NLP with LLMs: Hugging Face Classification, QnA, & Text Generation Pipelines

Large Language Models (LLMs)    |    Intermediate
  • 13 videos | 1h 50m 9s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 3 users Rating 4.3 of 3 users (3)
Sentiment analysis, named entity recognition (NER), question answering, and text generation are pivotal tasks in the realm of Natural Language Processing (NLP) that enable machines to interpret and understand human language in a nuanced manner. In this course, you will be introduced to the concept of Hugging Face pipelines, a streamlined approach to applying pre-trained models to a variety of NLP tasks. Through hands-on exploration, you will learn how to classify text using zero-shot classification techniques, perform sentiment analysis with DistilBERT, and apply models to specialized tasks, utilizing the power of NLP to adapt to niche domains. Next, you will discover how to employ models to accurately answer questions based on provided contexts and understand the mechanics behind model-based answers, including their limitations and capabilities. Finally, you will discover various text generation strategies such as greedy search and beam search, learning how to balance predictability with creativity in generated text. You will also explore text generation through sampling techniques and the application of mask filling with BERT models.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline the use of hugging face pipelines
    Perform zero-shot classification
    Perform sentiment analysis
    Perform sentiment analysis with finbert
    Perform named entity recognition (ner) with a bert model
    Perform ner manually
  • Use a qna pipeline
    Get predictions from a qna pipeline
    Explore greedy search and beam search for text generation
    Perform text generation with sampling
    Perform mask filling using a bert model
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 41s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 7m 29s
    Upon completion of this video, you will be able to outline the use of Hugging Face pipelines. FREE ACCESS
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    3.  Performing Zero-shot Classification
    11m 13s
    In this video, you will learn how to perform zero-shot classification. FREE ACCESS
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    4.  Performing Sentiment Analysis Using DistilBERT
    10m 44s
    During this video, you will learn how to perform sentiment analysis. FREE ACCESS
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    5.  Detecting Emotion and Sentiment Analysis on Financial Data
    7m 59s
    In this video, find out how to perform sentiment analysis with FinBERT. FREE ACCESS
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    6.  Performing Named Entity Recognition (NER) with a Fine-tuned BERT Model
    8m 27s
    Learn how to perform named entity recognition (NER) with a BERT model. FREE ACCESS
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    7.  Performing Named Entity Recognition Using Tokenizer and Model
    7m 21s
    Discover how to perform NER manually. FREE ACCESS
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    8.  Performing Question Answering Using Pipelines
    6m 5s
    Find out how to use a QnA pipeline. FREE ACCESS
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    9.  Performing Question Answering Using Tokenizer and Model
    12m 22s
    During this video, discover how to get predictions from a QnA pipeline. FREE ACCESS
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    10.  Performing Greedy Search and Beam Search for Text Generation Using GPT-2
    10m 58s
    In this video, we will explore greedy search and beam search for text generation. FREE ACCESS
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    11.  Generating Text Using Sampling
    9m 31s
    Find out how to perform text generation with sampling. FREE ACCESS
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    12.  Performing Mask Filling Using Variations of the BERT Model
    12m 7s
    Learn how to perform mask filling using a BERT model. FREE ACCESS
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    13.  Course Summary
    3m 11s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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