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FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here. Some of the benchmark datasets (thermostability) contain a highly diverse set of sequences from many different protein families. Others (AAV, GB1) contain all sequences that are mutants of a single parent sequence. Each benchmark dataset features multiple "splits" -- different ways of train-test splitting the data to assess how well a model might generalize given limited information. The AAV benchmark, for example, features the "mutant vs designed" split in which a model is trained on randomly generated mutants and asked to predict the fitness of designed sequences, and the "seven vs many" split in which a model is trained on sequences with seven mutations and asked to make predictions for sequences with a different number of mutations.

FLIP includes several benchmark datasets that contain a variety of protein sequences, each with a real-valued label indicating its "fitness" (how well the protein performs some particular function). The goal is to predict the fitness of a given protein sequence using the sequence. Different representations of protein sequences (e.g. learned embeddings from large language models) may prove helpful here. This sub-dataset (AAV) is a set of 201,426 training sequences and 82,583 test sequences in which the goal is to predict the fitness of mutants of the capsid protein from the adeno-associated virus (AAV). The training set proteins were designed, while the test set proteins are random mutants. The absolute value of the fitness is not important, but its ranking / relative value is -- protein designers would like to be able to pick a sequence with high fitness relative to those in the training set. Performance is therefore usually assessed using Spearman's r correlation coefficient.

The Flipkart Products Review Dataset is a collection of reviews provided by customers who purchased products from Flipkart, one of India’s leading e-commerce platforms. This dataset is valuable for sentiment analysis, which helps determine the emotional tone of customer reviews. Sentiment analysis is commonly used in e-commerce and other platforms to understand customer opinions and experiences with products.