mirror of
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6f5e308d17
* Support a cfg field in transition system * Make NER 'has gold' check use right alignment for span * Pass 'negative_samples_key' property into NER transition system * Add field for negative samples to NER transition system * Check neg_key in NER has_gold * Support negative examples in NER oracle * Test for negative examples in NER * Fix name of config variable in NER * Remove vestiges of old-style partial annotation * Remove obsolete tests * Add comment noting lack of support for negative samples in parser * Additions to "neg examples" PR (#8201) * add custom error and test for deprecated format * add test for unlearning an entity * add break also for Begin's cost * add negative_samples_key property on Parser * rename * extend docs & fix some older docs issues * add subclass constructors, clean up tests, fix docs * add flaky test with ValueError if gold parse was not found * remove ValueError if n_gold == 0 * fix docstring * Hack in environment variables to try out training * Remove hack * Remove NER hack, and support 'negative O' samples * Fix O oracle * Fix transition parser * Remove 'not O' from oracle * Fix NER oracle * check for spans in both gold.ents and gold.spans and raise if so, to prevent memory access violation * use set instead of list in consistency check Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com> Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
461 lines
24 KiB
Markdown
461 lines
24 KiB
Markdown
---
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title: EntityRecognizer
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tag: class
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source: spacy/pipeline/ner.pyx
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teaser: 'Pipeline component for named entity recognition'
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api_base_class: /api/pipe
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api_string_name: ner
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api_trainable: true
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---
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A transition-based named entity recognition component. The entity recognizer
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identifies **non-overlapping labelled spans** of tokens. The transition-based
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algorithm used encodes certain assumptions that are effective for "traditional"
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named entity recognition tasks, but may not be a good fit for every span
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identification problem. Specifically, the loss function optimizes for **whole
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entity accuracy**, so if your inter-annotator agreement on boundary tokens is
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low, the component will likely perform poorly on your problem. The
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transition-based algorithm also assumes that the most decisive information about
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your entities will be close to their initial tokens. If your entities are long
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and characterized by tokens in their middle, the component will likely not be a
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good fit for your task.
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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.ner import DEFAULT_NER_MODEL
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> config = {
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> "moves": None,
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> "update_with_oracle_cut_size": 100,
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> "model": DEFAULT_NER_MODEL,
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> "incorrect_spans_key": "incorrect_spans",
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> }
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> nlp.add_pipe("ner", config=config)
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> ```
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| Setting | Description |
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| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `moves` | A list of transition names. Inferred from the data if not provided. Defaults to `None`. ~~Optional[List[str]]~~ |
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| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. Defaults to [TransitionBasedParser](/api/architectures#TransitionBasedParser). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `incorrect_spans_key` | This key refers to a `SpanGroup` in `doc.spans` that specifies incorrect spans. The NER wiill learn not to predict (exactly) those spans. Defaults to `None`. ~~Optional[str]~~ |
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```python
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%%GITHUB_SPACY/spacy/pipeline/ner.pyx
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```
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## EntityRecognizer.\_\_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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> ner = nlp.add_pipe("ner")
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>
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> # Construction via add_pipe with custom model
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> config = {"model": {"@architectures": "my_ner"}}
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> parser = nlp.add_pipe("ner", config=config)
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>
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> # Construction from class
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> from spacy.pipeline import EntityRecognizer
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> ner = EntityRecognizer(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#add_pipe).
