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967 lines
44 KiB
Markdown
967 lines
44 KiB
Markdown
---
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title: Training Models
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next: /usage/projects
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menu:
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- ['Introduction', 'basics']
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- ['Quickstart', 'quickstart']
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- ['Config System', 'config']
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- ['Custom Functions', 'custom-functions']
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- ['Transfer Learning', 'transfer-learning']
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- ['Parallel Training', 'parallel-training']
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- ['Internal API', 'api']
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---
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## Introduction to training models {#basics hidden="true"}
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import Training101 from 'usage/101/\_training.md'
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<Training101 />
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<Infobox title="Tip: Try the Prodigy annotation tool">
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[](https://prodi.gy)
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If you need to label a lot of data, check out [Prodigy](https://prodi.gy), a
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new, active learning-powered annotation tool we've developed. Prodigy is fast
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and extensible, and comes with a modern **web application** that helps you
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collect training data faster. It integrates seamlessly with spaCy, pre-selects
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the **most relevant examples** for annotation, and lets you train and evaluate
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ready-to-use spaCy models.
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</Infobox>
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## Quickstart {#quickstart tag="new"}
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The recommended way to train your spaCy models is via the
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[`spacy train`](/api/cli#train) command on the command line. It only needs a
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single [`config.cfg`](#config) **configuration file** that includes all settings
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and hyperparameters. You can optionally [overwritten](#config-overrides)
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settings on the command line, and load in a Python file to register
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[custom functions](#custom-code) and architectures. This quickstart widget helps
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you generate a starter config with the **recommended settings** for your
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specific use case. It's also available in spaCy as the
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[`init config`](/api/cli#init-config) command.
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> #### Instructions: widget
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>
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> 1. Select your requirements and settings.
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> 2. Use the buttons at the bottom to save the result to your clipboard or a
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> file `base_config.cfg`.
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> 3. Run [`init fill-config`](/api/cli#init-fill-config) to create a full
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> config.
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> 4. Run [`train`](/api/cli#train) with your config and data.
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>
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> #### Instructions: CLI
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>
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> 1. Run the [`init config`](/api/cli#init-config) command and specify your
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> requirements and settings as CLI arguments.
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> 2. Run [`train`](/api/cli#train) with the exported config and data.
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import QuickstartTraining from 'widgets/quickstart-training.js'
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<QuickstartTraining download="base_config.cfg" />
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After you've saved the starter config to a file `base_config.cfg`, you can use
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the [`init fill-config`](/api/cli#init-fill-config) command to fill in the
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remaining defaults. Training configs should always be **complete and without
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hidden defaults**, to keep your experiments reproducible.
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```cli
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$ python -m spacy init fill-config base_config.cfg config.cfg
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```
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> #### Tip: Debug your data
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>
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> The [`debug data` command](/api/cli#debug-data) lets you analyze and validate
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> your training and development data, get useful stats, and find problems like
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> invalid entity annotations, cyclic dependencies, low data labels and more.
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>
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> ```cli
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> $ python -m spacy debug data config.cfg
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> ```
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Instead of exporting your starter config from the quickstart widget and
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auto-filling it, you can also use the [`init config`](/api/cli#init-config)
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command and specify your requirement and settings and CLI arguments. You can now
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add your data and run [`train`](/api/cli#train) with your config. See the
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[`convert`](/api/cli#convert) command for details on how to convert your data to
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spaCy's binary `.spacy` format. You can either include the data paths in the
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`[paths]` section of your config, or pass them in via the command line.
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```cli
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$ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy
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```
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<!-- TODO:
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<Project id="some_example_project">
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The easiest way to get started with an end-to-end training process is to clone a
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[project](/usage/projects) template. Projects let you manage multi-step
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workflows, from data preprocessing to training and packaging your model.
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</Project>
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-->
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## Training config {#config}
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<!-- > #### Migration from spaCy v2.x
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>
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> TODO: once we have an answer for how to update the training command
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> (`spacy migrate`?), add details here -->
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Training config files include all **settings and hyperparameters** for training
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your model. Instead of providing lots of arguments on the command line, you only
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need to pass your `config.cfg` file to [`spacy train`](/api/cli#train). Under
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the hood, the training config uses the
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[configuration system](https://thinc.ai/docs/usage-config) provided by our
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machine learning library [Thinc](https://thinc.ai). This also makes it easy to
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integrate custom models and architectures, written in your framework of choice.
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Some of the main advantages and features of spaCy's training config are:
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- **Structured sections.** The config is grouped into sections, and nested
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sections are defined using the `.` notation. For example, `[components.ner]`
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defines the settings for the pipeline's named entity recognizer. The config
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can be loaded as a Python dict.
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- **References to registered functions.** Sections can refer to registered
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functions like [model architectures](/api/architectures),
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[optimizers](https://thinc.ai/docs/api-optimizers) or
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[schedules](https://thinc.ai/docs/api-schedules) and define arguments that are
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passed into them. You can also register your own functions to define
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[custom architectures](#custom-functions), reference them in your config and
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tweak their parameters.
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- **Interpolation.** If you have hyperparameters or other settings used by
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multiple components, define them once and reference them as
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[variables](#config-interpolation).
