Word Embeddings - EXPLAINED!

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Let's talk word embeddings in NLP!

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best explaining embedding so far from the video I watched on youtube. thanks and subscribed.

路人甲-gs

Thank you! This is the perfect level of summary I was looking for. I’m trying to figure out a certain use case, maybe someone reading can point me in the right direction..
How can one create embeddings that retain an imposed vector/parameter that represents the word chunks semantic location in a document? I.e, a phrase occurs in chapter 2 is meaningfully different from the same phrase in chapter 4. This seems to be achieved through parsing document by hand and inserting metadata. But it feels like there should be a more automatic way of doing this.

Jonathan-rmkt

Good video...explains things from a very high level, very well. But I'm trying to figure out why/how a single word would result in a large vector. So are the meanings of the word encoded into the vector? As an example, Cat would have ' fur, claws, mammal, kitten, animal...' etc, and result in a vector of say 100 elements? Even if the vector is generated by the computer, which obviously it does, each element in the vector has to represent something. I can't seem to get past this point. I understand what vec2vec does, I just don't know why it does it. Any help? Thanks.

sgrimm

I still don't understand how to convert words into numbers

markomilenkovic

great video but Im still a bit confused with what is currently being used for embedding? are you saying BERT is the next word2vec for embedding? is that what chatGPT4 uses? sorry if I didn't understand!

larrybird

Can you explain after training CBOW / Skip-gram models, how do you generate embeddings at inference time?
With Skip-gram, it is a bit intuitive that you would 1-hot encode the word and extract the output of embedding layer. Not sure how it works with CBOW where the input is a set of context words.

_seeker

Excellent video, you're a great teacher.

lorenzowottrich

It’s a little confusing Cz In many examples, a full chunk of text is converted into 1 embedding vector instead of multiple embedding vectors (one for each token of that chunk). Can you explain that ?

creativeuser

Are embedding models part of the base LLMs or are they a completely different model with different weights, and how does the training of embedding models look like?

creativeuser

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