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| Name | Description |
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| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| `moves` | A list of transition names. Inferred from the data if set to `None`, which is the default. ~~Optional[List[str]]~~ |
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| _keyword-only_ | |
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| `update_with_oracle_cut_size` | During training, cut long sequences into shorter segments by creating intermediate states based on the gold-standard history. The model is not very sensitive to this parameter, so you usually won't need to change it. Defaults to `100`. ~~int~~ |
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| `incorrect_spans_key` | Identifies spans that are known to be incorrect entity annotations. The incorrect entity annotations can be stored in the span group, under this key. Defaults to `None`. ~~Optional[str]~~ |
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## EntityRecognizer.\_\_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/entityrecognizer#call) and
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[`pipe`](/api/entityrecognizer#pipe) delegate to the
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[`predict`](/api/entityrecognizer#predict) and
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[`set_annotations`](/api/entityrecognizer#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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> ner = nlp.add_pipe("ner")
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> # This usually happens under the hood
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> processed = ner(doc)
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> ```
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| Name | Description |
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| ----------- | -------------------------------- |
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| `doc` | The document to process. ~~Doc~~ |
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| **RETURNS** | The processed document. ~~Doc~~ |
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## EntityRecognizer.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/entityrecognizer#call) and
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[`pipe`](/api/entityrecognizer#pipe) delegate to the
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[`predict`](/api/entityrecognizer#predict) and
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[`set_annotations`](/api/entityrecognizer#set_annotations) methods.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> for doc in ner.pipe(docs, batch_size=50):
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> pass
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------- |
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| `docs` | A stream of documents. ~~Iterable[Doc]~~ |
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| _keyword-only_ | |
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| `batch_size` | The number of documents to buffer. Defaults to `128`. ~~int~~ |
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| **YIELDS** | The processed documents in order. ~~Doc~~ |
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## EntityRecognizer.initialize {#initialize tag="method" new="3"}
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Initialize the component for training. `get_examples` should be a function that
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returns an iterable of [`Example`](/api/example) objects. The data examples are
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used to **initialize the model** of the component and can either be the full
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training data or a representative sample. Initialization includes validating the
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network,
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[inferring missing shapes](https://thinc.ai/docs/usage-models#validation) and
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setting up the label scheme based on the data. This method is typically called
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by [`Language.initialize`](/api/language#initialize) and lets you customize
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arguments it receives via the
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[`[initialize.components]`](/api/data-formats#config-initialize) block in the
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config.
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<Infobox variant="warning" title="Changed in v3.0" id="begin_training">
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This method was previously called `begin_training`.
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</Infobox>
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> ner.initialize(lambda: [], nlp=nlp)
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> ```
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>
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> ```ini
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> ### config.cfg
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> [initialize.components.ner]
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>
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> [initialize.components.ner.labels]
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> @readers = "spacy.read_labels.v1"
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> path = "corpus/labels/ner.json
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> ```
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| Name | Description |
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| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `get_examples` | Function that returns gold-standard annotations in the form of [`Example`](/api/example) objects. ~~Callable[[], Iterable[Example]]~~ |
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| _keyword-only_ | |
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| `nlp` | The current `nlp` object. Defaults to `None`. ~~Optional[Language]~~ |
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| `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[Dict[str, Dict[str, int]]]~~ |
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## EntityRecognizer.predict {#predict tag="method"}
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Apply the component's model to a batch of [`Doc`](/api/doc) objects, without
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modifying them.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> scores = ner.predict([doc1, doc2])
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------- |
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| `docs` | The documents to predict. ~~Iterable[Doc]~~ |
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| **RETURNS** | A helper class for the parse state (internal). ~~StateClass~~ |
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## EntityRecognizer.set_annotations {#set_annotations tag="method"}
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Modify a batch of [`Doc`](/api/doc) objects, using pre-computed scores.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> scores = ner.predict([doc1, doc2])
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> ner.set_annotations([doc1, doc2], scores)
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> ```
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| Name | Description |
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| -------- | ------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | The documents to modify. ~~Iterable[Doc]~~ |
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| `scores` | The scores to set, produced by `EntityRecognizer.predict`. Returns an internal helper class for the parse state. ~~List[StateClass]~~ |
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## EntityRecognizer.update {#update tag="method"}
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Learn from a batch of [`Example`](/api/example) objects, updating the pipe's
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model. Delegates to [`predict`](/api/entityrecognizer#predict) and
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[`get_loss`](/api/entityrecognizer#get_loss).
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> optimizer = nlp.initialize()
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> losses = ner.update(examples, sgd=optimizer)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------ |
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| `examples` | A batch of [`Example`](/api/example) objects to learn from. ~~Iterable[Example]~~ |
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| _keyword-only_ | |
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| `drop` | The dropout rate. ~~float~~ |
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| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
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| `losses` | Optional record of the loss during training. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
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| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
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## EntityRecognizer.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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> ner = nlp.add_pipe("ner")
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> scores = ner.predict([eg.predicted for eg in examples])
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> loss, d_loss = ner.get_loss(examples, scores)
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> ```
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| Name | Description |
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| ----------- | --------------------------------------------------------------------------- |
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| `examples` | The batch of examples. ~~Iterable[Example]~~ |
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| `scores` | Scores representing the model's predictions. ~~StateClass~~ |
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| **RETURNS** | The loss and the gradient, i.e. `(loss, gradient)`. ~~Tuple[float, float]~~ |
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## EntityRecognizer.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 = ner.score(examples)
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> ```
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| Name | Description |
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| ----------- | --------------------------------------------------------- |
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| `examples` | The examples to score. ~~Iterable[Example]~~ |
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| **RETURNS** | The scores. ~~Dict[str, Union[float, Dict[str, float]]]~~ |
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## EntityRecognizer.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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> ner = nlp.add_pipe("ner")
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> optimizer = ner.create_optimizer()
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> ```
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| Name | Description |
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| ----------- | ---------------------------- |
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| **RETURNS** | The optimizer. ~~Optimizer~~ |
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## EntityRecognizer.use_params {#use_params tag="method, contextmanager"}
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Modify the pipe's model, to use the given parameter values. At the end of the
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context, the original parameters are restored.
