TextCategorizer
classv2This class is a subclass of Pipe and follows the same API. The pipeline
component is available in the processing pipeline
via the ID "textcat".
TextCategorizer.Model classmethod
Initialize a model for the pipe. The model should implement the
thinc.neural.Model API. Wrappers are under development for most major machine
learning libraries.
| Name | Type | Description |
|---|---|---|
**kwargs | - | Parameters for initializing the model |
| RETURNS | object | The initialized model. |
TextCategorizer.__init__ method
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.create_pipe.
| Name | Type | Description |
|---|---|---|
vocab | Vocab | The shared vocabulary. |
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. |
exclusive_classes | bool | Make categories mutually exclusive. Defaults to False. |
architecture | unicode | Model architecture to use, see architectures for details. Defaults to "ensemble". |
| RETURNS | TextCategorizer | The newly constructed object. |
Architectures v2.1
Text classification models can be used to solve a wide variety of problems.
Differences in text length, number of labels, difficulty, and runtime
performance constraints mean that no single algorithm performs well on all types
of problems. To handle a wider variety of problems, the TextCategorizer object
allows configuration of its model architecture, using the architecture keyword
argument.
| Name | Description |
|---|---|
"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. |
"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. |
"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. |
TextCategorizer.__call__ 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__ and pipe
delegate to the predict and
set_annotations methods.
| Name | Type | Description |
|---|---|---|
doc | Doc | The document to process. |
| RETURNS | Doc | The processed document. |
TextCategorizer.pipe 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__ and
pipe delegate to the
predict and
set_annotations methods.
| Name | Type | Description |
|---|---|---|
stream | iterable | A stream of documents. |
batch_size | int | The number of texts to buffer. Defaults to 128. |
| YIELDS | Doc | Processed documents in the order of the original text. |
TextCategorizer.predict method
Apply the pipeline’s model to a batch of docs, without modifying them.
| Name | Type | Description |
|---|---|---|
docs | iterable | The documents to predict. |
| 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. |
TextCategorizer.set_annotations method
Modify a batch of documents, using pre-computed scores.
| Name | Type | Description |
|---|---|---|
docs | iterable | The documents to modify. |
scores | - | The scores to set, produced by TextCategorizer.predict. |
tensors | iterable | The token representations used to predict the scores. |
TextCategorizer.update method
Learn from a batch of documents and gold-standard information, updating the
pipe’s model. Delegates to predict and
get_loss.
| Name | Type | Description |
|---|---|---|
docs | iterable | A batch of documents to learn from. |
golds | iterable | The gold-standard data. Must have the same length as docs. |
drop | float | The dropout rate. |
sgd | callable | The optimizer. Should take two arguments weights and gradient, and an optional ID. |
losses | dict | Optional record of the loss during training. The value keyed by the model’s name is updated. |
TextCategorizer.get_loss method
Find the loss and gradient of loss for the batch of documents and their predicted scores.
| Name | Type | Description |
|---|---|---|
docs | iterable | The batch of documents. |
golds | iterable | The gold-standard data. Must have the same length as docs. |
scores | - | Scores representing the model’s predictions. |
| RETURNS | tuple | The loss and the gradient, i.e. (loss, gradient). |
TextCategorizer.begin_training method
Initialize the pipe for training, using data examples if available. If no model has been initialized yet, the model is added.
| Name | Type | Description |
|---|---|---|
gold_tuples | iterable | Optional gold-standard annotations from which to construct GoldParse objects. |
pipeline | list | Optional list of pipeline components that this component is part of. |
sgd | callable | An optional optimizer. Should take two arguments weights and gradient, and an optional ID. Will be created via TextCategorizer if not set. |
| RETURNS | callable | An optimizer. |
TextCategorizer.create_optimizer method
Create an optimizer for the pipeline component.
| Name | Type | Description |
|---|---|---|
| RETURNS | callable | The optimizer. |
TextCategorizer.use_params methodcontextmanager
Modify the pipe’s model, to use the given parameter values.
| Name | Type | Description |
|---|---|---|
params | dict | The parameter values to use in the model. At the end of the context, the original parameters are restored. |
TextCategorizer.add_label method
Add a new label to the pipe.
| Name | Type | Description |
|---|---|---|
label | unicode | The label to add. |
TextCategorizer.to_disk method
Serialize the pipe to disk.
| Name | Type | Description |
|---|---|---|
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. |
exclude | list | String names of serialization fields to exclude. |
TextCategorizer.from_disk method
Load the pipe from disk. Modifies the object in place and returns it.
| Name | Type | Description |
|---|---|---|
path | unicode / Path | A path to a directory. Paths may be either strings or Path-like objects. |
exclude | list | String names of serialization fields to exclude. |
| RETURNS | TextCategorizer | The modified TextCategorizer object. |
TextCategorizer.to_bytes method
Serialize the pipe to a bytestring.
| Name | Type | Description |
|---|---|---|
exclude | list | String names of serialization fields to exclude. |
| RETURNS | bytes | The serialized form of the TextCategorizer object. |
TextCategorizer.from_bytes method
Load the pipe from a bytestring. Modifies the object in place and returns it.
| Name | Type | Description |
|---|---|---|
bytes_data | bytes | The data to load from. |
exclude | list | String names of serialization fields to exclude. |
| RETURNS | TextCategorizer | The TextCategorizer object. |
TextCategorizer.labels property
The labels currently added to the component.
| Name | Type | Description |
|---|---|---|
| RETURNS | tuple | The labels added to the component. |
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.
| Name | Description |
|---|---|
vocab | The shared 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. |