spaCy/website/docs/usage/training.md

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Introduction to training models

import Training101 from 'usage/101/_training.md'

If you need to label a lot of data, check out Prodigy, a new, active learning-powered annotation tool we've developed. Prodigy is fast and extensible, and comes with a modern web application that helps you collect training data faster. It integrates seamlessly with spaCy, pre-selects the most relevant examples for annotation, and lets you train and evaluate ready-to-use spaCy models.

Quickstart

The recommended way to train your spaCy models is via the spacy train command on the command line. It only needs a single config.cfg configuration file that includes all settings and hyperparameters. You can optionally overwritten settings on the command line, and load in a Python file to register custom functions and architectures.

Instructions

1. Select your requirements and settings.
2. Use the buttons at the bottom to save the result to your clipboard or a file base_config.cfg.
3. Run init config to create a full training config.
4. Run train with your config and data.

import QuickstartTraining from 'widgets/quickstart-training.js'

After you've saved the starter config to a file base_config.cfg, you can use the init config command to fill in the remaining defaults. Training configs should always be complete and without hidden defaults, to keep your experiments reproducible.

$ python -m spacy init config config.cfg --base base_config.cfg

Tip: Debug your data

The debug data command lets you analyze and validate your training and development data, get useful stats, and find problems like invalid entity annotations, cyclic dependencies, low data labels and more.

$ python -m spacy debug data config.cfg --verbose

You can now add your data and run train with your config. See the convert command for details on how to convert your data to spaCy's binary .spacy format. You can either include the data paths in the [paths] section of your config, or pass them in via the command line.

$ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy

The easiest way to get started with an end-to-end training process is to clone a project template. Projects let you manage multi-step workflows, from data preprocessing to training and packaging your model.

Training config

Migration from spaCy v2.x

TODO: once we have an answer for how to update the training command (spacy migrate?), add details here

Training config files include all settings and hyperparameters for training your model. Instead of providing lots of arguments on the command line, you only need to pass your config.cfg file to spacy train. Under the hood, the training config uses the configuration system provided by our machine learning library Thinc. This also makes it easy to integrate custom models and architectures, written in your framework of choice. Some of the main advantages and features of spaCy's training config are:

• Structured sections. The config is grouped into sections, and nested sections are defined using the . notation. For example, [components.ner] defines the settings for the pipeline's named entity recognizer. The config can be loaded as a Python dict.
• References to registered functions. Sections can refer to registered functions like model architectures, optimizers or schedules and define arguments that are passed into them. You can also register your own functions to define custom architectures, reference them in your config and tweak their parameters.
• Interpolation. If you have hyperparameters or other settings used by multiple components, define them once and reference them as variables.
• Reproducibility with no hidden defaults. The config file is the "single source of truth" and includes all settings.
• Automated checks and validation. When you load a config, spaCy checks if the settings are complete and if all values have the correct types. This lets you catch potential mistakes early. In your custom architectures, you can use Python type hints to tell the config which types of data to expect.

https://github.com/explosion/spaCy/blob/develop/spacy/default_config.cfg

Under the hood, the config is parsed into a dictionary. It's divided into sections and subsections, indicated by the square brackets and dot notation. For example, [training] is a section and [training.batch_size] a subsections. Subsections can define values, just like a dictionary, or use the @ syntax to refer to registered functions. This allows the config to not just define static settings, but also construct objects like architectures, schedules, optimizers or any other custom components. The main top-level sections of a config file are:

Section	Description
nlp	Definition of the nlp object, its tokenizer and processing pipeline component names.
components	Definitions of the pipeline components and their models.
paths	Paths to data and other assets. Re-used across the config as variables, e.g. ${paths:train}, and can be overwritten on the CLI.
system	Settings related to system and hardware. Re-used across the config as variables, e.g. ${system.seed}, and can be overwritten on the CLI.
training	Settings and controls for the training and evaluation process.
pretraining	Optional settings and controls for the language model pretraining.

For a full overview of spaCy's config format and settings, see the data format documentation and Thinc's config system docs. The settings available for the different architectures are documented with the model architectures API. See the Thinc documentation for optimizers and schedules.

Overwriting config settings on the command line

The config system means that you can define all settings in one place and in a consistent format. There are no command-line arguments that need to be set, and no hidden defaults. However, there can still be scenarios where you may want to override config settings when you run spacy train. This includes file paths to vectors or other resources that shouldn't be hard-code in a config file, or system-dependent settings.

For cases like this, you can set additional command-line options starting with -- that correspond to the config section and value to override. For example, --paths.train ./corpus/train.spacy sets the train value in the [paths] block.