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- **Reproducibility with no hidden defaults.** The config file is the "single
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source of truth" and includes all settings.
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- **Automated checks and validation.** When you load a config, spaCy checks if
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the settings are complete and if all values have the correct types. This lets
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you catch potential mistakes early. In your custom architectures, you can use
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Python [type hints](https://docs.python.org/3/library/typing.html) to tell the
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config which types of data to expect.
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```ini
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https://github.com/explosion/spaCy/blob/develop/spacy/default_config.cfg
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```
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Under the hood, the config is parsed into a dictionary. It's divided into
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sections and subsections, indicated by the square brackets and dot notation. For
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example, `[training]` is a section and `[training.batch_size]` a subsection.
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Subsections can define values, just like a dictionary, or use the `@` syntax to
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refer to [registered functions](#config-functions). This allows the config to
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not just define static settings, but also construct objects like architectures,
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schedules, optimizers or any other custom components. The main top-level
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sections of a config file are:
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| Section | Description |
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| ------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `nlp` | Definition of the `nlp` object, its tokenizer and [processing pipeline](/usage/processing-pipelines) component names. |
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| `components` | Definitions of the [pipeline components](/usage/processing-pipelines) and their models. |
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| `paths` | Paths to data and other assets. Re-used across the config as variables, e.g. `${paths.train}`, and can be [overwritten](#config-overrides) on the CLI. |
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| `system` | Settings related to system and hardware. Re-used across the config as variables, e.g. `${system.seed}`, and can be [overwritten](#config-overrides) on the CLI. |
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| `training` | Settings and controls for the training and evaluation process. |
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| `pretraining` | Optional settings and controls for the [language model pretraining](#pretraining). |
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<Infobox title="Config format and settings" emoji="📖">
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For a full overview of spaCy's config format and settings, see the
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[data format documentation](/api/data-formats#config) and
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[Thinc's config system docs](https://thinc.ai/usage/config). The settings
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available for the different architectures are documented with the
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[model architectures API](/api/architectures). See the Thinc documentation for
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[optimizers](https://thinc.ai/docs/api-optimizers) and
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[schedules](https://thinc.ai/docs/api-schedules).
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</Infobox>
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### Overwriting config settings on the command line {#config-overrides}
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The config system means that you can define all settings **in one place** and in
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a consistent format. There are no command-line arguments that need to be set,
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and no hidden defaults. However, there can still be scenarios where you may want
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to override config settings when you run [`spacy train`](/api/cli#train). This
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includes **file paths** to vectors or other resources that shouldn't be
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hard-code in a config file, or **system-dependent settings**.
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For cases like this, you can set additional command-line options starting with
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`--` that correspond to the config section and value to override. For example,
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`--paths.train ./corpus/train.spacy` sets the `train` value in the `[paths]`
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block.
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```cli
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$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy --paths.dev ./corpus/dev.spacy --training.batch_size 128
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```
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Only existing sections and values in the config can be overwritten. At the end
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of the training, the final filled `config.cfg` is exported with your model, so
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you'll always have a record of the settings that were used, including your
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overrides. Overrides are added before [variables](#config-interpolation) are
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resolved, by the way – so if you need to use a value in multiple places,
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reference it across your config and override it on the CLI once.
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### Defining pipeline components {#config-components}
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When you train a model, you typically train a
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[pipeline](/usage/processing-pipelines) of **one or more components**. The
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`[components]` block in the config defines the available pipeline components and
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how they should be created – either by a built-in or custom
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[factory](/usage/processing-pipelines#built-in), or
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[sourced](/usage/processing-pipelines#sourced-components) from an existing
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pretrained model. For example, `[components.parser]` defines the component named
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`"parser"` in the pipeline. There are different ways you might want to treat
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your components during training, and the most common scenarios are:
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1. Train a **new component** from scratch on your data.
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2. Update an existing **pretrained component** with more examples.
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3. Include an existing pretrained component without updating it.
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4. Include a non-trainable component, like a rule-based
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[`EntityRuler`](/api/entityruler) or [`Sentencizer`](/api/sentencizer), or a
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fully [custom component](/usage/processing-pipelines#custom-components).
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If a component block defines a `factory`, spaCy will look it up in the
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[built-in](/usage/processing-pipelines#built-in) or
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[custom](/usage/processing-pipelines#custom-components) components and create a
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new component from scratch. All settings defined in the config block will be
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passed to the component factory as arguments. This lets you configure the model
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settings and hyperparameters. If a component block defines a `source`, the
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component will be copied over from an existing pretrained model, with its
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existing weights. This lets you include an already trained component in your
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model pipeline, or update a pretrained components with more data specific to
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your use case.
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```ini
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### config.cfg (excerpt)
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[components]
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# "parser" and "ner" are sourced from pretrained model
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[components.parser]
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source = "en_core_web_sm"
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[components.ner]
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source = "en_core_web_sm"
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# "textcat" and "custom" are created blank from built-in / custom factory
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[components.textcat]
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factory = "textcat"
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[components.custom]
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factory = "your_custom_factory"
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your_custom_setting = true
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```
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The `pipeline` setting in the `[nlp]` block defines the pipeline components
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added to the pipeline, in order. For example, `"parser"` here references
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`[components.parser]`. By default, spaCy will **update all components that can
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be updated**. Trainable components that are created from scratch are initialized
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with random weights. For sourced components, spaCy will keep the existing
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weights and [resume training](/api/language#resume_training).
