I think that is a very liberal use of the word “equivalent”.
Without source code, you don’t know what operations a computer program is performing and you do not have it under a human-readable form in which the author has it. Source code and object code, also, are considered as two representations of the same asset, legally and conceptually.
When it comes to trained models, the dataset is not necessary to know what operations the model is performing (that is determined by the architecture and by the frameworks being used), except at a specific conceptual level which requires massive efforts to understand (with relatively poor results) and which data doesn’t reveal anyways. The dataset and the model are also not two different representations of the same thing and conversion in either direction can be far from trivial (while, with software, conversion can happen in at least one direction usually with little effort). While two different code-bases, developed separately, represent different programs, even if compiled, two different dataset sampled from essentially the same distribution will lead to substantially similar models, if the same architecture is used.