Building a Custom Core ML Model on

Out-of-the box, provides users with a library of starter model examples that cover a variety of machine learning techniques that you can integrate into your iOS application. The purpose of these models is to get users up and running with an ML enabled iOS app as soon as possible! However, it’s quite possible that a new user (like you) will have an idea for a mobile app that none of the available models address. On Skafos, that’s no problem at all! Below I will explore an example of how to build a custom Core ML model and deploy it with the Skafos framework.

Language Modeling

Ever wondered how Apple or Google determine the most likely next word in a text message or email thread? Have an idea for an app to generate a sequence of text influenced by your favorite book, author, or playwright? Then this example is for you!

Text generation is a word-level language modeling task: a core application of natural language processing (NLP) in the real world. A trained language model determines the likelihood of occurrence of a word based on previous words in the text. For example, if I were to say:

“I drank so much water today. I really need to use the ____!”

Most of us are able to intuit that the likely next word is either “restroom” or “bathroom”. This seems natural. Truthfully, it’s just a pattern, and one that a computer can recognize given enough examples to learn from. Just like any ML model, the way in which the next word is selected is based on training data that it has seen. Train a language model on a subset of Shakespeare’s plays, and your app will generate text that sounds like a sixteenth century English playwright. Train it on DC comic books, and you’ll get word generation for superheroes.

How does it work?

Predicting the next likely word in a sequence is no different than predicting tomorrow’s closing stock price for your favorite company, given the last month of price movement. Actually — it’s much easier than that because believe it or not language is a fundamentally sequential (and logical) type of data.

Text Data Magic

The only major difference here is that instead of numbers, we are dealing with words. So, the first step in any type of language modeling approach is to convert the raw text, through a series of pre-processing steps, into sequences of….you guessed it… numbers. We will take the raw text, strip out non-ASCII characters, convert everything to lowercase, remove punctuation, and then map each word to a unique integer.


The next step is converting our newly-processed text into sequences. Sequences of text are the bread and butter of training a word-level language model. For the model to learn the most likely next word given a chain previous of words (normally part of a sentence), we must break up our training data into similar sequences. For example, if we wanted to train a model to take 5 words and predict the next most likely word, our input sequences would need to look something like this for the following sentence:

Figure 1. Mapping words to integers and then creating sequences to use for modeling

Recurrent Neural Networks

Now here’s where the real “magic” comes in. We have pre-processed our text and generated training data sequences for the model. We’ve all likely heard of neural networks at some point. In this example, we leverage a recurrent neural network architecture to model the sequences of text. In short, as we pass these sequences of numbers to the model during training, it retains information about patterns and relationships that tend to be predictive of the next word. For a deeper dive into RNNs, check out this awesome blog post!

Let’s Make One!

For those of you that would prefer to jump straight to the code: here it is.

The rest of this example has the following steps:

  • Step 1: Setup your Skafos project and get the code for steps 2 through 6
  • Step 2: Gather training data
  • Step 3: Pre-process text
  • Step 4: Train the model
  • Step 5: Test the language model
  • Step 6: Save the model to Skafos


First, make sure you have signed up for Skafos and created a log-in. On the dashboard, create a new standard project. If you have just created a new account, you will be presented with a screen like below. Select the “standard project workflow” near the top:

Figure 2. Create a new standard project on Skafos to get started.

Enter a name and description for the project and then navigate to the project’s JupyterLab instance (JLab). This is basically a cloud-based IDE running on Skafos, provisioned just for you:

To get the code that goes with this example, fork this github repo to your own account, and then clone it into your JLab instance using the provided terminal window:

$ git clone<your-account-name>/WordLanguageModel.ipynb

Once you have the code in your JLab, enter the directory and install the python dependencies in the terminal window:

$ pip install -r requirements.txt

Now open up the word_language_model.ipynb” notebook and import the python libraries you just installed:

Figure 3. Import python dependencies.

Gather Training Data

The training data we need to build a language model is just any bit of formatted text (meaning sentences that are for the most part grammatically correct). The more data you can get your hands on, and the more you like the tone and diction of the text, the better your model will meet your standards.

For this example, and for the sake of speed, we will use an existing dataset of Yelp business reviews. This is the same dataset used to train and deploy the Text Classifier model with Skafos. I chose this dataset for the language modeling example because it is familiar, easily accessible, and quite frankly… just funny! Don’t feel like you need to stay with that data beyond this example. In fact, I encourage you to go find your own text data to build a language model on.

Figure 4. Load text training data.

Pre-process Text

Now define a couple helper functions to process the text data we just downloaded:

Figure 5. Helper functions to process the text.
Figure 6. Parse and clean the text.