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If you don't want a component to be updated, you can **freeze** it by adding it
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to the `frozen_components` list in the `[training]` block. Frozen components are
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**not updated** during training and are included in the final trained model
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as-is.
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> #### Note on frozen components
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>
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> Even though frozen components are not **updated** during training, they will
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> still **run** during training and evaluation. This is very important, because
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> they may still impact your model's performance – for instance, a sentence
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> boundary detector can impact what the parser or entity recognizer considers a
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> valid parse. So the evaluation results should always reflect what your model
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> will produce at runtime.
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```ini
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[nlp]
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lang = "en"
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pipeline = ["parser", "ner", "textcat", "custom"]
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[training]
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frozen_components = ["parser", "custom"]
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```
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### Using registered functions {#config-functions}
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The training configuration defined in the config file doesn't have to only
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consist of static values. Some settings can also be **functions**. For instance,
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the `batch_size` can be a number that doesn't change, or a schedule, like a
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sequence of compounding values, which has shown to be an effective trick (see
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[Smith et al., 2017](https://arxiv.org/abs/1711.00489)).
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```ini
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### With static value
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[training]
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batch_size = 128
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```
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To refer to a function instead, you can make `[training.batch_size]` its own
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section and use the `@` syntax specify the function and its arguments – in this
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case [`compounding.v1`](https://thinc.ai/docs/api-schedules#compounding) defined
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in the [function registry](/api/top-level#registry). All other values defined in
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the block are passed to the function as keyword arguments when it's initialized.
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You can also use this mechanism to register
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[custom implementations and architectures](#custom-functions) and reference them
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from your configs.
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> #### How the config is resolved
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>
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> The config file is parsed into a regular dictionary and is resolved and
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> validated **bottom-up**. Arguments provided for registered functions are
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> checked against the function's signature and type annotations. The return
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> value of a registered function can also be passed into another function – for
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> instance, a learning rate schedule can be provided as the an argument of an
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> optimizer.
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```ini
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### With registered function
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[training.batch_size]
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@schedules = "compounding.v1"
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start = 100
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stop = 1000
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compound = 1.001
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```
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### Using variable interpolation {#config-interpolation}
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Another very useful feature of the config system is that it supports variable
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interpolation for both **values and sections**. This means that you only need to
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define a setting once and can reference it across your config using the
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`${section.value}` syntax. In this example, the value of `seed` is reused within
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the `[training]` block, and the whole block of `[training.optimizer]` is reused
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in `[pretraining]` and will become `pretraining.optimizer`.
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```ini
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### config.cfg (excerpt) {highlight="5,18"}
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[system]
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seed = 0
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[training]
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seed = ${system.seed}
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[training.optimizer]
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@optimizers = "Adam.v1"
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beta1 = 0.9
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beta2 = 0.999
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L2_is_weight_decay = true
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L2 = 0.01
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grad_clip = 1.0
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use_averages = false
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eps = 1e-8
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[pretraining]
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optimizer = ${training.optimizer}
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```
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You can also use variables inside strings. In that case, it works just like
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f-strings in Python. If the value of a variable is not a string, it's converted
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to a string.
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```ini
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[paths]
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version = 5
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root = "/Users/you/data"
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train = "${paths.root}/train_${paths.version}.spacy"
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# Result: /Users/you/data/train_5.spacy
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```
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<Infobox title="Tip: Override variables on the CLI" emoji="💡">
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If you need to change certain values between training runs, you can define them
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once, reference them as variables and then [override](#config-overrides) them on
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the CLI. For example, `--paths.root /other/root` will change the value of `root`
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in the block `[paths]` and the change will be reflected across all other values
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that reference this variable.
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</Infobox>
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### Model architectures {#model-architectures}
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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 ~~Model[List[Doc],
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> List[Floats2d]]~~. This so-called generic type describes the layer and its
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> input and output type – in this case, it takes a list of `Doc` objects as the
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> input and list of 2-dimensional arrays of floats as the output. You can read
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> more about defining Thinc models [here](https://thinc.ai/docs/usage-models).
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> Also see the [type checking](https://thinc.ai/docs/usage-type-checking) for
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> how to enable linting in your editor to see live feedback if your inputs and
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> outputs don't match.
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A **model architecture** is a function that wires up a Thinc
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[`Model`](https://thinc.ai/docs/api-model) instance, which you can then use in a
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component or as a layer of a larger network. You can use Thinc as a thin
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[wrapper around frameworks](https://thinc.ai/docs/usage-frameworks) such as
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PyTorch, TensorFlow or MXNet, or you can implement your logic in Thinc
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[directly](https://thinc.ai/docs/usage-models).
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spaCy's built-in components will never construct their `Model` instances
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themselves, so you won't have to subclass the component to change its model
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architecture. You can just **update the config** so that it refers to a
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different registered function. Once the component has been created, its `Model`
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instance has already been assigned, so you cannot change its model architecture.
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The architecture is like a recipe for the network, and you can't change the
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recipe once the dish has already been prepared. You have to make a new one.
