M-Taghizadeh/flan-t5-base-imdb-text-classification
In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.
This project helps you automatically sort written feedback, like customer reviews or survey responses, into categories of 'positive' or 'negative.' You provide the raw text, and it tells you if the sentiment is good or bad. This is useful for anyone who needs to quickly understand the general feeling expressed in a large collection of text, such as a customer service analyst or a product manager.
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Use this if you need a fast and accurate way to classify large volumes of text as either positive or negative.
Not ideal if you need to identify nuanced sentiments (like sarcasm or mixed feelings) or categorize text into more than just two predefined classes.
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May 12, 2023
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