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* document token ent_kb_id * document span kb_id * update pipeline documentation * prior and context weights as bool's instead * entitylinker api documentation * drop for both models * finish entitylinker documentation * small fixes * documentation for KB * candidate documentation * links to api pages in code * small fix * frequency examples as counts for consistency * consistent documentation about tensors returned by predict * add entity linking to usage 101 * add entity linking infobox and KB section to 101 * entity-linking in linguistic features * small typo corrections * training example and docs for entity_linker * predefined nlp and kb * revert back to similarity encodings for simplicity (for now) * set prior probabilities to 0 when excluded * code clean up * bugfix: deleting kb ID from tokens when entities were removed * refactor train el example to use either model or vocab * pretrain_kb example for example kb generation * add to training docs for KB + EL example scripts * small fixes * error numbering * ensure the language of vocab and nlp stay consistent across serialization * equality with = * avoid conflict in errors file * add error 151 * final adjustements to the train scripts - consistency * update of goldparse documentation * small corrections * push commit * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
353 lines
18 KiB
Markdown
353 lines
18 KiB
Markdown
---
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title: TextCategorizer
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tag: class
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source: spacy/pipeline/pipes.pyx
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new: 2
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---
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This class is a subclass of `Pipe` and follows the same API. The pipeline
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component is available in the [processing pipeline](/usage/processing-pipelines)
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via the ID `"textcat"`.
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## TextCategorizer.Model {#model tag="classmethod"}
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Initialize a model for the pipe. The model should implement the
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`thinc.neural.Model` API. Wrappers are under development for most major machine
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learning libraries.
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| Name | Type | Description |
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| ----------- | ------ | ------------------------------------- |
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| `**kwargs` | - | Parameters for initializing the model |
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| **RETURNS** | object | The initialized model. |
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## TextCategorizer.\_\_init\_\_ {#init tag="method"}
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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.create_pipe`](/api/language#create_pipe).
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> #### Example
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>
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> ```python
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> # Construction via create_pipe
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> textcat = nlp.create_pipe("textcat")
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> textcat = nlp.create_pipe("textcat", config={"exclusive_classes": True})
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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)
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> textcat.from_disk("/path/to/model")
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> ```
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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` | `thinc.neural.Model` / `True` | The model powering the pipeline component. If no model is supplied, the model is created when you call `begin_training`, `from_disk` or `from_bytes`. |
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| `exclusive_classes` | bool | Make categories mutually exclusive. Defaults to `False`. |
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| `architecture` | unicode | Model architecture to use, see [architectures](#architectures) for details. Defaults to `"ensemble"`. |
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| **RETURNS** | `TextCategorizer` | The newly constructed object. |
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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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## 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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> textcat = TextCategorizer(nlp.vocab)
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> doc = nlp(u"This is a sentence.")
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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 = TextCategorizer(nlp.vocab)
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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 | A stream of documents. |
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| `batch_size` | int | The number of texts to buffer. Defaults to `128`. |
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| **YIELDS** | `Doc` | Processed documents in the order of the original text. |
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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 = TextCategorizer(nlp.vocab)
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> scores, tensors = 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 | The documents to predict. |
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| **RETURNS** | tuple | A `(scores, tensors)` tuple where `scores` is the model's prediction for each document and `tensors` is the token representations used to predict the scores. Each tensor is an array with one row for each token in the 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 = TextCategorizer(nlp.vocab)
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> scores, tensors = textcat.predict([doc1, doc2])
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> textcat.set_annotations([doc1, doc2], scores, tensors)
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> ```
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| Name | Type | Description |
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| -------- | -------- | --------------------------------------------------------- |
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| `docs` | iterable | The documents to modify. |
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| `scores` | - | The scores to set, produced by `TextCategorizer.predict`. |
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| `tensors`| iterable | The token representations used to predict the scores. |
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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 = TextCategorizer(nlp.vocab)
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> losses = {}
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> optimizer = nlp.begin_training()
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> textcat.update([doc1, doc2], [gold1, gold2], losses=losses, sgd=optimizer)
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> ```
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| Name | Type | Description |
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| -------- | -------- | -------------------------------------------------------------------------------------------- |
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| `docs` | iterable | A batch of documents to learn from. |
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| `golds` | iterable | The gold-standard data. Must have the same length as `docs`. |
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| `drop` | float | The dropout rate. |
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| `sgd` | callable | The optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. |
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| `losses` | dict | Optional record of the loss during training. The value keyed by the model's name is updated. |
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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 = TextCategorizer(nlp.vocab)
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> scores = textcat.predict([doc1, doc2])
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> loss, d_loss = textcat.get_loss([doc1, doc2], [gold1, gold2], scores)
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> ```
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| Name | Type | Description |
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| ----------- | -------- | ------------------------------------------------------------ |
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| `docs` | iterable | The batch of documents. |
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| `golds` | iterable | The gold-standard data. Must have the same length as `docs`. |
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| `scores` | - | Scores representing the model's predictions. |
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| **RETURNS** | tuple | The loss and the gradient, i.e. `(loss, gradient)`. |
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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. If no model
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has been initialized yet, the model is added.
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> #### Example
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>
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> ```python
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> textcat = TextCategorizer(nlp.vocab)
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> nlp.pipeline.append(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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| `gold_tuples` | iterable | Optional gold-standard annotations from which to construct [`GoldParse`](/api/goldparse) objects. |
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| `pipeline` | list | Optional list of pipeline components that this component is part of. |
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| `sgd` | callable | An optional optimizer. Should take two arguments `weights` and `gradient`, and an optional ID. Will be created via [`TextCategorizer`](/api/textcategorizer#create_optimizer) if not set. |
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| **RETURNS** | callable | An optimizer. |
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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 = TextCategorizer(nlp.vocab)
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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** | callable | The optimizer. |
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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 = TextCategorizer(nlp.vocab)
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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. At the end of the context, the original parameters are restored. |
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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 = TextCategorizer(nlp.vocab)
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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` | unicode | The label to add. |
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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 = TextCategorizer(nlp.vocab)
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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` | unicode / `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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| `exclude` | list | 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 = TextCategorizer(nlp.vocab)
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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` | unicode / `Path` | A path to a directory. Paths may be either strings or `Path`-like objects. |
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| `exclude` | list | 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 = TextCategorizer(nlp.vocab)
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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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| `exclude` | list | 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 = TextCategorizer(nlp.vocab)
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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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| `exclude` | list | 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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