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spaCy includes a variety of built-in [architectures](/api/architectures) for
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different tasks. For example:
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<!-- TODO: model return types -->
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| Architecture | Description |
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| ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [HashEmbedCNN](/api/architectures#HashEmbedCNN) | Build spaCy’s "standard" embedding layer, which uses hash embedding with subword features and a CNN with layer-normalized maxout. ~~Model[List[Doc], List[Floats2d]]~~ |
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| [TransitionBasedParser](/api/architectures#TransitionBasedParser) | Build a [transition-based parser](https://explosion.ai/blog/parsing-english-in-python) model used in the default [`EntityRecognizer`](/api/entityrecognizer) and [`DependencyParser`](/api/dependencyparser). ~~Model[List[Docs], List[List[Floats2d]]]~~ |
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| [TextCatEnsemble](/api/architectures#TextCatEnsemble) | Stacked ensemble of a bag-of-words model and a neural network model with an internal CNN embedding layer. Used in the default [`TextCategorizer`](/api/textcategorizer). ~~Model~~ |
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<!-- TODO: link to not yet existing usage page on custom architectures etc. -->
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### Metrics, training output and weighted scores {#metrics}
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When you train a model using the [`spacy train`](/api/cli#train) command, you'll
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see a table showing the metrics after each pass over the data. The available
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metrics **depend on the pipeline components**. Pipeline components also define
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which scores are shown and how they should be **weighted in the final score**
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that decides about the best model.
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The `training.score_weights` setting in your `config.cfg` lets you customize the
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scores shown in the table and how they should be weighted. In this example, the
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labeled dependency accuracy and NER F-score count towards the final score with
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40% each and the tagging accuracy makes up the remaining 20%. The tokenization
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accuracy and speed are both shown in the table, but not counted towards the
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score.
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> #### Why do I need score weights?
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>
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> At the end of your training process, you typically want to select the **best
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> model** – but what "best" means depends on the available components and your
|
||
> specific use case. For instance, you may prefer a model with higher NER and
|
||
> lower POS tagging accuracy over a model with lower NER and higher POS
|
||
> accuracy. You can express this preference in the score weights, e.g. by
|
||
> assigning `ents_f` (NER F-score) a higher weight.
|
||
|
||
```ini
|
||
[training.score_weights]
|
||
dep_las = 0.4
|
||
ents_f = 0.4
|
||
tag_acc = 0.2
|
||
token_acc = 0.0
|
||
speed = 0.0
|
||
```
|
||
|
||
The `score_weights` don't _have to_ sum to `1.0` – but it's recommended. When
|
||
you generate a config for a given pipeline, the score weights are generated by
|
||
combining and normalizing the default score weights of the pipeline components.
|
||
The default score weights are defined by each pipeline component via the
|
||
`default_score_weights` setting on the
|
||
[`@Language.component`](/api/language#component) or
|
||
[`@Language.factory`](/api/language#factory). By default, all pipeline
|
||
components are weighted equally.
|
||
|
||
<Accordion title="Understanding the training output and score types" spaced>
|
||
|
||
| Name | Description |
|
||
| -------------------------- | ----------------------------------------------------------------------------------------------------------------------- |
|
||
| **Loss** | The training loss representing the amount of work left for the optimizer. Should decrease, but usually not to `0`. |
|
||
| **Precision** (P) | Percentage of predicted annotations that were correct. Should increase. |
|
||
| **Recall** (R) | Percentage of reference annotations recovered. Should increase. |
|
||
| **F-Score** (F) | Harmonic mean of precision and recall. Should increase. |
|
||
| **UAS** / **LAS** | Unlabeled and labeled attachment score for the dependency parser, i.e. the percentage of correct arcs. Should increase. |
|
||
| **Words per second** (WPS) | Prediction speed in words per second. Should stay stable. |
|
||
|
||
Note that if the development data has raw text, some of the gold-standard
|
||
entities might not align to the predicted tokenization. These tokenization
|
||
errors are **excluded from the NER evaluation**. If your tokenization makes it
|
||
impossible for the model to predict 50% of your entities, your NER F-score might
|
||
still look good.
|
||
|
||
</Accordion>
|
||
|
||
## Custom Functions {#custom-functions}
|
||
|
||
Registered functions in the training config files can refer to built-in
|
||
implementations, but you can also plug in fully **custom implementations**. All
|
||
you need to do is register your function using the `@spacy.registry` decorator
|
||
with the name of the respective [registry](/api/top-level#registry), e.g.
|
||
`@spacy.registry.architectures`, and a string name to assign to your function.
|
||
Registering custom functions allows you to **plug in models** defined in PyTorch
|
||
or TensorFlow, make **custom modifications** to the `nlp` object, create custom
|
||
optimizers or schedules, or **stream in data** and preprocesses it on the fly
|
||
while training.
|
||
|
||
Each custom function can have any numbers of arguments that are passed in via
|
||
the [config](#config), just the built-in functions. If your function defines
|
||
**default argument values**, spaCy is able to auto-fill your config when you run
|
||
[`init fill-config`](/api/cli#init-fill-config). If you want to make sure that a
|
||
given parameter is always explicitely set in the config, avoid setting a default
|
||
value for it.
