How to use patterns for multilabel text classification annotation in Prodigy

Photo by George Pagan III on Unsplash

Prodigy is a great tool for annotating the datasets needed to train machine learning models. It has built in support for many kinds of tasks, from text classification, to named entity recognition and even for image and audio annotation.

One of the cool things about Prodigy is that it integrates with Spacy (they are created by the same company), so you can use active learning (having a model suggest annotations and then being corrected by humans) or you can leverage Spacy patterns to automatically suggest annotations.

Prodigy has various recipes for these things, but it doesn’t come with a recipe to use only patterns for manual annotation for a multilabel text classification problem, only in combination with an active learning loop. The problem is that for multi-label annotation, Prodigy does binary annotation for each document, meaning the human annotator will be shown only one label at a time and they’ll have to decide if it’s relevant to the document or not. If you have many labels, it means each document might be shown as many times as there are labels.

I recently had to solve a problem where I knew that most of the documents would have a single label, but in a few cases there would be multiple labels. I also had some pretty good patterns to help bootstrap the process, so I wrote a custom recipe that used only patterns for a multilabel text classification problem.

Code for custom recipe

To do this, I combined some code from the recipes that are provided by Prodigy for text categorization. Let’s see how it work.

First, let’s define the CLI arguments in a file called manual_patterns.py. We’ll need:

@recipe(
    "textcat.manual_patterns",  # Name of the recipe
    dataset=("Dataset to save annotations to", "positional", None, str),
    source=("File path with data to annotate", "positional", None, str),
    spacy_model=("Loadable spaCy pipeline or blank:lang (e.g. blank:en)", "positional", None, str),
    labels=("Comma-separated label(s) to annotate or text file with one label per line", "option", "l", get_labels),
    patterns=("Path to match patterns file", "option", "pt", str),
)

Then we need to define the function that loads the stream of data, runs the PhraseMatcher on it and returns the project config:

def manual(
    dataset: str,
    source: Union[str, Iterable[dict]],
    spacy_model: str,
    labels: Optional[List[str]] = None,
    patterns: Optional[str] = None,
):
    log("RECIPE: Starting recipe textcat.manual_patterns", locals())
    log(f"RECIPE: Annotating with {len(labels)} labels", labels)
    stream = get_stream(
        source, rehash=True, dedup=True, input_key="text"
    )
    nlp = spacy.load(spacy_model)

    matcher = PatternMatcher(nlp, prior_correct=5.0, prior_incorrect=5.0,
        label_span=False, label_task=True, filter_labels=labels,
        combine_matches=True, task_hash_keys=("label",),
    )
    matcher = matcher.from_disk(patterns)
    stream = add_suggestions(stream, matcher, labels)

    return {
        "view_id": "choice",
        "dataset": dataset,
        "stream": stream,
        "config": {
            "labels": labels,
            "choice_style": "multiple",
            "choice_auto_accept": False,
            "exclude_by": "task",
            "auto_count_stream": True,
        },
    }

The last bit is the function which takes the suggestions generated by the PhraseMatcher and adds them to be selected by default in the UI. In this way, the annotators can quickly accept them:

def add_suggestions(stream, matcher, labels):
    texts = (eg for score, eg in matcher(stream))
    options = [{"id": label, "text": label} for label in labels]

    for eg in texts:
        task = copy.deepcopy(eg)

        task["options"] = options
        if 'label' in task:
            task["accept"] = [task['label']]
            del task['label']
        yield task

Expected file formats

Now let’s run the recipe. Assuming we have an news_headlines.jsonl file in the following format:

{"text":"Pearl Automation, Founded by Apple Veterans, Shuts Down"}
{"text":"Silicon Valley Investors Flexed Their Muscles in Uber Fight"}
{"text":"Uber is a Creature of an Industry Struggling to Grow Up"}
{"text": "Brad Pitt is divorcing Angelina Jolie"}
{"text": "Physicists discover new exotic particle"}

And an pattern file patterns.jsonl:

{"pattern": "Uber", "label": "Technology"}
{"pattern": "Brad Pitt", "label": "Entertainment"}
{"pattern": "Angelina Jolie", "label": "Entertainment"}
{"pattern": "physicists", "label": "Science"}

Running the custom recipe

You can start Prodigy with the following command:

> python -m prodigy textcat.manual_patterns news_headlines news_headlines.jsonl  blank:en --label "Science,Technology,Entertainment,Politics" --patterns patterns.jsonl -F .\manual_patterns.py

Using 4 label(s): Science, Technology, Entertainment, Politics
Added dataset news_headlines to database SQLite.
D:\Work\staa\prodigy_models\manual_patterns.py:67: UserWarning: [W036] The component 'matcher' does not have any patterns defined.
  texts = (eg for score, eg in matcher(stream))

✨  Starting the web server at http://localhost:8080 ...
Open the app in your browser and start annotating!