$ python -m spacy train config.cfg --paths.train ./corpus/train.spacy
--paths.dev ./corpus/dev.spacy --training.batch_size 128

Only existing sections and values in the config can be overwritten. At the end of the training, the final filled config.cfg is exported with your model, so you'll always have a record of the settings that were used, including your overrides. Overrides are added before variables are resolved, by the way – so if you need to use a value in multiple places, reference it across your config and override it on the CLI once.

Defining pipeline components

When you train a model, you typically train a pipeline of one or more components. The [components] block in the config defines the available pipeline components and how they should be created – either by a built-in or custom factory, or sourced from an existing pretrained model. For example, [components.parser] defines the component named "parser" in the pipeline. There are different ways you might want to treat your components during training, and the most common scenarios are:

1. Train a new component from scratch on your data.
2. Update an existing pretrained component with more examples.
3. Include an existing pretrained component without updating it.
4. Include a non-trainable component, like a rule-based EntityRuler or Sentencizer, or a fully custom component.

If a component block defines a factory, spaCy will look it up in the built-in or custom components and create a new component from scratch. All settings defined in the config block will be passed to the component factory as arguments. This lets you configure the model settings and hyperparameters. If a component block defines a source, the component will be copied over from an existing pretrained model, with its existing weights. This lets you include an already trained component in your model pipeline, or update a pretrained components with more data specific to your use case.

### config.cfg (excerpt)
[components]

# "parser" and "ner" are sourced from pretrained model
[components.parser]
source = "en_core_web_sm"

[components.ner]
source = "en_core_web_sm"

# "textcat" and "custom" are created blank from built-in / custom factory
[components.textcat]
factory = "textcat"

[components.custom]
factory = "your_custom_factory"
your_custom_setting = true

The pipeline setting in the [nlp] block defines the pipeline components added to the pipeline, in order. For example, "parser" here references [components.parser]. By default, spaCy will update all components that can be updated. Trainable components that are created from scratch are initialized with random weights. For sourced components, spaCy will keep the existing weights and resume training.

If you don't want a component to be updated, you can freeze it by adding it to the frozen_components list in the [training] block. Frozen components are not updated during training and are included in the final trained model as-is.

Note on frozen components

Even though frozen components are not updated during training, they will still run during training and evaluation. This is very important, because they may still impact your model's performance – for instance, a sentence boundary detector can impact what the parser or entity recognizer considers a valid parse. So the evaluation results should always reflect what your model will produce at runtime.

[nlp]
lang = "en"
pipeline = ["parser", "ner", "textcat", "custom"]

[training]
frozen_components = ["parser", "custom"]

Using registered functions

The training configuration defined in the config file doesn't have to only consist of static values. Some settings can also be functions. For instance, the batch_size can be a number that doesn't change, or a schedule, like a sequence of compounding values, which has shown to be an effective trick (see Smith et al., 2017).

### With static value
[training]
batch_size = 128

To refer to a function instead, you can make [training.batch_size] its own section and use the @ syntax specify the function and its arguments – in this case compounding.v1 defined in the function registry. All other values defined in the block are passed to the function as keyword arguments when it's initialized. You can also use this mechanism to register custom implementations and architectures and reference them from your configs.

How the config is resolved

The config file is parsed into a regular dictionary and is resolved and validated bottom-up. Arguments provided for registered functions are checked against the function's signature and type annotations. The return value of a registered function can also be passed into another function – for instance, a learning rate schedule can be provided as the an argument of an optimizer.

### With registered function
[training.batch_size]
@schedules = "compounding.v1"
start = 100
stop = 1000
compound = 1.001

Using variable interpolation

Another very useful feature of the config system is that it supports variable interpolation for both values and sections. This means that you only need to define a setting once and can reference it across your config using the ${section:value} or ${section.block} syntax. In this example, the value of seed is reused within the [training] block, and the whole block of [training.optimizer] is reused in [pretraining] and will become pretraining.optimizer.

Note on syntax

There are two different ways to format your variables, depending on whether you want to reference a single value or a block. Values are specified after a :, while blocks are specified with a .:

1. ${section:value}, ${section.subsection:value}
2. ${section.block}, ${section.subsection.block}

### config.cfg (excerpt) {highlight="5,18"}
[system]
seed = 0

[training]
seed = ${system:seed}

[training.optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
L2_is_weight_decay = true
L2 = 0.01
grad_clip = 1.0
use_averages = false
eps = 1e-8

[pretraining]
optimizer = ${training.optimizer}

You can also use variables inside strings. In that case, it works just like f-strings in Python. If the value of a variable is not a string, it's converted to a string.

