Have you ever asked a voice assistant to play your favorite music or used a language translation app on your phone? If so, you’ve experienced the power of natural language processing.
But what exactly is NLP and how does it work?
Well, simply put, NLP is a subfield of machine learning that enables computers to understand, interpret, and process human language. While humans can speak multiple languages fluently, computers traditionally only understand machine language, a series of ones and zeros that are difficult for most people to comprehend.
To bridge this gap and allow for communication between humans and computers, various programming languages have been developed over the years, including early methods like punched cards and more modern languages like C and C++. However, none of these have been able to fully understand natural language.
That’s where natural language processing comes in.
By using machine learning algorithms, NLP allows computers to interpret and respond to human language in a way that feels almost natural. For example, when you say, “Okay, Google, play me some music,” a voice assistant can understand your request and ask which app you’d like to use—YouTube or Spotify. Furthermore, if you say, “I like this song,” the assistant can respond with, “Rating written” and adjust its algorithms to play that song the next time you ask for your favorite music.
Although computers still have ways to go before they can fully understand and mimic human language, the progress made through NLP is impressive. It’s a time-saving technology that can make it feel like you’re communicating with a real person rather than a machine operating on a series of ones and zeros.
While natural language processing can be incredibly useful, implementing machine learning models for NLP can be complex and require a deep understanding of artificial intelligence.
The process typically involves several steps, such as implementing the model, loading pretrained weights, preprocessing data, feeding data into the model, and defining the training loop with optimizers, objective functions, and data loaders.
These steps can vary depending on the type of data and the problem being addressed, making them difficult to navigate for those without a background in machine learning.
Having said that, there’s a solution that makes it easy for anyone to take advantage of NLP: the Hugging Face library.
This tool offers access to a wide range of predefined NLP models with a consistent interface, making it a piece of cake for non-experts to use and implement NLP models for various problems. It also provides all the necessary resources for data preprocessing and other tasks.
In short, the Hugging Face library makes it incredibly easy for anyone to take advantage of the power of NLP without needing a degree in machine learning.
Hugging Face makes performing even complex NLP tasks with just a few lines of code a no-brainer, thanks to its high-level abstraction pipelines. And don’t worry, it doesn’t mean you can’t customize your pipeline or define individual steps yourself. These more advanced techniques are covered in this article.
But before diving into solving natural language processing tasks with the Hugging Face library, let’s take a look at a simple example of NLP in action. For this, we’ll start by preparing a piece of text:
This text represents a review of STX Next’s services by one of their clients. Of course, the text you use could be different in length and topic.
The truth is, natural language processing tasks can be quite complex and time-consuming, but they don’t have to be. The Hugging Face library is a game-changer for NLP, offering a wide range of pretrained models and an intuitive interface that makes it easy to get started. With this tool, you can achieve your NLP goals faster and more accurately, no matter your background or expertise.
Whether you’re a machine learning expert looking to streamline your workflow or a business owner seeking to harness the power of NLP to gain insights and make data-driven decisions, the Hugging Face library has something to offer.
Let’s now take a look at the main use cases of Hugging Face using the example of our review mentioned above. On its basis, we’ll show you the countless ways in which you can use the Hugging face library to make your natural language processing tasks a trifle.
Why should you waste time and energy struggling with complex NLP tasks when the Hugging Face library is available? You can start leveraging the power of this amazing tool right away. In the next sections, you’ll see just how much it can help you achieve your NLP goals.
One of the most popular uses of NLP is sentiment analysis, the process of determining whether a piece of text conveys a positive, negative, or neutral sentiment.
This technique has a wide range of applications, including monitoring social media for customer feedback on products or services, making it easier to identify areas for improvement.
Sentiment analysis can also be used to identify the reputation of an entire brand, rather than just a single product. It allows your company to recognize the first signs of negative reviews and take action to mitigate the situation while it’s still possible.
In addition to analyzing your own products and services, you can also monitor how competitors are perceived by customers. This also allows you to see how particular promotional campaigns may be perceived by your audience.
