From the course: AI Text Summarization with Hugging Face
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Understanding tokenizers - Hugging Face Tutorial
From the course: AI Text Summarization with Hugging Face
Understanding tokenizers
Previously we generated summaries using the T5 model with zero-shot learning, that is, we didn't fine-tune the model before using it to generate summaries. We just used the pre-trained model directly. We'll now continue working with the T5 model, except that, we'll fine-tune the model on this CNN dataset that we have. Fine-tuning is an approach to transfer learning where we start with the weights of the pre-trained model and then train it on the dataset that we have. We'll see if the fine-tuned model produces better summaries than just using the pre-trained model. In order to be able to fine-tune this model, we need to understand in a little more detail the steps involved when we run a Hugging Face pipeline. The steps include first preparing and processing the inputs that you parse in, then running the model on those inputs to get predictions and then processing these outputs, these predictions so that they return to you in a form that you can understand. When we fine-tune our model…
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