mirror of https://github.com/explosion/spaCy.git
420 lines
22 KiB
Markdown
420 lines
22 KiB
Markdown
---
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title: TextCategorizer
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tag: class
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source: spacy/pipeline/textcat.py
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new: 2
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teaser: 'Pipeline component for text classification'
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api_base_class: /api/pipe
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api_string_name: textcat
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api_trainable: true
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---
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## Config and implementation {#config}
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The default config is defined by the pipeline component factory and describes
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how the component should be configured. You can override its settings via the
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`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
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[`config.cfg` for training](/usage/training#config). See the
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[model architectures](/api/architectures) documentation for details on the
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architectures and their arguments and hyperparameters.
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> #### Example
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>
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> ```python
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> from spacy.pipeline.textcat import DEFAULT_TEXTCAT_MODEL
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> config = {
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> "labels": [],
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> "model": DEFAULT_TEXTCAT_MODEL,
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> }
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> nlp.add_pipe("textcat", config=config)
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> ```
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| Setting | Type | Description | Default |
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| -------- | ------------------------------------------ | ------------------ | ----------------------------------------------------- |
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| `labels` | `Iterable[str]` | The labels to use. | `[]` |
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| `model` | [`Model`](https://thinc.ai/docs/api-model) | The model to use. | [TextCatEnsemble](/api/architectures#TextCatEnsemble) |
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```python
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https://github.com/explosion/spaCy/blob/develop/spacy/pipeline/textcat.py
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```
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## TextCategorizer.\_\_init\_\_ {#init tag="method"}
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> #### Example
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>
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> ```python
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> # Construction via add_pipe with default model
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> textcat = nlp.add_pipe("textcat")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_textcat"}}
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> parser = nlp.add_pipe("textcat", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import TextCategorizer
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> textcat = TextCategorizer(nlp.vocab, model)
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> ```
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Create a new pipeline instance. In your application, you would normally use a
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shortcut for this and instantiate the component using its string name and
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[`nlp.add_pipe`](/api/language#create_pipe).
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| Name | Type | Description |
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| -------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------- |
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| `vocab` | `Vocab` | The shared vocabulary. |
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| `model` | [`Model`](https://thinc.ai/docs/api-model) | The Thinc [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. |
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| `name` | str | String name of the component instance. Used to add entries to the `losses` during training. |
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| _keyword-only_ | | |
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| `labels` | `Iterable[str]` | The labels to use. |
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<!-- TODO move to config page
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### Architectures {#architectures new="2.1"}
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Text classification models can be used to solve a wide variety of problems.
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Differences in text length, number of labels, difficulty, and runtime
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performance constraints mean that no single algorithm performs well on all types
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of problems. To handle a wider variety of problems, the `TextCategorizer` object
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allows configuration of its model architecture, using the `architecture` keyword
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argument.
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `"ensemble"` | **Default:** Stacked ensemble of a bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. The "ngram_size" and "attr" arguments can be used to configure the feature extraction for the bag-of-words model. |
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| `"simple_cnn"` | A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network. This architecture is usually less accurate than the ensemble, but runs faster. |
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| `"bow"` | An ngram "bag-of-words" model. This architecture should run much faster than the others, but may not be as accurate, especially if texts are short. The features extracted can be controlled using the keyword arguments `ngram_size` and `attr`. For instance, `ngram_size=3` and `attr="lower"` would give lower-cased unigram, trigram and bigram features. 2, 3 or 4 are usually good choices of ngram size. |
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-->
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## TextCategorizer.\_\_call\_\_ {#call tag="method"}
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Apply the pipe to one document. The document is modified in place, and returned.
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This usually happens under the hood when the `nlp` object is called on a text
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and all pipeline components are applied to the `Doc` in order. Both
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[`__call__`](/api/textcategorizer#call) and [`pipe`](/api/textcategorizer#pipe)
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delegate to the [`predict`](/api/textcategorizer#predict) and
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[`set_annotations`](/api/textcategorizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> doc = nlp("This is a sentence.")
