Пікірлер

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  @user-ls4tp7sy4d8 ай бұрын

  Use a pre-completion document, that stores the cached response of the model to write to as a sort of reverse RAG (gives a space for multi-hop to happen, and stores context for cross document RAGs)

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  @FunCodingwithRahul Жыл бұрын

  Can I integrate any LLM as generator model e.g. Falcon? I am getting errors while doing it. any lead would be of great help !!

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  @cosmicray007 Жыл бұрын

  Question, why not just use Apache Hive as a Catalog meta database, like the one found in AWS Glue. No need to move the data, since the catalog mdb points to the data. Moving data is costly, just saying. 18:44 sounds like we are in a pickle :)

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  @johnryan8645 Жыл бұрын

  Clifford algebras do high dimensional cross products.

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  @alexiagordon6389 Жыл бұрын

  Wow i love this! You need Promo-SM!!!

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  @goelnikhils Жыл бұрын

  Amazing Talk

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  @anupamaappasahebdeshmukh4395 Жыл бұрын

  Thank you sir, do you also have some gihub samples to understand this concept? Can you please share?

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  @eugeneware3296 Жыл бұрын

  This is so brilliant. It's so hard to find these real-life learnings of how to deliver world-class ML systems in production. Especially the interaction between spark and transformers.

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  @jasonmackay891 Жыл бұрын

  100%

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  @VaclavKosar Жыл бұрын

  Great meetup, thx. Loved the part about Spark cpu transformer embedding calculation for semantic indexing.

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  @mprone Жыл бұрын

  Any chance to get the first github repository ? after more than 2 years it is still private I guess

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  @dibdias12 жыл бұрын

  Very good to have this video!

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  @hritikakolkar2 жыл бұрын

  Informative

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  @ppujari2 жыл бұрын

  segmentation part is not clear. Lookslike speaker is not intended to tell.

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  @justingidman16842 жыл бұрын

  ᴘʀᴏᴍᴏsᴍ

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  @KwanyuetHo2 жыл бұрын

  I think I still have the questions about merging cross products. From what I understand cross products between two vectors are only defined for three-dimensional vectors. Is there an extension to n dimensions? Is it something from differential geometry? Or are you talking about outer products?

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  @vikankshnath80682 жыл бұрын

  What about multi span setting? When ans is spanned at different places.

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  @davidnkanta96652 жыл бұрын

  Can i deploy my model as a Conversational Agent(like gpt3 or bert for question answering) on a mobile application. Further more, can i get graphical plot of the training and accuracy of my trained model??

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  @davidnkanta96652 жыл бұрын

  For 6 montha i have been struggling with my dissertation project. This is pure magic

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  @doyourealise2 жыл бұрын

  i could not attend the live session so i am here. Thanks for the video.

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  @doyourealise2 жыл бұрын

  thaanks for the video

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  @giannagiavelli50982 жыл бұрын

  Transformer is rubbish, years behind Noonean

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  @ajitkumar152 жыл бұрын

  Thank you for such video which give us glance in the advancement of Language processing

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  @_HarshVerma2 жыл бұрын

  Now this guy knows what he is talking about otherwise every other person talkin rag on KZread is just saying gibrish

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  @bhaumikpatel4902 Жыл бұрын

  Probably because he was a co-author 😅

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  @ZKYQUQ6 ай бұрын

  You're right bro. Love u.@@bhaumikpatel4902

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  @in_experience63833 жыл бұрын

  Thanks man it was nice Intro. the Q&A beyond SQuAD.

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  @digidim3 жыл бұрын

  The repository does not exist. Could you please recheck the github link ?

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  @asitkumar31763 жыл бұрын

  why dont u post the code about which you are talking about..I mean with retrievers

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  @DistortedV123 жыл бұрын

  Awesome work

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  @jamesheffernan80003 жыл бұрын

  Excellent, Thank You Dave.

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  @connorshorten63113 жыл бұрын

  These videos are great! Thank you so much!

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  @gulanshunan3 жыл бұрын

  what if an answer is not in the given text

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  @paraschopra3 жыл бұрын

  You use abstractive answer generation technique.

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  @gulanshunan3 жыл бұрын

  what to do if an answer is not in the given text

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  @gulanshunan3 жыл бұрын

  will you share pdf version or repository of code

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  @TheMriganks3 жыл бұрын

  Good session, possible to get the slides?

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  @venkatagudala51724 жыл бұрын

  51:53 Retriever in the QA Pipeline uses modified TF-IDF which is Okapi-BM25 by adjusting the Term Frequency(TF) and Inverse Document Frequency (IDF) by adding parameters 'k1' and 'b' to the TF-IDF equation

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  @connectrRomania4 жыл бұрын

  amazing, is there a pdf or ppt version of the presentation?

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  @katejannuzzi23754 жыл бұрын

  Thank you for posting this to you tube, I was happy to see this after I missed the live one!

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  @gabrielaltay58074 жыл бұрын

  Hello viewers, There was a window sharing issue between 24:30 and 27:30. The kernel that was supposed to be on the screen was an introduction to the Wikipedia part of the dataset (www.kaggle.com/kenshoresearch/kdwd-wikipedia-introduction). Also we didn't have time to get to many of the kernels, but you can check these out ... * www.kaggle.com/kenshoresearch/kdwd-explicit-topic-models * www.kaggle.com/kenshoresearch/kdwd-pmi-word-vectors * www.kaggle.com/kenshoresearch/kdwd-aliases-and-disambiguation * www.kaggle.com/gabrielaltay/kdwd-subclass-path-ner and a blog post going into more detail on creating synonyms from Wikipedia blog.kensho.com/how-to-build-a-smart-synonyms-model-1d525971a4ee