51–60 of 63 posts
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GPT-4 presumably.
I keep bumping in to the context window size. I'm trying to figure out a "compression" step that I can use in the general case, but nothing's very satisfying so far. The mozilla/readability library is a good first step though.
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Yeah I get that, after all attension is all you need. But unless you want to spend a bunch of money on the 32k context version I don't think there are other options than embeddings and index.
What is the state of the art for running models locally in terms of context size?
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Could you explain how you use GPT for ocr correction?
https://promptbase.com/prompt/ocr-text-fixer This is the prompt I bought from promptbase. You basically provide GPT with some examples on possible OCR errors, and then you give it the OCRed text and it tries to correct it
I've worked extensively in this space. For those looking for just an OCR solution MSFT's offering "read" is by and far the most accurate. Key-value, table and other information extraction is a much harder problem. Anything that can go wrong in production will. Documents with extra pages, rotated, blacked out, fuzzy. There are many steps that go into making document extraction really e2e. The biggest enterprise users…
Do they have human workers for those hard to solve cases in the loop?
I've worked extensively in this space. For those looking for just an OCR solution MSFT's offering "read" is by and far the most accurate. Key-value, table and other information extraction is a much harder problem. Anything that can go wrong in production will. Documents with extra pages, rotated, blacked out, fuzzy. There are many steps that go into making document extraction really e2e. The biggest enterprise users…
I would like to extract text from approximately 2000 PDF files (machine generated, not scanned) in which the layout can be different on a file basis. Some have normal paragraphs, others two columns and even three columns. All contain tables, but I am not interested in them. Do you know a good (semi-)automatic solution for this?
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I would like to extract text from approximately 2000 PDF files (machine generated, not scanned) in which the layout can be different on a file basis. Some have normal paragraphs, others two columns and even three columns. All contain tables, but I am not interested in them. Do you know a good (semi-)automatic solution for this?
this is a hard problem and will require an enterprise solution unfortunately. If its only 2000 pdfs you might be better outsourcing to an off-shore consulting agency to do it manually
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I keep bumping in to the context window size. I'm trying to figure out a "compression" step that I can use in the general case, but nothing's very satisfying so far. The mozilla/readability library is a good first step though.
Yeah I have the same issue with context window size. Personally I'm just waiting for future LLM's with 10x-100x context window. However, someone else recently came up with this solution: https://news.ycombinator.com/item?id=35488291
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I keep bumping in to the context window size. I'm trying to figure out a "compression" step that I can use in the general case, but nothing's very satisfying so far. The mozilla/readability library is a good first step though.
Why do you want to compress this data? What's the final use case here?
Yes, I could try to figure out which bits of the page need to be sent along with the prompt, but that's hard in the general case. Squeezing a bit more out of the prompt window by stripping out unnecessary boilerplate is easy by comparison. (Multi-page articles are another headache).
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Why do you want to compress this data? What's the final use case here?
Imagine a browser plugin that pops up a modal. I write a query related to the current page, eg "Please summarize this page in three paragraphs, and translate to Turkish" or "There's a recipe somewhere on this page. Please suggest some variations on the filling" or "Can you make me a list of all the people mentioned on this page". Whole page (or at least the meat of it) gets bundled up with the query and sent to opena…