As you already know, I’m a big fan of spaCy’s configuration and project system. It’s a seamless way to integrate your model’s configration into your overall project workflow. So this time, let’s dive deeper and understand what happens under the hood whenever we initialize a pipeline.

This blog post will walk you through how to initialize a span categorizer (spancat) pipeline manually. By that, I mean taking advantage of spaCy’s registry system. If you’re a spaCy user, I find no practical application in this—I only use this method for debugging purposes as a spaCy developer (for curiosity’s sake). But I decided to write about this topic to appreciate the neatness of spaCy’s API design, and maybe you can use this pattern in your tools too!

Note: after publishing I noticed that the diagrams are too small when you view them in mobile, sorry about that! It might take some time for me to update them so no promises! The illustrations are high-res though, so you can open them in a new tab as we go along.

The catalogue library

First things first, spaCy’s registry system is managed by the catalogue library. It allows us to create serializable function callbacks. So let’s say we’re writing a module that loads different types of data (e.g., PDF, PNG, JEPG) for a document processing workflow. One way to do that is to write a handler for every data type:

from pathlib import Path

def handle_pdf(path: Path):
    # pdf specific logic
    return something

def handle_png(path: Path):
    # png specific logic
    return something

def handle_jpeg(path: Path):
    # jpg specific logic
    return something

Then you might have a function that takes an arbitrary data type and a specific handler:

from pathlib import Path
from typing import Callable

def load_data(path: Path, handler: Callable):
    something = handler(path)
    return something

# Usage: load_data("path/to/file.pdf", handler=handle_pdf)

This design is neat and acceptable, but we cannot create serializable records (read: something you can log into a file, save into JSON, pickle or joblib, etc.) for these functions because Callable objects are not so straightforward to serialize.1 This becomes important when you want to “save” the settings for a particular function and reuse them later.

The catalogue library solves this by registering your function so that you can just use strings as your handler parameter.

import catalogue

loaders = catalogue.create("my_project", "loaders")

The loaders Registry acts as an intermediate object for registering and retrieving functions. You can use it as a decorator for your custom loaders:

from pathlib import Path
from my_scripts import loaders  # type: Registry

def handle_pdf(path: Path):
    # pdf specific logic
    return something

def handle_png(path: Path):
    # png specific logic
    return something

def handle_jpeg(path: Path):
    # jpg specific logic
    return something

We can then rewrite the main handler to use strings (str ) instead of functions:

from pathlib import Path
from my_script import loaders

def load_data(path: Path, handler_id: str):
    # We're now using the handler_id to retrieve the correct handler
    handler = loaders.get(handler_id)
    something = handler(path)
    return something

# Usage: load_data("path/to/file.pdf", handler_id="pdf")

Factory design pattern

The factory design pattern takes advantage of this setup. For example, if you have a function that takes additional parameters, then you can use them to set up a factory so that the resulting function behaves in a certain way:

def build_pdf_handler(page_num: int, n_jobs: int) -> Callable:
    def _pdf_handler(path: Path):
        pages = load_pages(path, n_jobs=n_jobs)
        page_to_load = pages[page_num]
        # other pdf specific logic
        return something
    return _pdf_handler

# Usage:
# > pdf_handler_factory = loaders.get("pdf")
# > pdf_handler = pdf_handler_factory(page_num=1, n_jobs=3)
# > pdf_handler("path/to/file.pdf")

This design pattern makes up the bulk of spaCy’s pipeline initialization process. In the next section, we’ll look into the spancat architecture and build the pipeline manually using the registry system.

Spancat architecture walkthrough

Now we’ll see catalogue in action in spancat. If this is the first time you heard about spaCy’s SpanCategorizer, then head over to this blogpost for a brief overview.2 In summary, spancat is a highly-configurable component that performs span labelling à la NER. It does more than NER though because it can handle overlapping, long, and fragmented spans. Its architecture looks like this:

It looks complex now, but if we start from the very right, we see our end goal, i.e., to initialize the Span Categorizer (spancat) pipeline. Doing so requires a suggester and a model, with the latter requiring a tok2vec, reducer, and so on. We’ll go through the whole dependency chain as we go.

Let’s first load the registry, and create example Doc objects:

import spacy  # as of writing, I'm using v3.3.1
reg = spacy.registry  # shorthand

For the texts, let’s have two sentences:

from typing import List
from spacy.tokens import Doc
import spacy

nlp = spacy.blank("en")
texts = ["A short sentence.", "Welcome to the Bank of China."]
docs: List[Doc] = list(nlp.pipe(texts))

Suggester function

Let’s start with the suggester function because it has no dependencies. In spancat, the suggester function suggests candidate spans that we will feed later on into a multilabel classifier for labelling. The simplest way to suggest spans is to sweep with a window of a fixed n-gram length (ngram_suggester.v1).

suggester_factory = reg.get("misc", "spacy.ngram_suggester.v1")
suggester: Callable = suggester_factory(sizes=[1, 2, 3])

The factory approach is similar to our earlier example in build_pdf_handler. However, this time, we now have access to the suggester function, and we can pass any Doc objects (Iterable[Doc]) to see their candidate spans. Let’s do that now:

candidates = suggester(docs)

# > Ragged(
# >    data=array([
# >        [0, 1], [1, 2], [2, 3], [3, 4], [0, 2], [1, 3], [2, 4], [0, 3], [1, 4],
# >        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7], [0, 2], [1, 3],
# >        [2, 4], [3, 5], [4, 6], [5, 7], [0, 3], [1, 4], [2, 5], [3, 6], [4, 7]], dtype=int32), 
# >    lengths=array([ 9, 18], dtype=int32), 
# >    data_shape=(-1, 2), 
# >    starts_ends=None
# > ) 

