The Text Summarizer project is focused on building and training a machine learning model to automatically generate concise summaries of text. The key components of the project include:
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Model Selection: The project uses a pre-trained sequence-to-sequence model, such as PEGASUS, which is fine-tuned for text summarization tasks. The model is adapted to work with specific datasets, such as the Samsum dataset, which is commonly used for summarizing dialogues.
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Dataset Handling: The project involves loading datasets (potentially from disk) to be used for training the model. These datasets include text and corresponding summaries, enabling the model to learn how to generate summaries from input text.
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Training Pipeline: The system leverages the Hugging Face
Trainerclass, which simplifies the process of training models. The training configuration (such as learning rate, batch size, warmup steps, etc.) is customizable through a configuration class,ModelTrainerConfig. -
Evaluation and Optimization: The project supports the evaluation of the model at regular intervals during training, optimizing the model's performance based on specific evaluation metrics.
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Saving the Model: After training, the fine-tuned model and tokenizer are saved for future use, enabling the model to generate summaries on new, unseen text.