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> textcat = nlp.add_pipe("textcat")
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> # This usually happens under the hood
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> processed = textcat(doc)
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ------------------------ |
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| `doc` | `Doc` | The document to process. |
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| **RETURNS** | `Doc` | The processed document. |
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## TextCategorizer.pipe {#pipe tag="method"}
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Apply the pipe to a stream of documents. This usually happens under the hood
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when the `nlp` object is called on a text and all pipeline components are
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applied to the `Doc` in order. Both [`__call__`](/api/textcategorizer#call) and
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[`pipe`](/api/textcategorizer#pipe) delegate to the
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[`predict`](/api/textcategorizer#predict) and
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[`set_annotations`](/api/textcategorizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> for doc in textcat.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Type | Description |
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| -------------- | --------------- | ----------------------------------------------------- |
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| `stream` | `Iterable[Doc]` | A stream of documents. |
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| _keyword-only_ | | |
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| `batch_size` | int | The number of documents to buffer. Defaults to `128`. |
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| **YIELDS** | `Doc` | The processed documents in order. |
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## TextCategorizer.begin_training {#begin_training tag="method"}
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Initialize the pipe for training, using data examples if available. Returns an
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[`Optimizer`](https://thinc.ai/docs/api-optimizers) object.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> optimizer = textcat.begin_training(pipeline=nlp.pipeline)
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> ```
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| Name | Type | Description |
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| -------------- | --------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
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| `get_examples` | `Callable[[], Iterable[Example]]` | Optional function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. |
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| _keyword-only_ | | |
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| `pipeline` | `List[Tuple[str, Callable]]` | Optional list of pipeline components that this component is part of. |
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| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | An optional optimizer. Will be created via [`create_optimizer`](/api/textcategorizer#create_optimizer) if not set. |
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| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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## TextCategorizer.predict {#predict tag="method"}
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Apply the pipeline's model to a batch of docs, without modifying them.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> scores = textcat.predict([doc1, doc2])
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> ```
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| Name | Type | Description |
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| ----------- | --------------- | ----------------------------------------- |
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| `docs` | `Iterable[Doc]` | The documents to predict. |
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| **RETURNS** | - | The model's prediction for each document. |
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## TextCategorizer.set_annotations {#set_annotations tag="method"}
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Modify a batch of documents, using pre-computed scores.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> scores = textcat.predict(docs)
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> textcat.set_annotations(docs, scores)
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> ```
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| Name | Type | Description |
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| -------- | --------------- | --------------------------------------------------------- |
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| `docs` | `Iterable[Doc]` | The documents to modify. |
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| `scores` | - | The scores to set, produced by `TextCategorizer.predict`. |
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## TextCategorizer.update {#update tag="method"}
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Learn from a batch of documents and gold-standard information, updating the
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pipe's model. Delegates to [`predict`](/api/textcategorizer#predict) and
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[`get_loss`](/api/textcategorizer#get_loss).
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> optimizer = nlp.begin_training()
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> losses = textcat.update(examples, sgd=optimizer)
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> ```
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| Name | Type | Description |
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| ----------------- | --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
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| _keyword-only_ | | |
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| `drop` | float | The dropout rate. |
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| `set_annotations` | bool | Whether or not to update the `Example` objects with the predictions, delegating to [`set_annotations`](/api/textcategorizer#set_annotations). |
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| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## TextCategorizer.rehearse {#rehearse tag="method,experimental"}
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Perform a "rehearsal" update from a batch of data. Rehearsal updates teach the
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current model to make predictions similar to an initial model, to try to address
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the "catastrophic forgetting" problem. This feature is experimental.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> optimizer = nlp.resume_training()
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> losses = textcat.rehearse(examples, sgd=optimizer)
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> ```
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| Name | Type | Description |
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| -------------- | --------------------------------------------------- | ----------------------------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | A batch of [`Example`](/api/example) objects to learn from. |
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| _keyword-only_ | | |
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| `drop` | float | The dropout rate. |
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| `sgd` | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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| `losses` | `Dict[str, float]` | Optional record of the loss during training. Updated using the component name as the key. |
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| **RETURNS** | `Dict[str, float]` | The updated `losses` dictionary. |
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## TextCategorizer.get_loss {#get_loss tag="method"}
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Find the loss and gradient of loss for the batch of documents and their
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predicted scores.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> scores = textcat.predict([eg.predicted for eg in examples])
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> loss, d_loss = textcat.get_loss(examples, scores)
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> ```
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| Name | Type | Description |
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| ----------- | --------------------- | --------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The batch of examples. |
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| `scores` | - | Scores representing the model's predictions. |
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| **RETURNS** | `Tuple[float, float]` | The loss and the gradient, i.e. `(loss, gradient)`. |
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## TextCategorizer.score {#score tag="method" new="3"}
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Score a batch of examples.