And you should see the following in the browser:

The full code for the recipe can be found here.

The Best Text Classification library for a Quick Baseline

Text classification is a very frequent use case for machine learning (ML) and natural language processing (NLP). It’s used for things like spam detection in emails, sentiment analysis for social media posts, or intent detection in chat bots.

In this series I am going to compare several libraries that can be used to train text classification models.

The fastText library

fastText is a tool from Facebook made specifically for efficient text classification. It’s written in C++ and optimized for multi-core training, so it’s very fast, being able to process hundreds of thousands of words per second per core. It’s very straightforward to use, either as a Python library or through a CLI tool.

Despite using an older machine learning model (a neural network architecture from 2016), fastText is still very competitive and provides an excellent baseline. If you also take into account resource usage, it will be all but impossible to improve on the fastText results, considering that the only models that perform better require powerful GPUs.

Getting started with text classification with fastText

fastText requires the training data for text classification to be in a special format: each document should be on a single line and the labels should be at the start of the line, with the prefix __label__, like this:

Training data format

__label__sauce __label__cheese How much does potato starch affect a cheese sauce recipe?
 __label__food-safety __label__acidity Dangerous pathogens capable of growing in acidic environments
 __label__cast-iron __label__stove How do I cover up the white spots on my cast iron stove?

If you use Doccano for annotating the text data, it has an option to export the data in fastText format. But even if you used another tool for annotation, it’s only a couple of lines of Python code to convert to the appropriate format. Let’s say we have our data in a JSONL format, with each JSON object having a labels key and a text key. To convert to fastText format, we can use the following short snippet:

with open("fasttext.txt", "w") as output:
    with open("dataset.jsonl", encoding="utf8") as f:
        for l in f:
            doc = json.loads(l)
            labels = [x.replace(" ", "_") for x in doc['labels']]
            labels = " ".join(f"__label__{x}" for x in labels)
            txt = " ".join(l['text'].splitlines())
            line = f"{labels} {txt}\n"
            output.write(line)

Training text classification models with fastText

After you have the data in the right format, the simplest way to use fastText is through it’s CLI tool. After you installed it, you can train a model with the supervised subcommand:

> ./fasttext supervised -input fasttext.txt -output model
Read 0M words
Number of words:  16568
Number of labels: 736
Progress: 100.0% words/sec/thread:   47065 lr:  0.000000 avg.loss: 10.027837 ETA:   0h 0m 0s

You can evaluate the model on a separate dataset with the test subcommand and you will get the precision and recall for the first candidate label:

> ./fasttext test model.bin validation.txt
N       15404
P@1     0.162
R@1     0.0701

You can also get predictions for new documents:

> ./fasttext predict model.bin -
How to make lasagna?
__label__baking
Best way to chop meat
__label__food-safety
How to store steak
__label__food-safety

fastText comes with a builtin hyperparameter optimizer, to find the best model on a validation dataset, within the given time (5 minutes by default):

> ./fasttext supervised -input fasttext.txt -output model -autotune-validation validation.txt

If we reevaluate this model we’ll find it performs much better:

> ./fasttext test model.bin validation.txt
N       15404
P@1     0.727
R@1     0.315

A precision of 0.72, compared to 0.16 before. Not bad, for 10 minutes of our time, out of which 5 was waiting for the computer to find us a better model1Autotuning and performance evaluation should happen on separate datasets, to avoid overfitting, so real world performance is likely a bit worse than we got here.

Optimizing for different metrics

This library provides a couple of knobs you can use to try to obtain better models, from what kind of n-grams to use, how big the learning rate should be, what should be the loss function, but also what metric are you trying to optimize. Is precision or recall better aligned with your business KPIs? Is it more important to have the top result be a really good one or are you looking for several good results among in the top 5? Are you only interested in high confidence results? All this depends on the problem you are trying to solve and fastText provides ways to optimize for each of those.

Cons of fastText

Of course, fastText has some disadvantages:

  • Not much flexibility – only one neural network architecture from 2016 implemented with very few parameters to tune
  • No option to speed up using GPU
  • Can be used only for text classification and word embeddings
  • Doesn’t have too wide support in other tools (for deployments for example)

Conclusion

fastText is a great library to use when you want to start solving a text classification problem. In less than half an hour, you can get a good baseline going, which will tell you if this is a problem that is worth pursuing or not.