Sentence Transformers - EXPLAINED!

  Рет қаралды 31,553

CodeEmporium

CodeEmporium

Күн бұрын

Пікірлер: 46
@CodeEmporium
@CodeEmporium 2 жыл бұрын
Hey Everyone! Hope you're all doing super well. This video will give you everything you need to know about Transformer Neural Networks, BERT Networks and Sentence Transformers - or at least all that we can cover in 17 minutes. Hoping we all understand why these Architectures were developed the way they were, painting the picture as a fluid story. I'm trying another teaching style here. If you like this kind of video, please do let me know in the comments. Put a lot of effort into this, so I hope you think this is good! Enjoy! And Cheers!
@Daniel-gy1rc
@Daniel-gy1rc 2 жыл бұрын
dude you are amazing. Hope you keep this work up! Explaining complex things in an easy-to-follow and examplified way is a great skill!
@CodeEmporium
@CodeEmporium 2 жыл бұрын
Thanks a ton Daniel! Much appreciated complements :)
@brewingacupofdata
@brewingacupofdata 3 ай бұрын
Great Video! One minor comment: Shouldn't the loss equation from the triplet method (13:58) be the other way around? The difference that is subtracted should be between the anchor sentence and the negative sentence.
@sooryaprakash6390
@sooryaprakash6390 27 күн бұрын
I have the same doubt.
@manash.b4892
@manash.b4892 Жыл бұрын
Wow. Thanks a lot for all these videos. I am self-studying beginner and your videos have been a boon. Keep up the good work, man!
@TheHamoodz
@TheHamoodz 2 жыл бұрын
This channel has orders of magnitude more views than it deserves
@LiaAnggraini1
@LiaAnggraini1 2 жыл бұрын
Thank you! This is what I need for my thesis
@MohammadShafkatIslam-k4x
@MohammadShafkatIslam-k4x 3 ай бұрын
Great video. I just figured out the issue with my dataset, after I had bad results from directly using Roberta
@kevon217
@kevon217 Жыл бұрын
Excellent overview!
@BinayGupta-ny7bp
@BinayGupta-ny7bp 4 ай бұрын
Great explanation dude
@HazemAzim
@HazemAzim Жыл бұрын
really neat . Thank you , I was looking for nice stuff on SBERT with decent depth
@simoneparvizi775
@simoneparvizi775 2 жыл бұрын
Hey man huge fan! Would you do a video about the "vanishing gradient problem"? Tbh I've been looking for a good video on it, but they're just not on point as you are....I'd really like your explanation on such argument! Keep up with the great work
@WhatsAI
@WhatsAI 2 жыл бұрын
Amazing overview !
@Han-ve8uh
@Han-ve8uh 2 жыл бұрын
Could you explain these 2 points in more detail? 3:21 transformers weren't designed to be language models + 16:35 transformers not complex enough to train a language model 1. What are language models supposed to do that transformers can't? My interpretation is that transformers do seq-seq tasks like translation, and translation needs a language model, so transformers are language models. Anything wrong with this thinking? 2. Can I say transformers are only invented to parallelize RNN family of models with attention? Any other obvious general or task specific benefits of transformers?
@mizoru_
@mizoru_ 2 жыл бұрын
I guess he means that they get better through improved pretraining (thus understand language better) From Papers with code: "BERT improves upon standard Transformers by removing the unidirectionality constraint by using a masked language model (MLM) pre-training objective. The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context."
@nadavnesher8641
@nadavnesher8641 4 ай бұрын
Brilliant video 🚀
@safwanmohammed7715
@safwanmohammed7715 5 ай бұрын
Masterpiece 💯
@sooryaprakash6390
@sooryaprakash6390 27 күн бұрын
