So how does it work? Well, first we feed the model some input text (let’s say “I spilled coffee on my laptop because I was distracted by a phone call”). The model then uses its fancy algorithms to analyze this text and figure out what caused the event described in the sentence.

In more technical terms, FlaxBart is based on the BART framework (which stands for bidirectional encoder representations from transformers), but with some modifications specifically designed for causality analysis. These modifications include adding a special “cause” token to the input text and training the model to predict which words in the sentence are most likely to be causes of other events described later on.

Here’s an example output:

Input Text: I spilled coffee on my laptop because I was distracted by a phone call. Output: The cause of “spilling coffee” is “being distracted”. Pretty cool, right? But what if we want to analyze more complex causal relationships in text data? For instance, let’s say we have this sentence:

Input Text: John ate pizza for lunch because he was hungry and didn’t feel like cooking. Output: The cause of “eating pizza” is “being hungry”. But what about the second part of the sentence? Is there a causal relationship between not feeling like cooking and eating pizza? Well, according to FlaxBart, yes! Here’s how it works:

First, we feed the model both input sentences (the one with just “eating pizza” and the longer version). Then, we ask the model to predict which words in each sentence are most likely to be causes of other events described later on. In this case, FlaxBart would identify “being hungry” as a cause of “eating pizza”, but it would also recognize that not feeling like cooking is another potential cause (although less strong than being hungry). With FlaxBart for causal language modeling, we can analyze complex text data and figure out which events are most likely to be causes or effects. Pretty cool stuff!