NLP with LLMs: Fine-tuning Models for Language Translation, & Summarization

Large Language Models (LLMs)    |    Expert
  • 12 videos | 1h 38m 15s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Causal language modeling (CLM), text translation, and summarization demonstrate the versatility and depth of language understanding and generation by artificial intelligence (AI). Fine-tuning models help improve the performance of models for these specific tasks. ​In this course, you will explore CLM with DistilGPT-2 and masked language modeling (MLM) with DistilRoBERTa, learning how to prepare, process, and fine-tune models for generating and predicting text. Next, you will dive into the nuances of language translation, focusing on translating English to Spanish. You will prepare and evaluate training data and learn to use BLEU scores for assessing translation quality. You will fine-tune a pre-trained T5-small model, enhancing its accuracy and broadening its linguistic capabilities. Finally, you will explore the intricacies of text summarization. Starting with data loading and visualization, you will establish a benchmark using the pre-trained T5-small model. You will then fine-tune this model for summarization tasks, learning to condense extensive texts into succinct summaries.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Load and process data for causal language modeling (clm)
    Fine-tune a distilgpt model for clm
    Fine-tune a distilroberta model for masked language modeling (mlm)
    Preprocess text for translation
    Generate bleu scores for text translation
  • Fine-tune the t5-small model for translation
    Load and visualize data for summarization
    Summarize text with a regular t5-small model
    Fine-tune a t5-small model for summarization
    Compare the performance of fine-tuned t5 and baseline t5 models
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 50s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 11m 21s
    In this video, you will learn how to load and process data for causal language modeling (CLM). FREE ACCESS
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    3.  Fine-tuning a DistilGPT-2 Model for Causal Language Modeling
    7m 50s
    During this video, discover how to fine-tune a DistilGPT model for CLM. FREE ACCESS
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    4.  Fine-tuning a DistilRoBERTa Model for Masked Language Modeling (MLM)
    10m 40s
    Find out how to fine-tune a DistilRoBERTa model for masked language modeling (MLM). FREE ACCESS
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    5.  Preparing the Translation Data for Fine-tuning
    6m 30s
    In this video, find out how to preprocess text for translation. FREE ACCESS
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    6.  Preprocessing Text and Computing Metrics for Translation
    10m 37s
    During this video, you will learn how to generate BLEU scores for text translation. FREE ACCESS
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    7.  Fine-tuning the T5-small Model for English to Spanish Translation
    8m 52s
    Discover how to fine-tune the T5-small model for translation. FREE ACCESS
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    8.  Loading and Visualizing Summarization Data
    10m 3s
    Learn how to load and visualize data for summarization. FREE ACCESS
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    9.  Evaluating the Baseline Performance of the Pretrained T5-small Model
    12m 8s
    In this video, discover how to summarize text with a regular T5-small model. FREE ACCESS
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    10.  Fine-tuning the T5-small Model for Summarization
    6m 44s
    Find out how to fine-tune a T5-small model for summarization. FREE ACCESS
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    11.  Comparing the Fine-tuned Model's Performance with the Baseline Model
    8m 44s
    In this video, you will learn how to compare the performance of fine-tuned T5 and baseline T5 models. FREE ACCESS
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    12.  Course Summary
    2m 57s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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