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> #### Example
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>
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> ```python
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> scores = textcat.score(examples)
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> ```
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| Name | Type | Description |
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| ---------------- | ------------------- | ---------------------------------------------------------------------- |
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| `examples` | `Iterable[Example]` | The examples to score. |
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| _keyword-only_ | | |
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| `positive_label` | str | Optional positive label. |
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| **RETURNS** | `Dict[str, Any]` | The scores, produced by [`Scorer.score_cats`](/api/scorer#score_cats). |
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## TextCategorizer.create_optimizer {#create_optimizer tag="method"}
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Create an optimizer for the pipeline component.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> optimizer = textcat.create_optimizer()
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> ```
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| Name | Type | Description |
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| ----------- | --------------------------------------------------- | -------------- |
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| **RETURNS** | [`Optimizer`](https://thinc.ai/docs/api-optimizers) | The optimizer. |
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## TextCategorizer.add_label {#add_label tag="method"}
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Add a new label to the pipe.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> textcat.add_label("MY_LABEL")
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> ```
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| Name | Type | Description |
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| ----------- | ---- | --------------------------------------------------- |
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| `label` | str | The label to add. |
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| **RETURNS** | int | `0` if the label is already present, otherwise `1`. |
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## TextCategorizer.use_params {#use_params tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> with textcat.use_params(optimizer.averages):
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> textcat.to_disk("/best_model")
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> ```
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| Name | Type | Description |
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| -------- | ---- | ----------------------------------------- |
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| `params` | dict | The parameter values to use in the model. |
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## TextCategorizer.to_disk {#to_disk tag="method"}
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Serialize the pipe to disk.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> textcat.to_disk("/path/to/textcat")
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> ```
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| Name | Type | Description |
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| -------------- | --------------- | --------------------------------------------------------------------------------------------------------------------- |
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| `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. |
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| _keyword-only_ | | |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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## TextCategorizer.from_disk {#from_disk tag="method"}
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Load the pipe from disk. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> textcat.from_disk("/path/to/textcat")
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> ```
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| Name | Type | Description |
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| -------------- | ----------------- | -------------------------------------------------------------------------- |
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| `path` | str / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
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| _keyword-only_ | | |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | `TextCategorizer` | The modified `TextCategorizer` object. |
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## TextCategorizer.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> textcat = nlp.add_pipe("textcat")
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> textcat_bytes = textcat.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Type | Description |
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| -------------- | --------------- | ------------------------------------------------------------------------- |
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| _keyword-only_ | | |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | bytes | The serialized form of the `TextCategorizer` object. |
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## TextCategorizer.from_bytes {#from_bytes tag="method"}
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Load the pipe from a bytestring. Modifies the object in place and returns it.
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> #### Example
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>
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> ```python
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> textcat_bytes = textcat.to_bytes()
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> textcat = nlp.add_pipe("textcat")
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> textcat.from_bytes(textcat_bytes)
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> ```
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| Name | Type | Description |
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| -------------- | ----------------- | ------------------------------------------------------------------------- |
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| `bytes_data` | bytes | The data to load from. |
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| _keyword-only_ | | |
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| `exclude` | `Iterable[str]` | String names of [serialization fields](#serialization-fields) to exclude. |
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| **RETURNS** | `TextCategorizer` | The `TextCategorizer` object. |
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## TextCategorizer.labels {#labels tag="property"}
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The labels currently added to the component.
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> #### Example
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>
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> ```python
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> textcat.add_label("MY_LABEL")
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> assert "MY_LABEL" in textcat.labels
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> ```
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| Name | Type | Description |
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| ----------- | ----- | ---------------------------------- |
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| **RETURNS** | tuple | The labels added to the component. |
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## Serialization fields {#serialization-fields}
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During serialization, spaCy will export several data fields used to restore
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different aspects of the object. If needed, you can exclude them from
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serialization by passing in the string names via the `exclude` argument.
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> #### Example
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>
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> ```python
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> data = textcat.to_disk("/path", exclude=["vocab"])
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> ```
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| Name | Description |
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| ------- | -------------------------------------------------------------- |
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| `vocab` | The shared [`Vocab`](/api/vocab). |
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| `cfg` | The config file. You usually don't want to exclude this. |
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| `model` | The binary model data. You usually don't want to exclude this. |
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