}
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat_bytes = textcat.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 `TextCategorizer` object. ~~bytes~~ |
|
|
|
|
## TextCategorizer.from_bytes {#from_bytes tag="method"}
|
|
|
|
Load the pipe from a bytestring. Modifies the object in place and returns it.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat_bytes = textcat.to_bytes()
|
|
> textcat = nlp.add_pipe("textcat")
|
|
> textcat.from_bytes(textcat_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 `TextCategorizer` object. ~~TextCategorizer~~ |
|
|
|
|
## TextCategorizer.labels {#labels tag="property"}
|
|
|
|
The labels currently added to the component.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> textcat.add_label("MY_LABEL")
|
|
> assert "MY_LABEL" in textcat.labels
|
|
> ```
|
|
|
|
| Name | Description |
|
|
| ----------- | ------------------------------------------------------ |
|
|
| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
|
|
|
|
## TextCategorizer.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
|
|
[`TextCategorizer.initialize`](/api/textcategorizer#initialize) to initialize
|
|
the model with a pre-defined label set.
|
|
|
|
> #### Example
|
|
>
|
|
> ```python
|
|
> labels = textcat.label_data
|
|
> textcat.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 = textcat.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. |
|