thinc 6.4.0

Practical Machine Learning for NLP

Thinc is the machine learning library powering spaCy. It features a battle-tested linear model designed for large sparse learning problems, and a flexible neural network model under development for spaCy v2.0.

Thinc is a practical toolkit for implementing models that follow the “Embed, encode, attend, predict” architecture. It’s designed to be easy to install, efficient for CPU usage and optimised for NLP and deep learning with text – in particular, hierarchically structured input and variable-length sequences.

🔮 Version 6.3 out now! Read the release notes here.

Quickstart

If you have Fabric installed, you can use the shortcut:

git clone https://github.com/explosion/thinc
cd thinc
fab clean env make test

You can then run the examples as follows:

fab eg.mnist
fab eg.basic_tagger
fab eg.cnn_tagger

Otherwise, you can build and test explicitly with:

git clone https://github.com/explosion/thinc
cd thinc

virtualenv .env
source .env/bin/activate

pip install -r requirements.txt
python setup.py build_ext --inplace
py.test thinc/

And then run the examples as follows:

python examples/mnist.py
python examples/basic_tagger.py
python examples/cnn_tagger.py

Usage

The Neural Network API is still subject to change, even within minor versions. You can get a feel for the current API by checking out the examples. Here are a few quick highlights.

1. Shape inference

Models can be created with some dimensions unspecified. Missing dimensions are inferred when pre-trained weights are loaded or when training begins. This eliminates a common source of programmer error:

# Invalid network — shape mismatch
model = FeedForward(ReLu(512, 748), ReLu(512, 784), Softmax(10))

# Leave the dimensions unspecified, and you can't be wrong.
model = FeedForward(ReLu(512), ReLu(512), Softmax())

2. Operator overloading

The Model.define_operators() classmethod allows you to bind arbitrary binary functions to Python operators, for use in any Model instance. The method can (and should) be used as a context-manager, so that the overloading is limited to the immediate block. This allows concise and expressive model definition:

with Model.define_operators({'>>': chain}):
    model = ReLu(512) >> ReLu(512) >> Softmax()

The overloading is cleaned up at the end of the block. Only a few functions are currently implemented. The three most useful are:

• chain(model1, model2): Compose two models f(x) and g(x) into a single model computing g(f(x)).

• clone(model1, int): Create n copies of a model, each with distinct weights, and chain them together.

• concatenate(model1, model2): Given two models with output dimensions (n,) and (m,), construct a model with output dimensions (m+n,).

Putting these things together, here’s the sort of tagging model that Thinc is designed to make easy.

with Model.define_operators({'>>': chain, '**': clone, '|': concatenate}):
    model = (
        add_eol_markers('EOL')
        >> flatten
        >> memoize(
            CharLSTM(char_width)
            | (normalize >> str2int >> Embed(word_width)))
        >> ExtractWindow(nW=2)
        >> BatchNorm(ReLu(huidden_width)) ** 3
        >> Softmax()
    )

Not all of these pieces are implemented yet, but hopefully this shows where we’re going. The memoize function will be particularly important: in any batch of text, the common words will be very common. It’s therefore important to evaluate models such as the CharLSTM once per word type per minibatch, rather than once per token.

3. Callback-based backpropagation

Most neural network libraries use a computational graph abstraction. This takes the execution away from you, so that gradients can be computed automatically. Thinc follows a style more like the autograd library, but with larger operations. Usage is as follows:

def explicit_sgd_update(X, y):
    sgd = lambda weights, gradient: weights - gradient * 0.001
    yh, finish_update = model.begin_update(X, drop=0.2)
    finish_update(y-yh, optimizer)

Separating the backpropagation into three parts like this has many advantages. The interface to all models is completely uniform — there is no distinction between the top-level model you use as a predictor and the internal models for the layers. We also make concurrency simple, by making the begin_update() step a pure function, and separating the accumulation of the gradient from the action of the optimizer.

4. Class annotations

To keep the class hierarchy shallow, Thinc uses class decorators to reuse code for layer definitions. Specifically, the following decorators are available:

• describe.attributes(): Allows attributes to be specified by keyword argument. Used especially for dimensions and parameters.

• describe.on_init(): Allows callbacks to be specified, which will be called at the end of the __init__.py.

• describe.on_data(): Allows callbacks to be specified, which will be called on Model.begin_training().