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> #### Example
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>
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> ```python
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> ner = EntityRecognizer(nlp.vocab)
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> with ner.use_params(optimizer.averages):
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> ner.to_disk("/best_model")
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> ```
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| Name | Description |
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| -------- | -------------------------------------------------- |
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| `params` | The parameter values to use in the model. ~~dict~~ |
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## EntityRecognizer.add_label {#add_label tag="method"}
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Add a new label to the pipe. Note that you don't have to call this method if you
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provide a **representative data sample** to the [`initialize`](#initialize)
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method. In this case, all labels found in the sample will be automatically added
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to the model, and the output dimension will be
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[inferred](/usage/layers-architectures#thinc-shape-inference) automatically.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> ner.add_label("MY_LABEL")
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> ```
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| Name | Description |
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| ----------- | ----------------------------------------------------------- |
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| `label` | The label to add. ~~str~~ |
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| **RETURNS** | `0` if the label is already present, otherwise `1`. ~~int~~ |
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## EntityRecognizer.set_output {#set_output tag="method"}
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Change the output dimension of the component's model by calling the model's
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attribute `resize_output`. This is a function that takes the original model and
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the new output dimension `nO`, and changes the model in place. When resizing an
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already trained model, care should be taken to avoid the "catastrophic
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forgetting" problem.
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> ner.set_output(512)
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> ```
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| Name | Description |
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| ---- | --------------------------------- |
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| `nO` | The new output dimension. ~~int~~ |
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## EntityRecognizer.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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> ner = nlp.add_pipe("ner")
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> ner.to_disk("/path/to/ner")
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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| `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]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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## EntityRecognizer.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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> ner = nlp.add_pipe("ner")
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> ner.from_disk("/path/to/ner")
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> ```
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| Name | Description |
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| -------------- | ----------------------------------------------------------------------------------------------- |
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| `path` | A path to a directory. Paths may be either strings or `Path`-like objects. ~~Union[str, Path]~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The modified `EntityRecognizer` object. ~~EntityRecognizer~~ |
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## EntityRecognizer.to_bytes {#to_bytes tag="method"}
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> #### Example
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>
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> ```python
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> ner = nlp.add_pipe("ner")
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> ner_bytes = ner.to_bytes()
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> ```
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Serialize the pipe to a bytestring.
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The serialized form of the `EntityRecognizer` object. ~~bytes~~ |
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## EntityRecognizer.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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> ner_bytes = ner.to_bytes()
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> ner = nlp.add_pipe("ner")
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> ner.from_bytes(ner_bytes)
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> ```
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| Name | Description |
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| -------------- | ------------------------------------------------------------------------------------------- |
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| `bytes_data` | The data to load from. ~~bytes~~ |
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| _keyword-only_ | |
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| `exclude` | String names of [serialization fields](#serialization-fields) to exclude. ~~Iterable[str]~~ |
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| **RETURNS** | The `EntityRecognizer` object. ~~EntityRecognizer~~ |
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## EntityRecognizer.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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> ner.add_label("MY_LABEL")
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> assert "MY_LABEL" in ner.labels
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------ |
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| **RETURNS** | The labels added to the component. ~~Tuple[str, ...]~~ |
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## EntityRecognizer.label_data {#label_data tag="property" new="3"}
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The labels currently added to the component and their internal meta information.
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This is the data generated by [`init labels`](/api/cli#init-labels) and used by
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[`EntityRecognizer.initialize`](/api/entityrecognizer#initialize) to initialize
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the model with a pre-defined label set.
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> #### Example
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>
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> ```python
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> labels = ner.label_data
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> ner.initialize(lambda: [], nlp=nlp, labels=labels)
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> ```
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| Name | Description |
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| ----------- | ------------------------------------------------------------------------------- |
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| **RETURNS** | The label data added to the component. ~~Dict[str, Dict[str, Dict[str, int]]]~~ |
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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 = ner.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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