[paths]
version = 5
root = "/Users/you/data"
train = "${paths:root}/train_${paths:version}.spacy"
# Result: /Users/you/data/train_5.spacy

If you need to change certain values between training runs, you can define them once, reference them as variables and then override them on the CLI. For example, --paths.root /other/root will change the value of root in the block [paths] and the change will be reflected across all other values that reference this variable.

Model architectures

Metrics, training output and weighted scores

When you train a model using the spacy train command, you'll see a table showing the metrics after each pass over the data. The available metrics depend on the pipeline components. Pipeline components also define which scores are shown and how they should be weighted in the final score that decides about the best model.

The training.score_weights setting in your config.cfg lets you customize the scores shown in the table and how they should be weighted. In this example, the labeled dependency accuracy and NER F-score count towards the final score with 40% each and the tagging accuracy makes up the remaining 20%. The tokenization accuracy and speed are both shown in the table, but not counted towards the score.

Why do I need score weights?

At the end of your training process, you typically want to select the best 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.

[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 or @Language.factory. By default, all pipeline components are weighted equally.

Name	Description
Loss	The training loss representing the amount of work left for the optimizer. Should decrease, but usually not to 0.
Precision (P)	Should increase.
Recall (R)	Should increase.
F-Score (F)	The weighted average 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.

Custom model implementations and architectures

Training with custom code

### Example {wrap="true"}
$ python -m spacy train config.cfg --code functions.py

The spacy 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

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 or 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.
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 that function in the callbacks 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

[nlp.before_creation]
@callbacks = "customize_language_data"

### 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

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.

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

[nlp.before_creation]
@callbacks = "customize_language_data"
extra_stop_words = ["ooh", "aah"]
debug = true

### 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

spaCy's configs are powered by our machine learning library Thinc's configuration system, which supports type hints and even advanced type annotations using 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 an boolean. If the value can't be coerced into a boolean, spaCy will raise an error. start: pydantic.StrictBool will force the value to be an boolean and raise an error if it's not – for instance, if your config defines 1 instead of true.

With your functions.py defining additional code and the updated config.cfg, you can now run spacy 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.

### Training with custom code {wrap="true"}
python -m spacy train config.cfg --output ./output --code ./functions.py

Example: Custom batch size 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 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.

### 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. If your function defines default argument values, spaCy is able to auto-fill your config when you run init config.

### config.cfg (excerpt)
[training.batch_size]
@schedules = "my_custom_schedule.v1"
start = 2
factor = 1.005

Example: Custom data reading and batching

Wrapping PyTorch and TensorFlow

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Defining custom architectures

Transfer learning

Using transformer models like BERT

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 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, that lets you do multi-task learning and lets you save the transformer outputs for later use.

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.

For more details on how to integrate transformer models into your training config and customize the implementations, see the usage guide on training transformers.

Pretraining with spaCy

Parallel Training with Ray

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Internal training API

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 command with a config.cfg to keep track of your settings and hyperparameters, instead of writing your own training scripts from scratch. Custom registered functions should typically give you everything you need to train fully custom models with spacy train.

The Example object contains annotated training data, also called the gold standard. It's initialized with a Doc object that will hold the predictions, and another Doc object that holds the gold-standard annotations. Here's an example of a simple Example for part-of-speech tags:

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:

tag_map = {"N": {"pos": "NOUN"}, "V": {"pos": "VERB"}}
vocab = Vocab(tag_map=tag_map)

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 – 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.

doc = Doc(nlp.vocab, words=["Facebook", "released", "React", "in", "2014"])
example = Example.from_dict(doc, {"entities": ["U-ORG", "O", "U-TECHNOLOGY", "O", "U-DATE"]})

As of v3.0, the Example object replaces the GoldParse class. It can be constructed in a very similar way, from a Doc and a dictionary of annotations:

- gold = GoldParse(doc, entities=entities)
+ example = Example.from_dict(doc, {"entities": entities})

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: The nlp object with the model.
• nlp.begin_training: Start the training and return an optimizer to update the model's weights.
• Optimizer: Function that holds state between updates.
• nlp.update: Update model with examples.
• Example: object holding predictions and gold-standard annotations.
• nlp.to_disk: Save the updated model to a directory.

### 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 method takes the following arguments:

Name	Description
examples	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 object, which updated the model's weights. If not set, spaCy will create a new one and save it for further use.

As of v3.0, the 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 with a Doc and the dictionary of annotations:

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])