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279 lines
11 KiB
Markdown
279 lines
11 KiB
Markdown
---
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title: Transformers
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teaser: Using transformer models like BERT in spaCy
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menu:
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- ['Installation', 'install']
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- ['Runtime Usage', 'runtime']
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- ['Training Usage', 'training']
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---
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## Installation {#install hidden="true"}
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spaCy v3.0 lets you use almost **any statistical model** to power your pipeline.
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You can use models implemented in a variety of
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[frameworks](https://thinc.ai/docs/usage-frameworks), including TensorFlow,
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PyTorch and MXNet. To keep things sane, spaCy expects models from these
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frameworks to be wrapped with a common interface, using our machine learning
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library [Thinc](https://thinc.ai). A transformer model is just a statistical
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model, so the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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actually has very little work to do: we just have to provide a few functions
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that do the required plumbing. We also provide a pipeline component,
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[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
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you save the transformer outputs for later use.
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To use transformers with spaCy, you need the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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installed. It takes care of all the setup behind the scenes, and makes sure the
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transformer pipeline component is available to spaCy.
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```bash
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$ pip install spacy-transformers
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```
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<!-- TODO: the text below has been copied from the spacy-transformers repo and needs to be updated and adjusted -->
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## Runtime usage {#runtime}
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Transformer models can be used as **drop-in replacements** for other types of
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neural networks, so your spaCy pipeline can include them in a way that's
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completely invisible to the user. Users will download, load and use the model in
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the standard way, like any other spaCy pipeline. Instead of using the
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transformers as subnetworks directly, you can also use them via the
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[`Transformer`](/api/transformer) pipeline component.
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The `Transformer` component sets the
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[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
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which lets you access the transformers outputs at runtime.
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```bash
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$ python -m spacy download en_core_trf_lg
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```
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```python
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### Example
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import spacy
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nlp = spacy.load("en_core_trf_lg")
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for doc in nlp.pipe(["some text", "some other text"]):
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tokvecs = doc._.trf_data.tensors[-1]
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```
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You can also customize how the [`Transformer`](/api/transformer) component sets
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annotations onto the [`Doc`](/api/doc), by customizing the `annotation_setter`.
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This callback will be called with the raw input and output data for the whole
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batch, along with the batch of `Doc` objects, allowing you to implement whatever
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you need. The annotation setter is called with a batch of [`Doc`](/api/doc)
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objects and a [`FullTransformerBatch`](/api/transformer#fulltransformerbatch)
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containing the transformers data for the batch.
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```python
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def custom_annotation_setter(docs, trf_data):
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# TODO:
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...
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nlp = spacy.load("en_core_trf_lg")
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nlp.get_pipe("transformer").annotation_setter = custom_annotation_setter
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doc = nlp("This is a text")
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print() # TODO:
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```
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## Training usage {#training}
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The recommended workflow for training is to use spaCy's
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[config system](/usage/training#config), usually via the
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[`spacy train`](/api/cli#train) command. The training config defines all
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component settings and hyperparameters in one place and lets you describe a tree
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of objects by referring to creation functions, including functions you register
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yourself.
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<Project id="en_core_bert">
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The easiest way to get started is to clone a transformers-based project
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template. Swap in your data, edit the settings and hyperparameters and train,
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evaluate, package and visualize your model.
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</Project>
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The `[components]` section in the [`config.cfg`](#TODO:) describes the pipeline
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components and the settings used to construct them, including their model
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implementation. Here's a config snippet for the
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[`Transformer`](/api/transformer) component, along with matching Python code:
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> #### Python equivalent
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>
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> ```python
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> from spacy_transformers import Transformer, TransformerModel
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> from spacy_transformers.annotation_setters import null_annotation_setter
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> from spacy_transformers.span_getters import get_doc_spans
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>
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> trf = Transformer(
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> nlp.vocab,
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> TransformerModel(
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> "bert-base-cased",
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> get_spans=get_doc_spans,
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> tokenizer_config={"use_fast": True},
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> ),
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> annotation_setter=null_annotation_setter,
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> max_batch_items=4096,
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> )
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> ```
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```ini
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### config.cfg (excerpt)
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[components.transformer]
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factory = "transformer"
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max_batch_items = 4096
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[components.transformer.model]
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@architectures = "spacy-transformers.TransformerModel.v1"
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name = "bert-base-cased"
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tokenizer_config = {"use_fast": true}
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[components.transformer.model.get_spans]
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@span_getters = "doc_spans.v1"
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[components.transformer.annotation_setter]
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@annotation_setters = "spacy-transformer.null_annotation_setter.v1"
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```
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The `[components.transformer.model]` block describes the `model` argument passed
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to the transformer component. It's a Thinc
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[`Model`](https://thinc.ai/docs/api-model) object that will be passed into the
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component. Here, it references the function
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[spacy-transformers.TransformerModel.v1](/api/architectures#TransformerModel)
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registered in the [`architectures` registry](/api/top-level#registry). If a key
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in a block starts with `@`, it's **resolved to a function** and all other
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settings are passed to the function as arguments. In this case, `name`,
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`tokenizer_config` and `get_spans`.
