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Hugging Face is a company devoted to the development of NLP technologies and democratization of artificial intelligence through natural language technologies. Their teams have changed the way we approach NLP by providing easy-to-understand language model architectures.
The Hugging Face Transformers pipeline is an easy way to perform different NLP tasks. It can be used to solve a variety of NLP projects with state-of-the-art strategies and technologies.
Today, I want to introduce you to the Hugging Face pipeline by showing you the top 5 tasks you can achieve with their tools.
Today, we will go over:
This course teaches the top techniques for processing text data, creating word embeddings, and using LSTM networks for NLP tasks.
Sentiment analysis refers to classifying a given text with POSITIVE or NEGATIVE labels based on their sentiment with a given probability score.
Here we will be giving two sentences and extracting their labels with a score based on probability rounded to 4 digits.
nlp = pipeline("sentiment-analysis")#First Sentenceresult = nlp("I love trekking and yoga.")[0]print(f"label: {result['label']}, with score: {round(result['score'], 4)}")#Second sentenceresult = nlp("Racial discrimination should be outright boycotted.")[0]print(f"label: {result['label']}, with score: {round(result['score'], 4)}")
The output for the first sentence is:
label: POSITIVE, with score: 0.9992
The output for the second sentence is:
label: NEGATIVE, with score: 0.9991
Question Answering refers to an answer to a question based on the information given to the model in the form of a paragraph. That information provided is known as its context. The answer is a small portion from the same context.
Below, a paragraph about Prime Numbers is given as a context, and 2 questions are asked based on the context. This context paragraph is taken from the SQuAD database.
nlp = pipeline("question-answering")context = r"""The property of being prime (or not) is called primality.A simple but slow method of verifying the primality of a given number n is known as trial division.It consists of testing whether n is a multiple of any integer between 2 and itself.Algorithms much more efficient than trial division have been devised to test the primality of large numbers.These include the Miller–Rabin primality test, which is fast but has a small probability of error, and the AKS primality test, which always produces the correct answer in polynomial time but is too slow to be practical.Particularly fast methods are available for numbers of special forms, such as Mersenne numbers.As of January 2016, the largest known prime number has 22,338,618 decimal digits."""#Question 1result = nlp(question="What is a simple method to verify primality?", context=context)print(f"Answer: '{result['answer']}'")#Question 2result = nlp(question="As of January 2016 how many digits does the largest known prime consist of?", context=context)print(f"Answer: '{result['answer']}'")
The answer to the first question is:
Answer: 'trial division'
The answer to the second question is:
Answer: '22,3338,618'
Text generation is one of the most popular NLP tasks. GPT-3 is a type of text generation model that generates text based on an input prompt.
Below, we will generate text based on the prompt A person must always work hard and. The model will then produce a short paragraph response. As you’ll see, the output is not very coherent because the model has fewer parameters.
text_generator = pipeline("text-generation")text= text_generator("A person must always work hard and", max_length=50, do_sample=False)[0]print(text['generated_text'])
The output for the above code is:
A person must always work hard and be prepared to do so.
The following are some of the things that you should do to help yoursefl:
1. Be prepared to work hard.
2. Be prepared to work hard.
Text summarization is the process of comprehending a large chunk of textual data and then responding with a brief summary of that data. Below we are getting a summary for a paragraph on the Apollo Mission.
summarizer = pipeline("summarization")ARTICLE = """The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972.First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space,Apollo was later dedicated to President John F. Kennedy's national goal of "landing a man on the Moon and returning him safely to the Earth" by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress.Project Mercury was followed by the two-man Project Gemini (1962–66).The first manned flight of Apollo was in 1968.Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966.Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions.Apollo used Saturn family rockets as launch vehicles.Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973–74, and the Apollo–Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975."""summary=summarizer(ARTICLE, max_length=130, min_length=30, do_sample=False)[0]print(summary['summary_text'])
The summary generated for the above paragraph is:
The Apollo program, also known as Project Apollo, was the third U.S. human spaceflight program carried out by the National Aeronautics and Space Administration (NASA) The first manned flight of Apollo was in 1968. The program was dedicated to President Kennedy's national goal of "landing a man on the Moon and returning him safely to the Earth"
Translation is the process of translating one language to another. NLP is used to generate automatic translations between languages. Below, we will translate a proverbial sentence from English to German.
translator = pipeline("translation_en_to_de")print(translator("A great obstacle to happiness is to expect too much happiness.", max_length=40)[0]['translation_text'])
The translated sentence is:
Ein großes Hindernis für das Glück besteht darin, zu viel Glück zu erwarten.
While sentiment analysis, question answering, text generation, summarization, and translation are still foundational tasks, the field of NLP has grown far beyond them. Here are several powerful and widely used tasks you can explore today:
Zero-shot classification: Classify text into categories without labeled training data by leveraging natural-language prompts.
Named Entity Recognition (NER): Identify and label entities like names, locations, or organizations in text.
Masked language modeling: Predict missing words or phrases — a common building block for pretraining and downstream tasks.
Text similarity and semantic search: Compare meanings across sentences, paragraphs, or documents.
Retrieval-augmented generation (RAG): Combine language models with external knowledge bases for fact-grounded generation.
Conversational systems: Build multi-turn dialogue agents that understand context and maintain state.
Multilingual NLP: Apply all of the above across dozens of languages with pre-trained multilingual transformers.
These tasks represent where real-world NLP development is heading, and most can still be tackled using Hugging Face’s pipeline API — often with just a few extra lines of code.
Every Hugging Face pipeline is powered by a specific model architecture — and knowing what’s happening under the hood helps you make smarter choices. Here are a few you’ll encounter frequently:
BERT, RoBERTa, DistilBERT: Great for classification, NER, and sentence-level tasks.
T5 and BART: Excellent for generation tasks like summarization and translation.
GPT-2, GPT-Neo, Falcon: Strong for creative or open-ended text generation.
Longformer and BigBird: Designed for handling long documents without truncation.
mBERT and XLM-R: Best for multilingual applications.
Choosing the right model means balancing accuracy, speed, and memory usage. For example, DistilBERT is faster and lighter but slightly less accurate, while larger models like GPT-Neo produce more fluent text at the cost of longer inference times.
Most production-grade NLP systems don’t stop at a single pipeline call. Instead, they combine multiple models, retrieval systems, and external tools to achieve better results. Here are some modern patterns you should know:
Retrieval-augmented generation (RAG): Fetch relevant documents from a database or vector store before generating a response.
Chained pipelines: Pass outputs from one model (e.g., classification) into another (e.g., summarization).
Prompt chaining: Guide models step-by-step through multi-stage reasoning tasks.
Tool use: Integrate external APIs for search, data lookup, or computations during inference.
These approaches transform NLP applications from simple demos into robust, production-ready systems.
Once you’re comfortable with Hugging Face pipelines, the next step is building custom solutions. Here’s a roadmap to keep learning:
Fine-tune pretrained models on your own data.
Build end-to-end applications with retrieval, orchestration, and evaluation.
Explore libraries like transformers, datasets, and evaluate for deeper control.
Join the Hugging Face community and experiment with cutting-edge research models.
This journey takes you from basic demos to state-of-the-art NLP systems capable of powering real-world applications.
NLP is a powerful tool, and there is so much to learn. If you are interested in exploring NLP on your own or designing projects using Hugging Face, consider starting with the following concepts:
Check out Educative’s course Natural Language Processing with Machine Learning to get started with these topics and beyond. You’ll learn the techniques for processing text data, creating word embeddings, and using LSTM networks for NLP tasks. After completing this course, you will be able to solve the important day-to-day NLP problems on your own.
Happy learning!
