[Tostino]

I am looking for a model similar to this, but for text. I want to group text with different labels that apply to subsets of the text. Think of being able to quickly pull-out related segments from a large body of text. Let's take, for instance, a sales contract that specifies a discounted price for various goods. If you select the label "data rows", the system should be able to extract all the text pertaining to the table that specifies which SKUs are being purchased, and at what discounted price. Moreover, this model should be capable of segmenting the content into semantically relevant chunks. One example: each row in the aforementioned table would be tagged with multiple labels. One would be just that it is a row, the data in the first column should be labeled for what it represents, e.g. "product number". Another example: if there's a section discussing the terms of delivery or warranty conditions, selecting the respective labels would instantly extract that specific information, regardless of where it's located within the document. Would be great for it to be able to segment into some controllable range of tokens/characters to allow for pulling those chunks into a vector database, along with the relevant tags related to the chunk.