Hands-on labeling of text data using the Snorkel API – Labeling Text Data-1

In this section, we are going to learn how to label text data using the Snorkel API.

Snorkel provides an API for programmatically labeling text data using a small set of ground truth labels that are created by domain experts. Snorkel, an open source data labeling and training platform, is used by various companies and organizations across different industries, such as Google, Apple, Facebook, IBM, and SAP.

It has unique features that differentiate it from other competitors, especially in the context of weak supervision and programmatically generating labeled data. Here’s a comparison with some of the other tools:

• Weak supervision: Snorkel excels in scenarios where labeled data is scarce, and manual labeling is expensive. It allows users to programmatically label large amounts of data using heuristics, patterns, and external resources.
• Flexible labeling functions: Snorkel enables the creation of labeling functions, which are essentially heuristic functions that assign labels to data. This provides a flexible and scalable way to generate labeled data.
• Probabilistic labeling: Snorkel generates probabilistic labels, acknowledging that labeling functions may have varying levels of accuracy. This probabilistic framework is useful in downstream tasks.

There can be a learning curve with Snorkel, especially for users who are new to weak supervision concepts. Other tools, such as Prodigy and Labelbox, are commercial tools and may involve licensing costs.

When choosing between these tools, the specific requirements of the project, the available budget, and the expertise of the users play crucial roles. Snorkel stands out when weak supervision and programmatically generated labels are essential for the task at hand. It’s particularly well suited for scenarios where manual labeling is impractical or cost-prohibitive. Other tools may be more appropriate based on different use cases, interface preferences, and integration requirements.

We will create rule-based labeling functions using Snorkel and then apply these labeling functions to classify and label text.

We have seen what a labeling function is and how to create labeling functions in Chapter 2. Let’s recap. In Snorkel, a labeling function is a Python function that heuristically generates labels for a dataset. These functions are used in the process of weak supervision, where instead of relying solely on manually labeled data, a machine learning model is trained using noisy, imperfect, or weakly labeled data.

Here is an example Python code that uses the Snorkel API to label text data using rule-based labeling functions.

Let’s install Snorkel using pip and import the required Python libraries for labeling as follows:
!pip install snorkel

Let’s break down the code into four steps and explain each one.