|
||
|
||
<!-- TODO: possibly link to new (not yet created) page on creating models ? -->
|
||
|
||
### Training with custom code {#custom-code}
|
||
|
||
> #### Example
|
||
>
|
||
> ```cli
|
||
> $ python -m spacy train config.cfg --code functions.py
|
||
> ```
|
||
|
||
The [`spacy train`](/api/cli#train) recipe lets you specify an optional argument
|
||
`--code` that points to a Python file. The file is imported before training and
|
||
allows you to add custom functions and architectures to the function registry
|
||
that can then be referenced from your `config.cfg`. This lets you train spaCy
|
||
models with custom components, without having to re-implement the whole training
|
||
workflow.
|
||
|
||
#### Example: Modifying the nlp object {#custom-code-nlp-callbacks}
|
||
|
||
For many use cases, you don't necessarily want to implement the whole `Language`
|
||
subclass and language data from scratch – it's often enough to make a few small
|
||
modifications, like adjusting the
|
||
[tokenization rules](/usage/linguistic-features#native-tokenizer-additions) or
|
||
[language defaults](/api/language#defaults) like stop words. The config lets you
|
||
provide three optional **callback functions** that give you access to the
|
||
language class and `nlp` object at different points of the lifecycle:
|
||
|
||
| Callback | Description |
|
||
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `before_creation` | Called before the `nlp` object is created and receives the language subclass like `English` (not the instance). Useful for writing to the [`Language.Defaults`](/api/language#defaults). |
|
||
| `after_creation` | Called right after the `nlp` object is created, but before the pipeline components are added to the pipeline and receives the `nlp` object. Useful for modifying the tokenizer. |
|
||
| `after_pipeline_creation` | Called right after the pipeline components are created and added and receives the `nlp` object. Useful for modifying pipeline components. |
|
||
|
||
The `@spacy.registry.callbacks` decorator lets you register your custom function
|
||
in the `callbacks` [registry](/api/top-level#registry) under a given name. You
|
||
can then reference the function in a config block using the `@callbacks` key. If
|
||
a block contains a key starting with an `@`, it's interpreted as a reference to
|
||
a function. Because you've registered the function, spaCy knows how to create it
|
||
when you reference `"customize_language_data"` in your config. Here's an example
|
||
of a callback that runs before the `nlp` object is created and adds a few custom
|
||
tokenization rules to the defaults:
|
||
|
||
> #### config.cfg
|
||
>
|
||
> ```ini
|
||
> [nlp.before_creation]
|
||
> @callbacks = "customize_language_data"
|
||
> ```
|
||
|
||
```python
|
||
### functions.py {highlight="3,6"}
|
||
import spacy
|
||
|
||
@spacy.registry.callbacks("customize_language_data")
|
||
def create_callback():
|
||
def customize_language_data(lang_cls):
|
||
lang_cls.Defaults.suffixes = lang_cls.Defaults.suffixes + (r"-+$",)
|
||
return lang_cls
|
||
|
||
return customize_language_data
|
||
```
|
||
|
||
<Infobox variant="warning">
|
||
|
||
Remember that a registered function should always be a function that spaCy
|
||
**calls to create something**. In this case, it **creates a callback** – it's
|
||
not the callback itself.
|
||
|
||
</Infobox>
|
||
|
||
Any registered function – in this case `create_callback` – can also take
|
||
**arguments** that can be **set by the config**. This lets you implement and
|
||
keep track of different configurations, without having to hack at your code. You
|
||
can choose any arguments that make sense for your use case. In this example,
|
||
we're adding the arguments `extra_stop_words` (a list of strings) and `debug`
|
||
(boolean) for printing additional info when the function runs.
|
||
|
||
> #### config.cfg
|
||
>
|
||
> ```ini
|
||
> [nlp.before_creation]
|
||
> @callbacks = "customize_language_data"
|
||
> extra_stop_words = ["ooh", "aah"]
|
||
> debug = true
|
||
> ```
|
||
|
||
```python
|
||
### functions.py {highlight="5,8-10"}
|
||
from typing import List
|
||
import spacy
|
||
|
||
@spacy.registry.callbacks("customize_language_data")
|
||
def create_callback(extra_stop_words: List[str] = [], debug: bool = False):
|
||
def customize_language_data(lang_cls):
|
||
lang_cls.Defaults.suffixes = lang_cls.Defaults.suffixes + (r"-+$",)
|
||
lang_cls.Defaults.stop_words.add(extra_stop_words)
|
||
if debug:
|
||
print("Updated stop words and tokenizer suffixes")
|
||
return lang_cls
|
||
|
||
return customize_language_data
|
||
```
|
||
|
||
<Infobox title="Tip: Use Python type hints" emoji="💡">
|
||
|
||
spaCy's configs are powered by our machine learning library Thinc's
|
||
[configuration system](https://thinc.ai/docs/usage-config), which supports
|
||
[type hints](https://docs.python.org/3/library/typing.html) and even
|
||
[advanced type annotations](https://thinc.ai/docs/usage-config#advanced-types)
|
||
using [`pydantic`](https://github.com/samuelcolvin/pydantic). If your registered
|
||
function provides type hints, the values that are passed in will be checked
|
||
against the expected types. For example, `debug: bool` in the example above will
|
||
ensure that the value received as the argument `debug` is a boolean. If the
|
||
value can't be coerced into a boolean, spaCy will raise an error.
