5 ms·

Wow this is exactly what I've been looking for, thank you! I just wish with these transformer models it was possible to extract a structured set of what the mod

by pugio 4y ago

Wow this is exactly what I've been looking for, thank you! I just wish with these transformer models it was possible to extract a structured set of what the model "knows" (for e.g. easy search indexing ). These natural language question systems are a little too fuzzy sometimes.

ankrgyl 4y ago
Can you tell me a bit more about your use case? A few things that come to mind: - There are some ML/transformer-based methods for extracting a known schema (e.g. NER) or an unknown schema (e.g. relation extraction). - We're going to add a feature to DocQuery called "templates" soon for some popular document types (e.g. invoices) + a document classifier which will automatically apply the template based on the doc type. - Our commercial product (http://impira.com/ http://impira.com/) supports all of this + is a hosted solution (many of our customers use us to automate accounts payable, process insurance documents, etc.)
- pugio 4y ago
  Your commercial product looks very cool, but my use case is in creating an offline-first local document storage system (data never reaches a cloud). I'd like to be enable users to search through all documents for relevant pieces of information. The templates sound very cool - are they essentially just using a preset list of (natural language) queries tied to a particular document class? It seems like you're using a version of donut for your document classification?
  - ankrgyl 4y ago
    > but my use case is in creating an offline-first local document storage system (data never reaches a cloud). Makes sense -- this is why we OSS'd DocQuery :) > The templates sound very cool - are they essentially just using a preset list of (natural language) queries tied to a particular document class? It seems like you're using a version of donut for your document classification? Yes that's the plan. We've done extensive testing with other approaches (e.g. NER) and realized that the benefits of using use-case specific queries (customizability, accuracy, flexibility for many use cases) outweigh the tradeoffs (NER only needs one execution for all fields). Currently, we support pre-trained Donut models for both querying and classification. You can play with it by adding the --classify flag to `docquery scan`. We're releasing some new stuff soon that should be faster and more accurate.
    - pugio 4y ago
      Sweet! I'll keep an eye on the repo. Thank you for open sourcing DocQuery. I agree with your reasoning: my current attempts to find an NER model that covers all my use cases have come up short
- mbb70 4y ago
  Since you mention insurance documents, could you speak to how well this would extract data from a policy document like https://ahca.myflorida.com/medicaid/Prescribed_Drug/drug_criteria_pdf/Spinraza_Criteria.pdf https://ahca.myflorida.com/medicaid/Prescribed_Drug/drug_cri... ? The unstoppable administrative engine that is the American Healthcare system produces hundreds of thousands of continuously updated documents like this with no standardized format/structure. Manually extracting/normalizing this data into a querable format is an industry all its own.
  - ankrgyl 4y ago
    It's very easy to try! Just plug that URL here: https://huggingface.co/spaces/impira/docquery https://huggingface.co/spaces/impira/docquery. I tried a few questions: What is the development date? -> June 20, 2017 What is the medicine? -> SPINRAZA® (nusinersen) How many doses -> 5 doses Did the patient meet the review criteria? -> Patient met initial review criteria. Is the patient treated with Evrysdi? -> not
a1369209993 4y ago
> to extract a structured set of what the model "knows" To be fair, that's impossible in the general case, since the model can know things (ie be able to answer queries) without knowing that it knows them (ie being able to produce a list of anserable queries by any means significantly more efficient than trying every query and seeing which ones work). As a reducto ad absurdum example, consider a 'model' consisting of a deniably encrypted key-value store, where it's outright cryptographically guaranteed that you can't effiently enumerate the queries. Neural networks aren't quite that bad, but (in the general-over-NNs case) they at least superficially appear to be pretty close. (They're definitely not reliably secure though; don't depend on that.)

by pugio 4y ago

ankrgyl 4y ago

Can you tell me a bit more about your use case? A few things that come to mind: - There are some ML/transformer-based methods for extracting a known schema (e.g. NER) or an unknown schema (e.g. relation extraction). - We're going to add a feature to DocQuery called "templates" soon for some popular document types (e.g. invoices) + a document classifier which will automatically apply the template based on the doc type. - Our commercial product (http://impira.com/ http://impira.com/) supports all of this + is a hosted solution (many of our customers use us to automate accounts payable, process insurance documents, etc.)

pugio 4y ago
Your commercial product looks very cool, but my use case is in creating an offline-first local document storage system (data never reaches a cloud). I'd like to be enable users to search through all documents for relevant pieces of information. The templates sound very cool - are they essentially just using a preset list of (natural language) queries tied to a particular document class? It seems like you're using a version of donut for your document classification?
- ankrgyl 4y ago
  > but my use case is in creating an offline-first local document storage system (data never reaches a cloud). Makes sense -- this is why we OSS'd DocQuery :) > The templates sound very cool - are they essentially just using a preset list of (natural language) queries tied to a particular document class? It seems like you're using a version of donut for your document classification? Yes that's the plan. We've done extensive testing with other approaches (e.g. NER) and realized that the benefits of using use-case specific queries (customizability, accuracy, flexibility for many use cases) outweigh the tradeoffs (NER only needs one execution for all fields). Currently, we support pre-trained Donut models for both querying and classification. You can play with it by adding the --classify flag to `docquery scan`. We're releasing some new stuff soon that should be faster and more accurate.
  - pugio 4y ago
    Sweet! I'll keep an eye on the repo. Thank you for open sourcing DocQuery. I agree with your reasoning: my current attempts to find an NER model that covers all my use cases have come up short
mbb70 4y ago
Since you mention insurance documents, could you speak to how well this would extract data from a policy document like https://ahca.myflorida.com/medicaid/Prescribed_Drug/drug_criteria_pdf/Spinraza_Criteria.pdf https://ahca.myflorida.com/medicaid/Prescribed_Drug/drug_cri... ? The unstoppable administrative engine that is the American Healthcare system produces hundreds of thousands of continuously updated documents like this with no standardized format/structure. Manually extracting/normalizing this data into a querable format is an industry all its own.
- ankrgyl 4y ago
  It's very easy to try! Just plug that URL here: https://huggingface.co/spaces/impira/docquery https://huggingface.co/spaces/impira/docquery. I tried a few questions: What is the development date? -> June 20, 2017 What is the medicine? -> SPINRAZA® (nusinersen) How many doses -> 5 doses Did the patient meet the review criteria? -> Patient met initial review criteria. Is the patient treated with Evrysdi? -> not

a1369209993 4y ago

> to extract a structured set of what the model "knows" To be fair, that's impossible in the general case, since the model can know things (ie be able to answer queries) without knowing that it knows them (ie being able to produce a list of anserable queries by any means significantly more efficient than trying every query and seeing which ones work). As a reducto ad absurdum example, consider a 'model' consisting of a deniably encrypted key-value store, where it's outright cryptographically guaranteed that you can't effiently enumerate the queries. Neural networks aren't quite that bad, but (in the general-over-NNs case) they at least superficially appear to be pretty close. (They're definitely not reliably secure though; don't depend on that.)