Large language models can perform various operations such as text summarization, sentiment analysis, language translation, and text-based recommendation systems.
With the introduction of the transformer architecture, natural language processing tasks are made easy. This architecture allows the model to process an entire sentence or paragraph at once, rather than each word at a time. The processing of text input with the transformer architecture is based on tokenization, which is the process of transforming texts into smaller components called tokens. These can be words, subwords, characters, or many others. With the power of transfer learning, we can enhance a specific task by training it in a specific dataset, a.k.a. fine-tuning
In this text summarization prototype, we are using the BART model, which is a Sequence-to-sequence model with an encoder and a decoder. The encoder is fed a corrupted version of the tokens, and the decoder is fed the original tokens. This model learned to generate concise summaries of long text passages. We evaluated the model's performance using metrics like ROUGE score, which measures how well the generated summaries match the original text. After training, we saved the model and tokenizer to disk for future use.
This technology has wide-ranging applications, from simplifying long articles to information retrieval and decision-making processes. By automating the summarization process, we can save time and resources while still capturing the essential information conveyed in the original text.
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