|
||
`debug: pydantic.StrictBool` will force the value to be a boolean and raise an
|
||
error if it's not – for instance, if your config defines `1` instead of `true`.
|
||
|
||
</Infobox>
|
||
|
||
With your `functions.py` defining additional code and the updated `config.cfg`,
|
||
you can now run [`spacy train`](/api/cli#train) and point the argument `--code`
|
||
to your Python file. Before loading the config, spaCy will import the
|
||
`functions.py` module and your custom functions will be registered.
|
||
|
||
```cli
|
||
$ python -m spacy train config.cfg --output ./output --code ./functions.py
|
||
```
|
||
|
||
#### Example: Custom batch size schedule {#custom-code-schedule}
|
||
|
||
For example, let's say you've implemented your own batch size schedule to use
|
||
during training. The `@spacy.registry.schedules` decorator lets you register
|
||
that function in the `schedules` [registry](/api/top-level#registry) and assign
|
||
it a string name:
|
||
|
||
> #### Why the version in the name?
|
||
>
|
||
> A big benefit of the config system is that it makes your experiments
|
||
> reproducible. We recommend versioning the functions you register, especially
|
||
> if you expect them to change (like a new model architecture). This way, you
|
||
> know that a config referencing `v1` means a different function than a config
|
||
> referencing `v2`.
|
||
|
||
```python
|
||
### functions.py
|
||
import spacy
|
||
|
||
@spacy.registry.schedules("my_custom_schedule.v1")
|
||
def my_custom_schedule(start: int = 1, factor: int = 1.001):
|
||
while True:
|
||
yield start
|
||
start = start * factor
|
||
```
|
||
|
||
In your config, you can now reference the schedule in the
|
||
`[training.batch_size]` block via `@schedules`. If a block contains a key
|
||
starting with an `@`, it's interpreted as a reference to a function. All other
|
||
settings in the block will be passed to the function as keyword arguments. Keep
|
||
in mind that the config shouldn't have any hidden defaults and all arguments on
|
||
the functions need to be represented in the config.
|
||
|
||
```ini
|
||
### config.cfg (excerpt)
|
||
[training.batch_size]
|
||
@schedules = "my_custom_schedule.v1"
|
||
start = 2
|
||
factor = 1.005
|
||
```
|
||
|
||
#### Example: Custom data reading and batching {#custom-code-readers-batchers}
|
||
|
||
Some use-cases require **streaming in data** or manipulating datasets on the
|
||
fly, rather than generating all data beforehand and storing it to file. Instead
|
||
of using the built-in [`Corpus`](/api/corpus) reader, which uses static file
|
||
paths, you can create and register a custom function that generates
|
||
[`Example`](/api/example) objects. The resulting generator can be infinite. When
|
||
using this dataset for training, stopping criteria such as maximum number of
|
||
steps, or stopping when the loss does not decrease further, can be used.
|
||
|
||
In this example we assume a custom function `read_custom_data` which loads or
|
||
generates texts with relevant text classification annotations. Then, small
|
||
lexical variations of the input text are created before generating the final
|
||
[`Example`](/api/example) objects. The `@spacy.registry.readers` decorator lets
|
||
you register the function creating the custom reader in the `readers`
|
||
[registry](/api/top-level#registry) and assign it a string name, so it can be
|
||
used in your config. All arguments on the registered function become available
|
||
as **config settings** – in this case, `source`.
|
||
|
||
> #### config.cfg
|
||
>
|
||
> ```ini
|
||
> [training.train_corpus]
|
||
> @readers = "corpus_variants.v1"
|
||
> source = "s3://your_bucket/path/data.csv"
|
||
> ```
|
||
|
||
```python
|
||
### functions.py {highlight="7-8"}
|
||
from typing import Callable, Iterator, List
|
||
import spacy
|
||
from spacy.gold import Example
|
||
from spacy.language import Language
|
||
import random
|
||
|
||
@spacy.registry.readers("corpus_variants.v1")
|
||
def stream_data(source: str) -> Callable[[Language], Iterator[Example]]:
|
||
def generate_stream(nlp):
|
||
for text, cats in read_custom_data(source):
|
||
# Create a random variant of the example text
|
||
i = random.randint(0, len(text) - 1)
|
||
variant = text[:i] + text[i].upper() + text[i + 1:]
|
||
doc = nlp.make_doc(variant)
|
||
example = Example.from_dict(doc, {"cats": cats})
|
||
yield example
|
||
|
||
return generate_stream
|
||
```
|
||
|
||
<Infobox variant="warning">
|
||
|
||
Remember that a registered function should always be a function that spaCy
|
||
**calls to create something**. In this case, it **creates the reader function**
|
||
– it's not the reader itself.
|
||
|
||
</Infobox>
|
||
|
||
We can also customize the **batching strategy** by registering a new batcher
|
||
function in the `batchers` [registry](/api/top-level#registry). A batcher turns
|
||
a stream of items into a stream of batches. spaCy has several useful built-in
|
||
[batching strategies](/api/top-level#batchers) with customizable sizes, but it's
|
||
also easy to implement your own. For instance, the following function takes the
|
||
stream of generated [`Example`](/api/example) objects, and removes those which
|
||
have the exact same underlying raw text, to avoid duplicates within each batch.
