NLP with LLMs: Language Translation, Summarization, & Semantic Similarity

Large Language Models (LLMs)    |    Intermediate
  • 10 videos | 1h 29m 29s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Language translation, text summarization, and semantic textual similarity are advanced problems within the field of Natural Language Processing (NLP) that are increasingly solvable due to advances in the use of large language models (LLMs) and pre-trained models. In this course, you will learn to translate text between languages with state-of-the-art pre-trained models such as T5, M2M 100, and Opus. You will also gain insights into evaluating translation accuracy with BLEU scores and explore multilingual translation techniques. Next, you will explore the process of summarizing text, utilizing the powerful BART and T5 models for abstractive summarization. You will see how these models extract and generate key information from large texts and learn to evaluate the quality of summaries using ROUGE scores. Finally, you will master the computation of semantic textual similarity using sentence transformers and apply clustering techniques to group texts based on their semantic content. You will also learn to compute embeddings and measure similarity directly.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Perform language translation with the t5 model
    Perform language translation with the m2m 100 and opus models
    Summarize text using bart and t5
    Preprocess text for summarization
  • Evaluate text summaries using rouge scores
    Compute text similarity with sentence transformers
    Perform clustering on sentence embeddings
    Compute text similarity with a tokenizer and model
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 11s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 11m 1s
    During this video, you will learn how to perform language translation with the T5 model. FREE ACCESS
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    3.  Performing Language Translation Using the M2M 100 and Opus Models
    11m 33s
    In this video, you will learn how to perform language translation with the M2M 100 and Opus models. FREE ACCESS
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    4.  Summarizing Text Using a BART Model and a T5 Model
    7m 36s
    Find out how to summarize text using BART and T5. FREE ACCESS
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    5.  Loading Data and Cleaning Text for Summarization
    6m 31s
    In this video, discover how to preprocess text for summarization. FREE ACCESS
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    6.  Evaluating Summaries Using ROUGE Scores
    12m 39s
    In this video, find out how to evaluate text summaries using ROUGE scores. FREE ACCESS
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    7.  Computing Semantic Textual Similarity Using Sentence Transformers
    12m 46s
    Discover how to compute text similarity with sentence transformers. FREE ACCESS
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    8.  Performing Clustering Using Sentence Embeddings
    10m 56s
    Learn how to perform clustering on sentence embeddings. FREE ACCESS
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    9.  Computing Embeddings and Similarity Using the Tokenier and Model
    11m 23s
    In this video, find out how to compute text similarity with a tokenizer and model. FREE ACCESS
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    10.  Course Summary
    2m 53s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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