spaCy/website/docs/usage/training.md

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Training Models	/usage/projects	Introduction basics CLI & Config cli-config Custom Models custom-models Transfer Learning transfer-learning Parallel Training parallel-training Internal API api

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.

Training CLI & config

The recommended way to train your spaCy models is via the spacy train command on the command line.

1. The training data in spaCy's binary format created using spacy convert.
2. A config.cfg configuration file with all settings and hyperparameters.
3. An optional Python file to register custom models and architectures.

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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 en train.json dev.json --verbose

When you train a model using the spacy train command, you'll see a table showing metrics after each pass over the data. Here's what those metrics means:

Name	Description
Dep Loss	Training loss for dependency parser. Should decrease, but usually not to 0.
NER Loss	Training loss for named entity recognizer. Should decrease, but usually not to 0.
UAS	Unlabeled attachment score for parser. The percentage of unlabeled correct arcs. Should increase.
NER P.	NER precision on development data. Should increase.
NER R.	NER recall on development data. Should increase.
NER F.	NER F-score on development data. Should increase.
Tag %	Fine-grained part-of-speech tag accuracy on development data. Should increase.
Token %	Tokenization accuracy on development data.
CPU WPS	Prediction speed on CPU in words per second, if available. Should stay stable.
GPU WPS	Prediction speed on GPU in words per second, if available. 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.

---

Training config files

[training]
use_gpu = -1
limit = 0
dropout = 0.2
patience = 1000
eval_frequency = 20
scores = ["ents_p", "ents_r", "ents_f"]
score_weights = {"ents_f": 1}
orth_variant_level = 0.0
gold_preproc = false
max_length = 0
seed = 0
accumulate_gradient = 1
discard_oversize = false

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

[training.optimizer]
@optimizers = "Adam.v1"
learn_rate = 0.001
beta1 = 0.9
beta2 = 0.999
use_averages = false

[nlp]
lang = "en"
vectors = null

[nlp.pipeline.ner]
factory = "ner"

[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
hidden_width = 128
maxout_pieces = 3
use_upper = true

[nlp.pipeline.ner.model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
width = 128
depth = 4
embed_size = 7000
maxout_pieces = 3
window_size = 1
subword_features = true
pretrained_vectors = null
dropout = null

Model architectures

Custom model implementations and architectures

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Training with custom code

Transfer learning

Using transformer models like BERT

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.

Pretraining with spaCy

Parallel Training with Ray

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Internal training API

The GoldParse object collects the annotated training examples, also called the gold standard. It's initialized with the Doc object it refers to, and keyword arguments specifying the annotations, like tags or entities. Its job is to encode the annotations, keep them aligned and create the C-level data structures required for efficient access. Here's an example of a simple GoldParse for part-of-speech tags:

vocab = Vocab(tag_map={"N": {"pos": "NOUN"}, "V": {"pos": "VERB"}})
doc = Doc(vocab, words=["I", "like", "stuff"])
gold = GoldParse(doc, tags=["N", "V", "N"])

Using the Doc and its gold-standard annotations, the model can be updated to learn a sentence of three words with their assigned part-of-speech tags. 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.

doc = Doc(Vocab(), words=["Facebook", "released", "React", "in", "2014"])
gold = GoldParse(doc, entities=["U-ORG", "O", "U-TECHNOLOGY", "O", "U-DATE"])

The same goes for 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.

• Training data: The training examples.
• Text and label: The current example.
• Doc: A Doc object created from the example text.
• GoldParse: A GoldParse object of the Doc and label.
• nlp: The nlp object with the model.
• Optimizer: A function that holds state between updates.
• Update: Update the model's weights.

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.

• begin_training: Start the training and return an optimizer function to update the model's weights. Can take an optional function converting the training data to spaCy's training format.
• update: Update the model with the training example and gold data.
• to_disk: Save the updated model to a directory.

### Example training loop
optimizer = nlp.begin_training(get_data)
for itn in range(100):
    random.shuffle(train_data)
    for raw_text, entity_offsets in train_data:
        doc = nlp.make_doc(raw_text)
        gold = GoldParse(doc, entities=entity_offsets)
        nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
nlp.to_disk("/model")

The nlp.update method takes the following arguments:

Name	Description
docs	Doc objects. The update method takes a sequence of them, so you can batch up your training examples. Alternatively, you can also pass in a sequence of raw texts.
golds	GoldParse objects. The update method takes a sequence of them, so you can batch up your training examples. Alternatively, you can also pass in a dictionary containing the annotations.
drop	Dropout rate. Makes it harder for the model to just memorize the data.
sgd	An optimizer, i.e. a callable to update the model's weights. If not set, spaCy will create a new one and save it for further use.

Instead of writing your own training loop, you can also use the built-in train command, which expects data in spaCy's JSON format. On each epoch, a model will be saved out to the directory.