|
||
Note that in a more realistic implementation, you'd also want to check whether
|
||
the annotations are exactly the same.
|
||
|
||
> #### config.cfg
|
||
>
|
||
> ```ini
|
||
> [training.batcher]
|
||
> @batchers = "filtering_batch.v1"
|
||
> size = 150
|
||
> ```
|
||
|
||
```python
|
||
### functions.py
|
||
from typing import Callable, Iterable, Iterator, List
|
||
import spacy
|
||
from spacy.gold import Example
|
||
|
||
@spacy.registry.batchers("filtering_batch.v1")
|
||
def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Example]]]:
|
||
def create_filtered_batches(examples):
|
||
batch = []
|
||
for eg in examples:
|
||
# Remove duplicate examples with the same text from batch
|
||
if eg.text not in [x.text for x in batch]:
|
||
batch.append(eg)
|
||
if len(batch) == size:
|
||
yield batch
|
||
batch = []
|
||
|
||
return create_filtered_batches
|
||
```
|
||
|
||
<!-- TODO:
|
||
|
||
<Project id="example_pytorch_model">
|
||
|
||
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
|
||
sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
|
||
mattis pretium.
|
||
|
||
</Project>
|
||
|
||
-->
|
||
|
||
### Defining custom architectures {#custom-architectures}
|
||
|
||
<!-- TODO: this should probably move to new section on models -->
|
||
|
||
## Transfer learning {#transfer-learning}
|
||
|
||
<!-- TODO: write something, link to embeddings and transformers page – should probably wait until transformers/embeddings/transfer learning docs are done -->
|
||
|
||
### Using transformer models like BERT {#transformers}
|
||
|
||
spaCy v3.0 lets you use almost any statistical model to power your pipeline. You
|
||
can use models implemented in a variety of frameworks. A transformer model is
|
||
just a statistical model, so the
|
||
[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
|
||
actually has very little work to do: it just has to provide a few functions that
|
||
do the required plumbing. It also provides a pipeline component,
|
||
[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
|
||
you save the transformer outputs for later use.
|
||
|
||
<!-- TODO:
|
||
|
||
<Project id="en_core_trf_lg">
|
||
|
||
Try out a BERT-based model pipeline using this project template: swap in your
|
||
data, edit the settings and hyperparameters and train, evaluate, package and
|
||
visualize your model.
|
||
|
||
</Project>
|
||
-->
|
||
|
||
For more details on how to integrate transformer models into your training
|
||
config and customize the implementations, see the usage guide on
|
||
[training transformers](/usage/embeddings-transformers#transformers-training).
|
||
|
||
### Pretraining with spaCy {#pretraining}
|
||
|
||
<!-- TODO: document spacy pretrain, objectives etc. – should probably wait until transformers/embeddings/transfer learning docs are done -->
|
||
|
||
## Parallel Training with Ray {#parallel-training}
|
||
|
||
<!-- TODO:
|
||
|
||
|
||
<Project id="some_example_project">
|
||
|
||
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
|
||
sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
|
||
mattis pretium.
|
||
|
||
</Project>
|
||
|
||
-->
|
||
|
||
## Internal training API {#api}
|
||
|
||
<Infobox variant="warning">
|
||
|
||
spaCy gives you full control over the training loop. However, for most use
|
||
cases, it's recommended to train your models via the
|
||
[`spacy train`](/api/cli#train) command with a [`config.cfg`](#config) to keep
|
||
track of your settings and hyperparameters, instead of writing your own training
|
||
scripts from scratch. [Custom registered functions](#custom-code) should
|
||
typically give you everything you need to train fully custom models with
|
||
[`spacy train`](/api/cli#train).
|
||
|
||
</Infobox>
|
||
|
||
The [`Example`](/api/example) object contains annotated training data, also
|
||
called the **gold standard**. It's initialized with a [`Doc`](/api/doc) object
|
||
that will hold the predictions, and another `Doc` object that holds the
|
||
gold-standard annotations. It also includes the **alignment** between those two
|
||
documents if they differ in tokenization. The `Example` class ensures that spaCy
|
||
can rely on one **standardized format** that's passed through the pipeline.
|
||
Here's an example of a simple `Example` for part-of-speech tags:
|
||
|
||
```python
|
||
words = ["I", "like", "stuff"]
|
||
predicted = Doc(vocab, words=words)
|
||
# create the reference Doc with gold-standard TAG annotations
|
||
tags = ["NOUN", "VERB", "NOUN"]
|
||
tag_ids = [vocab.strings.add(tag) for tag in tags]
|
||
reference = Doc(vocab, words=words).from_array("TAG", numpy.array(tag_ids, dtype="uint64"))
|
||
example = Example(predicted, reference)
|
||
```
|
||
|
||
Alternatively, the `reference` `Doc` with the gold-standard annotations can be
|
||
created from a dictionary with keyword arguments specifying the annotations,
|
||
like `tags` or `entities`. Using the `Example` object and its gold-standard
|
||
annotations, the model can be updated to learn a sentence of three words with
|
||
their assigned part-of-speech tags.