Now that we have a long list of our cleaned tokens (words), we will organize them into sequences of at max 11 words. Why 11? Well, I want to use an “input length” of 10 for the model. And I then want the model to predict the likely next word. 10 + 1 = 11. I also made sure to handle cases when a sequence splits a sentence into pieces:

Figure 7. Split text into sequences.

Lastly, we need to convert the processed text, now organized in sequences, to numeric form. We do this by mapping each word to a unique integer index. Fortunately, Keras has a nice tool that allows us to do this easily (Tokenizer).

Figure 8. Convert sequences of text to sequences of integers.

One last thing! Before building the model, we need to split the data into “X” and “y”. What is this? In order for a ML model to learn patterns, we explicitly define some input data (X) that maps to an output (y). In this case, our initial input of 10 words is our X, and the last word of the sequence is our y. We use array slicing to achieve this:

Figure 9. Split into X and y for model training.

Train the Model

First things first, we declare a base neural network class for us to start building our architecture, layer by layer. The first layer in the network is an embedding layer that takes a sequence of 10 integers (each mapping to a corresponding word) and extracts a 32-length vector for each to use as inputs to the LSTM layer (this is the recurrent layer of the network). Lastly, we use 2 Dense layers to output the probabilities of each word in the vocabulary given the inputs. I included links to the Keras documentation where you can learn more about each specific layer.

Figure 10. Construct and train a RNN model.

With any neural network, picking the right hyper-parameters is key (and also more art than science, if you ask me). Most of those choices depend on your training data, time frame, and required performance threshold.

The most common parameters to tweak include:

  • Number of training epochs
  • Number of samples included in a batch (a bunch of small batches make up an epoch)
  • The loss function, optimizer, and evaluation metric
  • Number of hidden layers (LSTM, Dense, etc… use more or less)
  • Number of units within each layer

But honestly, don’t even worry about that part until you get something working end-to-end. So many data scientists and machine learning engineers get lost in this step. We iterate for weeks on end, following a rigorous scientific method, but often run into integration challenges after they have burned precious weeks building the perfect model. Don’t do that.

Get your model hooked up to your app first, and then iterate on it. Because Skafos can “push” model updates to your app, you can iterate to your heart’s desire.

Test the Language Model

Now that the model has been trained, here is a function you can use (in python) to generate new text based on some input. This is a good thing to do in the JLab before deploying to your app to make sure it’s doing what you’d expect.

Figure 11. Code to test the language model by generating new sequences of text given some seed text.

Give it a whirl! Try out other seed text to see where the model takes it. The longer you train, the more likely it will make sense. However, the longer you train, the more likely the model will over fit to the particular linguistic style of the text you trained on. The results can be quite humorous..

Figure 12. Try out the language model!

Save the Model

To push this model to your iOS app, you need to do the following:

Convert the model to Core ML format: This makes the model class accessible in Xcode.

Figure 13. Export model to Core ML format.

If you haven’t already done so, configure your iOS app settings with Skafos on the project page of the dashboard. Enter the required ID’s and Keys, then head over to the integration guide.

Figure 14. Project page — model delivery settings for integrating with iOS app.

During step 1 of the integration guide, instead of downloading an initial model from the drop down list, click here to download a pre-trained model along with a word-index dictionary JSON file (zipped). Add those to your Xcode project resources.

From here on out, the Skafos framework will handle automated model updates to your devices. You can trigger model updates over-the-air using the Skafos SDK save and deliver methods.

This way, you can retrain or update your model in the JLab environment on Skafos, and have your changes propagate to your applications.


This model is a bit more advanced, so it is a bit trickier to deploy and use on devices (I trust you can still do it)! Remember that pre-processing work we did, converting text to integers and what not? Well, in order to use a language model like this in your app, you also need to include the mappings so that the results of the model can be translated into human readable text. Each time you save and deliver a model, you also need to include the word-index dictionary.json file that you can integrate into your app. Skafos can deliver these as a zipped bundle!

Coming Soon

Stay tuned for another blog post coming that will show you how to build a sample iOS application using the model we just saved.

Next Steps

Thanks for following along! I hope you learned something new and now have the tools to build an awesome language model. Get after it!

  • Fork and Clone the project repository in a Skafos Jupyter Lab.
  • Try swapping out the text training data for something else. Shakespeare, movie scripts, text messages, etc.
  • Adjust the number of training epochs or tweak the number of hidden layers in the neural network by removing them from the model constructor code.
  • Here is a list of some other iOS app templates that you can use to get rolling!
  • Reach out to us on our Slack Channel or find us on Reddit.