To get started with sentiment analysis using the Hugging Face library, you first need to import the pipeline class from the Transformers library and initialize it with the name of our downstream task as an argument, which in this case is “sentiment-analysis”:
Sentiment analysis is essentially a text classification task, which means you need a model that can take text data as input and classify it into one of several predefined categories as output. For this reason, when you run the above code, the Hugging Face library will automatically load a model that’s capable of classifying the sentiment of the input text.
Once your model is loaded, you can use it to classify the sentiment of your text. Here’s an example using the review you prepared earlier:
This will produce the following output:
As we can see, the model has identified a positive sentiment, which matches the overall tone of the review.
Easy, right? If you’re feeling up to it, try tackling some more NLP tasks with the Hugging Face library. We’ll cover them in the next sections.
This is probably the most interesting task on our list. Zero-shot classification is an exciting and useful NLP task that allows you to classify text from any angle without first having to train a model specifically for that task.
Using a pretrained model, you can “ask” it to “look” at your text from a specific perspective and classify it into predetermined categories. This approach is commonly used in commercial settings.
Text classification can be applied in a variety of contexts, such as categorizing sets of text data in ecommerce or tagging content on social media platforms to improve browsing or search capabilities. It can also be used to tag important tasks in a CRM system or filter out spam.
To use the zero-shot classification feature in the Hugging Face library, you’ll first need to define the appropriate pipeline:
Then, you can use it by providing the input text and a list of classes you want to classify the text into. For example, if you want to know if your text is about customer reviews, automotive, or agriculture, you can run the following code:
This will produce the following output:
As you can see, the output correctly indicates that our text is a review.
Named entity recognition (NER) involves detecting named entities such as products, places, and people in the text.
One of the most common uses of NER is to improve text search algorithms by identifying the most relevant words in a given text and reducing the volume of searched text to increase search speed. It can also be used to automate the recommendation process by representing content with tags recognized by NER and suggesting articles with common tags.
So how to use the named entity recognition models? Again, the whole process starts with defining the right pipeline:
Next, you’ll modify the text you’ve been working with to make it more suitable for this task:
Now, you can use the pipeline to identify named entities in the text:
The aggregation_strategy argument determines how to group words that belong to the same entity. For example, a single “STX Next” entity consists of two words. The output will look like this:
The score column in the output consists of the probabilities that the individual words belong to the specified categories, such as: person (PER), organization (ORG), location (LOC), or miscellaneous entities (MISC).
The model was able to correctly identify named entities in the text, such as “STX Next,” “Maciej Dziergwa,” and “Poznań University of Technology.” As you can see, NER can be a really handy tool for improving search algorithms, automating recommendations, and more.
Have you ever used a search engine to ask a question and received a detailed answer? Chances are, you have. But have you ever stopped to think about the process behind it?
When you ask Google a question like, “When was STX Next founded?” it first searches through 425,000 pages to find the relevant context, and then it extracts the correct answer: March 29, 2005.
This process is called question answering.
It usually consists of two steps: first, the context of the question must be retrieved from the given list of documents, and then the exact answer must be extracted. Here, you only perform the second step: based on the context and the question, the model will extract the correct answer.
Then, you provide the question and context for the model to process:
The output will show you the correct answer, as well as the start and end points in the context where the answer can be found:
As we’ve shown, with Hugging Face, you can easily perform advanced tasks like question answering with just a few lines of code. The library can help you extract valuable information from the text and make more data-driven decisions in the future.
Machine translation is a task that involves generating text by translating text from one language to another. While it may be more complex and require more resources compared to tasks such as text classification or question answering, the Hugging Face library offers an easy-to-use interface for it that makes the task much simpler.
For example, the following code initializes a machine translation model and translates a sentence from English to French:
The output of this code would be:
Again, in just a few lines of code, you were able to translate the English sentence, “How old are you?” to French. So you can see that the Hugging Face library makes it effortless to leverage the power of machine translation and break through language barriers, as well.
Text summarization is a powerful NLP task that allows us to condense long pieces of text into shorter, more concise versions. It can be challenging, as it requires determining which information from the original text is most important and summarizing it effectively, but Hugging Face will help us perform text summarization, too. Here’s an example of how to use it:
The output of this code would be:
This is an example of extractive summarization, where the model selects relevant passages from the original text and combines them to create the summary.