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`get_spans` is a function that takes a batch of `Doc` object and returns lists
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of potentially overlapping `Span` objects to process by the transformer. Several
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[built-in functions](/api/transformer#span-getters) are available – for example,
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to process the whole document or individual sentences. When the config is
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resolved, the function is created and passed into the model as an argument.
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<Infobox variant="warning">
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Remember that the `config.cfg` used for training should contain **no missing
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values** and requires all settings to be defined. You don't want any hidden
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defaults creeping in and changing your results! spaCy will tell you if settings
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are missing, and you can run [`spacy debug config`](/api/cli#debug-config) with
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`--auto-fill` to automatically fill in all defaults.
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<!-- TODO: update with details on getting started with a config -->
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</Infobox>
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### Customizing the settings {#training-custom-settings}
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To change any of the settings, you can edit the `config.cfg` and re-run the
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training. To change any of the functions, like the span getter, you can replace
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the name of the referenced function – e.g. `@span_getters = "sent_spans.v1"` to
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process sentences. You can also register your own functions using the
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`span_getters` registry:
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> #### config.cfg
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>
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> ```ini
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> [components.transformer.model.get_spans]
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> @span_getters = "custom_sent_spans"
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> ```
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```python
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### code.py
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import spacy_transformers
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@spacy_transformers.registry.span_getters("custom_sent_spans")
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def configure_custom_sent_spans():
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# TODO: write custom example
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def get_sent_spans(docs):
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return [list(doc.sents) for doc in docs]
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return get_sent_spans
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```
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To resolve the config during training, spaCy needs to know about your custom
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function. You can make it available via the `--code` argument that can point to
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a Python file:
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```bash
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$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py
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```
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### Customizing the model implementations {#training-custom-model}
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The [`Transformer`](/api/transformer) component expects a Thinc
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[`Model`](https://thinc.ai/docs/api-model) object to be passed in as its `model`
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argument. You're not limited to the implementation provided by
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`spacy-transformers` – the only requirement is that your registered function
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must return an object of type `Model[List[Doc], FullTransformerBatch]`: that is,
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a Thinc model that takes a list of [`Doc`](/api/doc) objects, and returns a
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[`FullTransformerBatch`](/api/transformer#fulltransformerbatch) object with the
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transformer data.
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> #### Model type annotations
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>
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> In the documentation and code base, you may come across type annotations and
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> descriptions of [Thinc](https://thinc.ai) model types, like
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> `Model[List[Doc], List[Floats2d]]`. This so-called generic type describes the
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> layer and its input and output type – in this case, it takes a list of `Doc`
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> objects as the input and list of 2-dimensional arrays of floats as the output.
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> You can read more about defining Thinc
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> models [here](https://thinc.ai/docs/usage-models). Also see the
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> [type checking](https://thinc.ai/docs/usage-type-checking) for how to enable
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> linting in your editor to see live feedback if your inputs and outputs don't
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> match.
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The same idea applies to task models that power the **downstream components**.
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Most of spaCy's built-in model creation functions support a `tok2vec` argument,
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which should be a Thinc layer of type `Model[List[Doc], List[Floats2d]]`. This
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is where we'll plug in our transformer model, using the
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[Tok2VecListener](/api/architectures#Tok2VecListener) layer, which sneakily
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delegates to the `Transformer` pipeline component.
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```ini
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### config.cfg (excerpt) {highlight="12"}
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[components.ner]
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factory = "ner"
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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hidden_width = 128
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maxout_pieces = 3
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use_upper = false
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[nlp.pipeline.ner.model.tok2vec]
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@architectures = "spacy-transformers.Tok2VecListener.v1"
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grad_factor = 1.0
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[nlp.pipeline.ner.model.tok2vec.pooling]
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@layers = "reduce_mean.v1"
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```
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The [Tok2VecListener](/api/architectures#Tok2VecListener) layer expects a
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[pooling layer](https://thinc.ai/docs/api-layers#reduction-ops), which needs to
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be of type `Model[Ragged, Floats2d]`. This layer determines how the vector for
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each spaCy token will be computed from the zero or more source rows the token is
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aligned against. Here we use the
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[`reduce_mean`](https://thinc.ai/docs/api-layers#reduce_mean) layer, which
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averages the wordpiece rows. We could instead use `reduce_last`,
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[`reduce_max`](https://thinc.ai/docs/api-layers#reduce_max), or a custom
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function you write yourself.
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<!--TODO: reduce_last: undocumented? -->
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You can have multiple components all listening to the same transformer model,
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and all passing gradients back to it. By default, all of the gradients will be
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**equally weighted**. You can control this with the `grad_factor` setting, which
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lets you reweight the gradients from the different listeners. For instance,
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setting `grad_factor = 0` would disable gradients from one of the listeners,
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while `grad_factor = 2.0` would multiply them by 2. This is similar to having a
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custom learning rate for each component. Instead of a constant, you can also
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provide a schedule, allowing you to freeze the shared parameters at the start of
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training.
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