Great Video
@norlesh
@norlesh Жыл бұрын
5:11 Bidirectional Encoder Representation FROM Transformer (not of Transformers)
@Slayer-dan
@Slayer-dan 2 жыл бұрын
Thank you sir.
@NicholasRenotte
@NicholasRenotte 2 жыл бұрын
Oooooooh, this is so freaking cool!! When are we teaming up to build something?!
@CodeEmporium
@CodeEmporium 2 жыл бұрын
Dude. I will reach out ma guy (sorry i didn't before) :)
@NicholasRenotte
@NicholasRenotte 2 жыл бұрын
@@CodeEmporium ayyy no problemo man!
@miriamramstudio3982
@miriamramstudio3982 Жыл бұрын
Great video. One part I didn't completely understood is the NLI part. Do you mean that after that NLI step, the mean pooling sentence vector of the newly trained BERT not be "poor" anymore? Thanks.
@JJ-dz2ne
@JJ-dz2ne Жыл бұрын
Very informative, thank you!
@CodeEmporium
@CodeEmporium Жыл бұрын
You are very welcome! Thanks for watching and commenting
@prasannabiswas2727
@prasannabiswas2727 2 жыл бұрын
Really the best info out. Thank you.
@GeoffLadwig
@GeoffLadwig 10 ай бұрын
Great stuff. Thanks
@PritishMishra
@PritishMishra 2 жыл бұрын
Great video!!! Can we get some project videos on Transformer? As you showed in this video about the text-similarity with BERT so do you have any plan to create a video to do this with python?
@thekarthikbharadwaj
@thekarthikbharadwaj 2 жыл бұрын
Yes, really needed. Internet is lacking with an exact project developed using Transformers with proper backend information
@RaghavendraK458
@RaghavendraK458 2 жыл бұрын
Great video. Thanks
@masteronepiece6559
@masteronepiece6559 2 жыл бұрын
Nice overview
@clairewang8370
@clairewang8370 2 жыл бұрын
This is 🔥!!!😍😍😍😍😍
@TheShadyStudios
@TheShadyStudios 2 жыл бұрын
Great choice!
@freedmoresidume
@freedmoresidume 2 жыл бұрын
Great video, thanks a lot
@CodeEmporium
@CodeEmporium 2 жыл бұрын
Welcome:)
@kestonsmith1354
@kestonsmith1354 2 жыл бұрын
My favourite model to train is T5. So much better . I don't like encoder models . I rather use a model that uses both encoder and decoder rather than either/or.
@keerthana2354
@keerthana2354 2 жыл бұрын
Can we use this for comparing two web articles?
@moslehmahamud
@moslehmahamud 2 жыл бұрын
this is good!
@shoukatali5671
@shoukatali5671 2 жыл бұрын
Приятно
@roccococolombo2044
@roccococolombo2044 2 жыл бұрын
It is spelled chien nor chein
@InquilineKea
@InquilineKea 2 жыл бұрын
QUORAAAAA
@CodeEmporium
@CodeEmporium 2 жыл бұрын
AAAHHH
BERT for Topic Modeling - EXPLAINED!
35:28
CodeEmporium
Рет қаралды 18 М.
Transformer Embeddings - EXPLAINED!
15:43
CodeEmporium
Рет қаралды 33 М.
99.9% IMPOSSIBLE
00:24
STORROR
Рет қаралды 31 МЛН
人是不能做到吗?#火影忍者 #家人  #佐助
00:20
火影忍者一家
Рет қаралды 20 МЛН
Леон киллер и Оля Полякова 😹
00:42
Канал Смеха
Рет қаралды 4,7 МЛН
Гениальное изобретение из обычного стаканчика!
00:31
Лютая физика | Олимпиадная физика
Рет қаралды 4,6 МЛН
Transformers (how LLMs work) explained visually | DL5
27:14
3Blue1Brown
Рет қаралды 4 МЛН
Why Does Diffusion Work Better than Auto-Regression?
20:18
Algorithmic Simplicity
Рет қаралды 398 М.
Visualizing transformers and attention | Talk for TNG Big Tech Day '24
57:45
Transformer Neural Networks - EXPLAINED! (Attention is all you need)
13:05
Vectoring Words (Word Embeddings) - Computerphile
16:56
Computerphile
Рет қаралды 299 М.
Sentence Embeddings - EXPLAINED!
16:59
CodeEmporium
Рет қаралды 3,4 М.
Attention in transformers, visually explained | DL6
26:10
3Blue1Brown
Рет қаралды 1,9 МЛН
Intro to Sentence Embeddings with Transformers
31:06
James Briggs
Рет қаралды 26 М.
99.9% IMPOSSIBLE
00:24
STORROR
Рет қаралды 31 МЛН