---
title: Training Models
next: /usage/projects
menu:
  - ['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 {#basics hidden="true"}

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

<Training101 />

<Infobox title="Tip: Try the Prodigy annotation tool">

[![Prodigy: Radically efficient machine teaching](../images/prodigy.jpg)](https://prodi.gy)

If you need to label a lot of data, check out [Prodigy](https://prodi.gy), 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.

</Infobox>

## Training CLI & config {#cli-config}

<!-- TODO: intro describing the new v3 training philosophy -->

The recommended way to train your spaCy models is via the
[`spacy train`](/api/cli#train) command on the command line.

1. The **training data** in spaCy's
   [binary format](/api/data-formats#binary-training) created using
   [`spacy convert`](/api/cli#convert).
2. A `config.cfg` **configuration file** with all settings and hyperparameters.
3. An optional **Python file** to register
   [custom models and architectures](#custom-models).

<!-- TODO: decide how we want to present the "getting started" workflow here, get a default config etc. -->

<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>

> #### Tip: Debug your data
>
> The [`debug-data` command](/api/cli#debug-data) 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.
>
> ```bash
> $ python -m spacy debug-data en train.json dev.json --verbose
> ```

<Accordion title="Understanding the training output">

When you train a model using the [`spacy train`](/api/cli#train) command, you'll
see a table showing metrics after each pass over the data. Here's what those
metrics means:

<!-- TODO: update table below with updated metrics if needed -->

| 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.

</Accordion>

---

### Training config files {#cli}

<!-- TODO: we need to come up with a good way to present the sections and their expected values visually? -->

<!-- TODO: instead of hard-coding a full config here, we probably want to embed it from GitHub, e.g. from one of the project templates. This also makes it easier to keep it up to date, and the embed widgets take up less space-->

```ini
[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 {#model-architectures}

<!-- TODO: refer to architectures API: /api/architectures. This should document the architectures in spacy/ml/models -->

## Custom model implementations and architectures {#custom-models}

<!-- TODO: document some basic examples for custom models, refer to Thinc, refer to example config/project -->

<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>

### Training with custom code

<!-- TODO: document usage of spacy train with --code -->

## Transfer learning {#transfer-learning}

### Using transformer models like BERT {#transformers}

<!-- TODO: document usage of spacy-transformers, refer to example config/project -->

<Project id="en_core_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.

</Project>

### Pretraining with spaCy {#pretraining}

<!-- TODO: document spacy pretrain -->

## Parallel Training with Ray {#parallel-training}

<!-- TODO: document Ray integration -->

<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}

<!-- TODO: rewrite for new nlp.update / example logic -->

The [`GoldParse`](/api/goldparse) object collects the annotated training
examples, also called the **gold standard**. It's initialized with the
[`Doc`](/api/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:

```python
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](/usage/adding-languages#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
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](/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.

> - **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.

![The training loop](../images/training-loop.svg)

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`](/api/language#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`](/api/language#update): Update the model with the training example
>   and gold data.
> - [`to_disk`](/api/language#to_disk): Save the updated model to a directory.

```python
### 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`](/api/language#update) method takes the following arguments:

| Name    | Description                                                                                                                                                                                                   |
| ------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `docs`  | [`Doc`](/api/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`](/api/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`](/api/cli#train) command, which expects data in spaCy's
[JSON format](/api/data-formats#json-input). On each epoch, a model will be
saved out to the directory.