|
||
|
||
> #### About the tag map
|
||
>
|
||
> The tag map is part of the vocabulary and defines the annotation scheme. If
|
||
> you're training a new language model, this will let you map the tags present
|
||
> in the treebank you train on to spaCy's tag scheme:
|
||
>
|
||
> ```python
|
||
> tag_map = {"N": {"pos": "NOUN"}, "V": {"pos": "VERB"}}
|
||
> vocab = Vocab(tag_map=tag_map)
|
||
> ```
|
||
|
||
```python
|
||
words = ["I", "like", "stuff"]
|
||
tags = ["NOUN", "VERB", "NOUN"]
|
||
predicted = Doc(nlp.vocab, words=words)
|
||
example = Example.from_dict(predicted, {"tags": tags})
|
||
```
|
||
|
||
Here's another example that shows how to define gold-standard named entities.
|
||
The letters added before the labels refer to the tags of the
|
||
[BILUO scheme](/usage/linguistic-features#updating-biluo) – `O` is a token
|
||
outside an entity, `U` an single entity unit, `B` the beginning of an entity,
|
||
`I` a token inside an entity and `L` the last token of an entity.
|
||
|
||
```python
|
||
doc = Doc(nlp.vocab, words=["Facebook", "released", "React", "in", "2014"])
|
||
example = Example.from_dict(doc, {"entities": ["U-ORG", "O", "U-TECHNOLOGY", "O", "U-DATE"]})
|
||
```
|
||
|
||
<Infobox title="Migrating from v2.x" variant="warning">
|
||
|
||
As of v3.0, the [`Example`](/api/example) object replaces the `GoldParse` class.
|
||
It can be constructed in a very similar way, from a `Doc` and a dictionary of
|
||
annotations. For more details, see the
|
||
[migration guide](/usage/v3#migrating-training).
|
||
|
||
```diff
|
||
- gold = GoldParse(doc, entities=entities)
|
||
+ example = Example.from_dict(doc, {"entities": entities})
|
||
```
|
||
|
||
</Infobox>
|
||
|
||
Of course, it's not enough to only show a model a single example once.
|
||
Especially if you only have few examples, you'll want to train for a **number of
|
||
iterations**. At each iteration, the training data is **shuffled** to ensure the
|
||
model doesn't make any generalizations based on the order of examples. Another
|
||
technique to improve the learning results is to set a **dropout rate**, a rate
|
||
at which to randomly "drop" individual features and representations. This makes
|
||
it harder for the model to memorize the training data. For example, a `0.25`
|
||
dropout means that each feature or internal representation has a 1/4 likelihood
|
||
of being dropped.
|
||
|
||
> - [`nlp`](/api/language): The `nlp` object with the model.
|
||
> - [`nlp.begin_training`](/api/language#begin_training): Start the training and
|
||
> return an optimizer to update the model's weights.
|
||
> - [`Optimizer`](https://thinc.ai/docs/api-optimizers): Function that holds
|
||
> state between updates.
|
||
> - [`nlp.update`](/api/language#update): Update model with examples.
|
||
> - [`Example`](/api/example): object holding predictions and gold-standard
|
||
> annotations.
|
||
> - [`nlp.to_disk`](/api/language#to_disk): Save the updated model to a
|
||
> directory.
|
||
|
||
```python
|
||
### Example training loop
|
||
optimizer = nlp.begin_training()
|
||
for itn in range(100):
|
||
random.shuffle(train_data)
|
||
for raw_text, entity_offsets in train_data:
|
||
doc = nlp.make_doc(raw_text)
|
||
example = Example.from_dict(doc, {"entities": entity_offsets})
|
||
nlp.update([example], sgd=optimizer)
|
||
nlp.to_disk("/model")
|
||
```
|
||
|
||
The [`nlp.update`](/api/language#update) method takes the following arguments:
|
||
|
||
| Name | Description |
|
||
| ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||
| `examples` | [`Example`](/api/example) objects. The `update` method takes a sequence of them, so you can batch up your training examples. |
|
||
| `drop` | Dropout rate. Makes it harder for the model to just memorize the data. |
|
||
| `sgd` | An [`Optimizer`](https://thinc.ai/docs/api-optimizers) object, which updated the model's weights. If not set, spaCy will create a new one and save it for further use. |
|
||
|
||
<Infobox title="Migrating from v2.x" variant="warning">
|
||
|
||
As of v3.0, the [`Example`](/api/example) object replaces the `GoldParse` class
|
||
and the "simple training style" of calling `nlp.update` with a text and a
|
||
dictionary of annotations. Updating your code to use the `Example` object should
|
||
be very straightforward: you can call
|
||
[`Example.from_dict`](/api/example#from_dict) with a [`Doc`](/api/doc) and the
|
||
dictionary of annotations:
|
||
|
||
```diff
|
||
text = "Facebook released React in 2014"
|
||
annotations = {"entities": ["U-ORG", "O", "U-TECHNOLOGY", "O", "U-DATE"]}
|
||
+ example = Example.from_dict(nlp.make_doc(text), {"entities": entities})
|
||
- nlp.update([text], [annotations])
|
||
+ nlp.update([example])
|
||
```
|
||
|
||
</Infobox>
|