There’s also abstractive summarization, where the model generates new text based on the original text, however, that is beyond the scope of this article.
The last pipeline we’ll discuss is text generation. The main idea here is that you feed an unfinished piece of text into a text generation model and it will automatically complete the thought and produce valuable content based on the available context.
For example, let’s say you want to write a blog post about developing mobile apps. You can pass the following text to a text generation model: “We’re developing mobile apps. In this blog post, we will show you how to…” The model will then generate text to complete the thought, such as, “…create great mobile apps in Python and Flask.”
But text generation isn’t just limited to autocomplete functionality. It can also be used as a standalone module to produce entire pieces of content. Just imagine the possibilities! With the right input and context, a text generation model could help you create a marketing campaign, write a compelling story, or even draft a blog post like this one.
To try it out, run the following code:
And here’s an example of how the output would look like:
You fed the model some text about developing a mobile app and writing a blog post. The output it generated was quite remarkable—it even mentioned specific programming languages like Python and Flask and gave your project a name: “MASS_BALANCE.”
While the text generation function may not always produce perfect results, it’s still a valuable tool that can greatly enhance authoring activities and help you produce high-quality content quickly and efficiently. So why not give it a try and see what kind of amazing content it can generate for you?
Hugging Face is an incredible resource for anyone working in natural language processing. In this section, we’ll explore the various benefits it offers to developers, business owners, and AI experts.
From its versatility and ease of use to its constantly evolving feature set, the Hugging Face library has something to offer for everyone working with NLP. So let’s see how Hugging Face can help you achieve your goals!
As we’ve presented in this article, Hugging Face is a valuable tool that provides a wide range of interfaces and features for solving modern NLP tasks.
While the library can often be treated as a black box, if you wish to optimize the performance of your tasks, you may want to delve deeper into the tool to understand its inner workings. By fine-tuning the model to fit the specific variant of the problem and data, you can further improve the results of your NLP projects.
One of the main benefits of the Hugging Face library for developers and AI experts is its versatility. It can be used for countless types of NLP tasks, from language translation to text classification and beyond. This means that you can use Hugging Face for all of your natural language processing projects, rather than switching between different tools and libraries.
In addition, the Hugging Face library is regularly updated with new features and improvements, so you can be confident that you’re always working with the most advanced and reliable NLP technology. Whether you are a seasoned NLP pro or just starting out, it’s a valuable resource worth considering for all of your NLP needs.
As a business owner, you’re always looking for new ways to streamline your processes and build innovative products that stand out on the market. Artificial intelligence and natural language processing can be valuable in achieving these goals. By using AI and NLP, you can automate tasks, gain insights from data, and create personalized experiences for your customers.
Whether you want to optimize your internal processes or build a new product that uses AI and NLP, the Hugging Face library can provide you with the tools and resources you need to succeed.
By using Hugging Face, you can not only save time and resources, but also gain a competitive edge in your industry. So don’t miss out on the opportunities that artificial intelligence and natural language processing can bring to your business, and consider incorporating the Hugging Face library into your workflow today.
Thank you for reading our article on implementing NLP in your business and elevating your NLP strategy with Hugging Face! We hope that it has helped you understand the importance of natural language processing in the area of machine learning and the various ways it is used in everyday technologies.
The Hugging Face library greatly simplifies the implementation of NLP in your business. To that end, we encourage you to take a look at our tutorial on text classification with this tool.
And that’s just the tip of the iceberg! With Hugging Face, you gain access to state-of-the-art models, cutting-edge techniques, and a vibrant community of developers and researchers who are constantly pushing the boundaries of NLP.
Furthermore, if you want to dive deeper into the world of natural language processing, machine learning, and data engineering, we have plenty of other resources for you to explore:
Aside from ML, AI, and DE, we have a wide range of software development services we can assist you with. Don’t hesitate to reach out and let us know how we can help you bring your business to the next level—we’ll be glad to support you!