Video

Great explaination.

Gotzero sucks. It says things i typed myself are AI. Its a scam

You are really doing good job! this is going to help alot of ppl

thank you for such lucid explanation

To @everyone: You have to watch this video, it's pretty cool 😄 such a nice job explaining even the minor details. Well done!

sir can you suggest some of the best ranking rag algorithms like to build ai freelancer matcher in which I will rank the gigs based on the description, ratings, number of review. How can I approach this.

You are a true TechViz man...love the simplicity that you explain with.

Really appreciated the effort, please continue the paper deep dives!

Please share the pdf link of this paper

List of unsupervised sentence embedding methods: github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning

Well explained, Thanks !

Thanks. Can donut be used for text region detection such as caption, oage number, serial number and classifiying them?

%AI improve and develop our contextual strand adequacy interpretation 2024 to %AI improve and develop our contextual strand adequacy interpretation 2024

Awresome man! Keep doing this stuff. Subscribed!!

Great video, thanks for making it.

NDCG Metric: czcams.com/video/xHhLOQ7Pnb4/video.html Mean Reciprocal Rank Metric: czcams.com/video/6dDvfGrxFns/video.html

Your explanation of T2 becoming 0 with sigmoid is incorrect.

Thanks for sharing this, as always! Interesting approach, but based on their results, it does not seem to provide significant improvement (besides SimCSE and Contriever). One must keep in mind the cost associated with having additional chunks, especially in popular retrievers like DPR.

You should also mention that a value head is getting trained, which is placed on the LLM. You can refer TRL PPO example and check it's source code.

Thanks for sharing this. Awesome work ✌🏻

🌟Watch out more Research Paper Summaries at czcams.com/video/ykClwtoLER8/video.html

can you code this paper with sample tickets data

Awesome.... Thank you so much

Interesting.... Thank you for sharing....🤝

Thanks for the intuition ! :)

Can u share the source code

Amazing paper. Thanks for making the video.

Thanks a lot... Very much needed.

> Interested in consuming byte-sized AI/ML content. Then feel free to check www.youtube.com/@TechVizTheDataScienceGuy/shorts > If you're looking for more research paper summaries, then check czcams.com/play/PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc.html

Thanks for sharing the insights. I was exploring for similar usecase.

thanks for this. you helped me solve a problem

Thanks for the clear explanation! Is there any self query retrieval cookbook available for reference?

Amazing 🙌

⭐Interested in consuming byte-sized AI/ML content. Then feel free to check www.youtube.com/@TechVizTheDataScienceGuy/shorts ⭐If you're looking for research paper summaries, then check czcams.com/play/PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc.html

Which model is used in Re ranker? And how to get likelihood probability using that model. Any notebook for demo.

Crisp and clear! ❤

⭐ Interested in consuming byte-sized AI/ML content. Then feel free to check www.youtube.com/@TechVizTheDataScienceGuy/shorts ⭐Just like this one, if research paper summaries are your type, then check czcams.com/play/PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc.html

🌟Blog: prakhar-mishra.medium.com/enhancing-passage-retrieval-with-zero-shot-question-generation-paper-summary-301d34e0278b 🌟Interested in consuming byte-sized AI/ML content. Then feel free to check www.youtube.com/@TechVizTheDataScienceGuy/shorts 🌟Just like this one, if research paper summaries are your type, then check czcams.com/play/PLsAqq9lZFOtWUz1WEoJ3GXw197LD7BxMc.html

Nicely explained

This is not your typical BI or reporting engine where you are performing a dtilldown and drill through your indices. The index in your Vector db and LLM is good enough for your RAG

Was waiting for your explainer videos since last few months! Thanks for sharing 😊

Awesome man

Very clear explanation. Thank you so much!

Its very clear and crisp . Is it possible to add the practical implementation of RRF ??

Good approach but isn't it too expensive? Comparing each sentence with the next sentence in the whole document. It would work good for smaller dataset but will be slower otherwise.

Are you familiar with the reasons for conducting re-ranking step? Specifically, given the premise of extracting relevant document candidates using only DPR, I'm curious about your perspective on why we'd need to conduct re-ranking using a cross-encoder, in addition to extracting relevant document candidates by computing cosine similarity with a bi-encoder.

Please reply, Confused with so many approaches( BART,BERT,t5,pytorch...) in this domain, how would you develop a doable uni level Text Summarization project (MAJOR) at present time? Appreciate the work!