Introducing KeyLLM - Keyword Extraction with Mistral 7B and KeyBERT

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Information Introducing KeyLLM - Keyword Extraction with Mistral 7B and KeyBERT

Title	:	Introducing KeyLLM - Keyword Extraction with Mistral 7B and KeyBERT
Lasting	:	18.52
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Thanks Maarten Can this approach extract keywords that are not mentioned in the document but are relevant to the document? Keywords not mentioned but very important keywords to help identify the document?
Comment from : @MyKlent

Thank you very much for sharing
Comment from : @jevy_yang

Thanks for the vids ! What if I have thousand of documents that are preprocessed web pages Will it be super slow ? What can I do to make it more performant ?
Comment from : @KeThienNguyen

Is it possible to extract document metadata the same way with Mistral?
Comment from : @dibu28

This is an amazing display of how how to make it easy to understand and execute these important new technologies Well done, thank you
Comment from : @dixon1e

Thank you, Maarten! It is an amazing content I'm having some problems while including RAG in KeyBert, so it would be nice a video about it
Comment from : @ronpao

Very nice! Next step key phrase?
Comment from : @araldjean-charles3924

Great content How values do you have by default for temperature and top_p ?
Comment from : @vesaalexandru6853

Hey Maarten, congrats by this amazin work! if i wanna use input in portuguese and output in portuguese, how I need to change? just prompt? What the format of input if i have docx files? Thanks
Comment from : @GeandersonLenz

what are the appropriate application scenarios for keybert/keyllm?
Comment from : @fanchuankang1228

Things are moving fast 6 months ago I was contemplating BERTopic or Neural Search with Elasticsearch using all-MiniLM-L6-v2 and ANN algorithm brNow we have Mistral, KeyBERT and RAG Just playing around a bit, my use case is topic extraction from relatively small documents
Comment from : @jeroenadamdevenijn4067

really good
Comment from : @streamocu2929

Hi Marteen, the diagrams makes the process stages quite clear Thanks for sharing! brbrBTW the link to the O'Reilly book is broken
Comment from : @LaHoraMaker

I think mistral llm is a gud choice 👍🏾
Comment from : @hemanth8195

Nice video, but I guess I miss what is the problem you want to solve When do you need to extract keywords? Say if you want to cluster documents, your approach would work, but what you suggest is the exact opposite?
Comment from : @PerFeldvoss

Hi Maarten, thanks for the informative video I was trying it out, can you suggest what i have to do as I am getting the "WARNING:ctransformers:Number of tokens (513) exceeded maximum context length (512)"
Comment from : @aranyapatra65

thank you for the video
Comment from : @LeoLogics

God what a Dutch accent, coming from a Dutchie 😂
Comment from : @programming1784

Thank you for the video Here what is the definition of a keyword? Is it picking all the words present (except the stop words)? Say I want to return words of one type (like location or food ingredients) how does one modify this example to get those words? Thank you
Comment from : @ramp2011