The suggester function gave us a list of start and end indices in the attribute and the number of candidates per document in candidates.lengths. We can view the spans by indexing through the document:

## View the spans for the first document: 'A short sentence.'

RELEVANT_DOC_IDX = 0 # because 'A short sentence is the 0th element in the list
doc = docs[RELEVANT_DOC_IDX]  
num_candidates: int = candidates.lengths[RELEVANT_DOC_IDX]
window_idxs: Iterable[Tuple[int, int]] =[:num_candidates].tolist()

for span_idx, (start, end) in enumerate(window_idxs):
    print(f"Span {span_idx}: {doc[start:end]}")

# > A short sentence.
# > Span 0: A
# > Span 1: short
# > Span 2: sentence
# > Span 3: .
# > Span 4: A short
# > Span 5: short sentence
# > Span 6: sentence.
# > Span 7: A short sentence
# > Span 8: short sentence.

As expected, the candidate spans are simply the n-gram sweeps of the document. In spacy-experimental, you can access other suggesters based on a text’s linguistic attributes. There’s also the SpanFinder that learns span boundaries and suggests fewer but more precise candidates.


So far, we already have a suggester. This box is now grayed out in the illustration below. The next step is to create our model. However, its parameters also require us to initialize other components, namely the tok2vec, reducer, and scorer. Let’s start with tok2vec.

For spancat, we will use the built-in spacy.Tok2Vec.v2 architecture for the tok2vec parameter. However, it also requires us to pass something in its embed and encode parameters. For embed we will be using spacy.MultiHashEmbed.v2 and for encode we will be using spacy.MaxoutWindowEncoder.v2.3

# Get factories
tok2vec_factory = reg.get("architectures", "spacy.Tok2Vec.v2")
embed_factory = reg.get("architectures", "spacy.MultiHashEmbed.v2")
encode_factory = reg.get("architectures", "spacy.MaxoutWindowEncoder.v2")

# Construct components
embed = embed_factory(
    rows=[5000, 2000, 1000, 1000],
    attrs=["ORTH", "PREFIX", "SUFFIX", "SHAPE"],
encode = encode_factory(

# Assemble
tok2vec = tok2vec_factory(embed=embed, encode=encode)

With this, we now have a tok2vec network to pass to the spacy.SpanCategorizer.v1 model. The remaining parameters are the reducer and the scorer.

We will follow the same process for the last two: we get the factories and then construct the components:

# Get factories
reducer_factory = reg.get("layers", "spacy.mean_max_reducer.v1")
scorer_factory = reg.get("layers", "spacy.LinearLogistic.v1")

# Construct components
reducer = reducer_factory(hidden_size=128)
scorer = scorer_factory()

And because we now have the tok2vec, reducer, and scorer, we can assemble them together for the spancat model:

model_factory = reg.get("architectures", "spacy.SpanCategorizer.v1")
model = model_factory(tok2vec=tok2vec, reducer=reducer, scorer=scorer)


Now that we have the model and suggester, we can finally assemble the spancat pipeline. There are other parameters we need to provide like nlp, but we don’t need to construct any of them unlike the previous components.

Optionally, we can also add a Scorer to store our results. We need to construct from a factory again but this should be quick and easy:

spacy_scorer_factory = reg.get("scorers", "spacy.spancat_scorer.v1")
spacy_scorer = spacy_scorer_factory()

What spacy.spancat_scorer.v1 does is that it adds the spans_sc_{} values in the spacy.Scorer so that it gets reported during training and evaluation. The component itself does not affect the model, it only reports what the original scorer, i.e., spacy.LinearLogistic.v1, has computed.

With that out of the way, let’s create our pipeline:

pipeline_factory = reg.get("factories", "spancat")
pipeline = pipeline_factory(
    nlp=nlp,  # a blank:en pipeline
    # These are the two that we constructed earlier
    # The optional spaCy scorer

# Initialize the pipeline for training
pipeline.initialize(lambda: [], nlp=nlp)

From here, we can now access anything in the SpanCategorizer pipeline. For example, we can train our model by calling the pipeline.update function. However, it’s more ideal to use the training and config system for that purpose.

Final thoughts

In this blog post, we went through spaCy’s internals by initializing the spancat pipeline through the registry system. We also learned about the catalogue library that handles this process and the factory design pattern it enables.

Perhaps the question you’re asking is: so what? These processes happen under the hood, so there’s no need to bother ourselves with it, right? Personally, I find it a fun exercise to go through the library’s intricacies. As a developer, being able to access the components through the registry allows me to:

  • Debug components by piecemeal: if I’m writing a suggester function, I don’t need to spawn a spaCy project just to see how it works. I will just call reg.get("misc", "my_custom_suggester.v1") for testing.
  • Understand that I can substitute any component in the spancat pipeline for another. For example, I can change the scorer function to Softmax instead of LinearLogistic.
  • Appreciate how custom components in spaCy work. The registry API design is quite neat because it allows extensibility not only for library developers but also for users. It’s another design pattern I can add to my toolbelt when writing libraries!

I hope you enjoyed reading this blog post as much as I enjoyed writing it. If you have any questions, feel free to post a comment below.


  1. You can probably use dill for serialization, or serialize the function’s bytecode. However, these things are tricky as compared to just using strings. 

  2. While you’re there, why not check out this awesome video that applies span categorization in a practical use case. 

  3. I encourage you to read the “Embed, encode, attend, predict” blog post from Explosion to see the theoretical background behind the embed and encode design.