Summary of Training summarization & translation models with fastai & blurr

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00:00:00 - 01:00:00

This video provides an overview of how to use the fastai and blurr libraries to train machine learning models for summarization and translation tasks. The video covers how to choose a data set, select metrics, and train the models. The presenter also introduces a translation preprocessor in blurr that can truncate long translations down to a minimum length of 30 characters.

  • 00:00:00 In this video, Wade discusses how he can help people learn how to translate and summarize models. He shares a library that can be used for data augmentation, and discusses some other tricks he has learned.
  • 00:05:00 This video covers how to build models for summarization and translation, focusing on fastai and blurr. The presenter discusses how to choose a data set, select metrics, and train the models.
  • 00:10:00 This video provides a quick overview of how to train a summarization model using fastai and blurr. The video first discusses how to summarize English articles using the cnn daily mail data set. The video then moves on to demonstrate how to summarize German articles using the same data set. Finally, the video discusses some tips for text generation.
  • 00:15:00 In this video, the author discusses how to use fastai and blurr to train an encoder-only and decoder-only translation model. He also highlights models that have worked well for him in summarization and translation.
  • 00:20:00 In this video, the presenter demonstrates how to use fastai and blurr to train and evaluate a machine learning model. He focuses on the bart and blurr machine learning models, and explains how to use pre-processing to adjust the data set in order to improve the accuracy of the predictions. He also introduces a translation preprocessor in blur that can truncate long translations down to a minimum length of 30 characters.
  • 00:25:00 In this video, the presenter explains how to use the fastai and blurr libraries to train machine learning models. The main points covered include: how to limit the number of tokens in the input and in the targets, how to use the mid-level APIs in blur to handle sequence of sequence tasks, and how to use the hugging face loss function to calculate the loss and generate the decoder input ids automatically.
  • 00:30:00 To train a machine translation model, you need to set up your data and tokenizer. The training process will run using a specific model, and you will need to be aware of the different models that are available and how they work. In particular, you need to set up your input data to match the model that you are using.
  • 00:35:00 This training video covers the use of fastai and blurr for training and prediction of object recognition models. The video explains how to train a model using star tokens, shift the inputs to the right one, and replace the star token with the decoder start token id. Next, the video explains how to generate text to be used in a machine translation model. Finally, the video covers the use of defaults in the blurr configuration object to simplify the process of setting up a model.
  • 00:40:00 This YouTube video discusses training models for sequence to sequence models with fastai and blurr. The reference summaries or translation task and the sequence to sequence models are passed into the batch tokenized transform. The seek to seek text block is prepared for inputs and targets. The data block is familiar and defines a git x and get y which use the call reader methods. The learner and metrics are defined. Single batch is used to see the data loaders. The rouge metrics are explained, and blue is compared to Sakura Blue. The meteor and birch score are also discussed.
  • 00:45:00 Training summarization and translation models with fastai and blurr is covered in this video. One model, Rouge l, looks at individual sentences and calculates the average length of matching words. Rouge l sum computes the rougel over the entire summary, rather than sentence-by-sentence. The paper recommends using a different model, Rouge one, for English and a different model, Rouge two, for non-English translations. Blue and Soccer Blue are the same thing, just that Soccer Blue overcomes a key weakness of Blue.
  • 00:50:00 In this video, the author describes the different metrics that are available for machine translation, and explains how to use them in a training pipeline. He also explains how to use a blur seek to seek splitter to speed up the training process.
  • 00:55:00 In this video, the presenter explains how to train a text generation model using fastai and blurr. They discuss how to configure the model, how to generate predictions, and how to measure the accuracy of the predictions.

01:00:00 - 01:05:00

This video explains how to use the fastai and blurr deep learning models to generate text. The main points covered are: training a model using a configuration object, trimming inputs and targets, calling show results to see results, and inference with blur's blur generate and blur translate methods.

  • 01:00:00 This video explains how to use the fastai and blurr deep learning models to generate text. The main points covered are: - Training a fastai or blurr deep learning model using a configuration object - Trimming inputs and targets - Calling show results to see results - Inference with blur's blur generate and blur translate methods.
  • 01:05:00 In this video, Wade detail how to use fastai and blurr to train translation models. He explains that the new bits in blurr are the core changes in terms of the prediction bits, the refactoring in naming, the improvements to the preparation of the predicted and reference summaries, and the new pre-processing bits across the board. He also reminds everyone that after this video, they are required to create a blog post or project sharing their results.

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