Help with seminar on conversation about nlp_course

[justheuristic]

Hi!

Disclaimer: i'm not very well versed with metric learning tasks. An expert's opinion should be preferable to mine.

(0) Not all BERT-like models are created equal :) There is a particular subtype that is aimed at embedding whole sentences -- no guarantees, but it might be worth trying. Here's a lib that has a bunch of them: https://github.com/UKPLab/sentence-transformers
(1) dim=16 seems too small based on my (limited) experience. The actual dimension should depend on the number of classes, but last time I was working on a similar architecture for retrieval, the optimal dimensions were in 128-1024 range
(2) If the dataset is large enough (so, tens of thousands, rather than hundreds), it is usually beneficial to also fine-tune BERT layers with a small learning rate. In that case, you can worry less about the architecture

[maiiabocharova]

Hi!

Disclaimer: i'm not very well versed with metric learning tasks. An expert's opinion should be preferable to mine.

(0) Not all BERT-like models are created equal :) There is a particular subtype that is aimed at embedding whole sentences -- no guarantees, but it might be worth trying. Here's a lib that has a bunch of them: https://github.com/UKPLab/sentence-transformers (1) dim=16 seems too small based on my (limited) experience. The actual dimension should depend on the number of classes, but last time I was working on a similar architecture for retrieval, the optimal dimensions were in 128-1024 range (2) If the dataset is large enough (so, tens of thousands, rather than hundreds), it is usually beneficial to also fine-tune BERT layers with a small learning rate. In that case, you can worry less about the architecture

Thanks a lot for answering! I used sentence-transformers. Also had the idea that it would be better than using just Bert. For starters I used miniLM-v6 as it was quick to train.

I have 140k datapoints in datasets. Number of classes is 6, but 80% of the datapoints belong to 1 class — class "others" and to most under-represented class belong only 0.5% of datapoints. The thing is that after training the embeddings I need to somehow use them to later classify new sentences. And here is where I am stuck. I thought about using SVM but it will not work with such high dimensional datapoints. I am now thinking about using a fully connected network with a couple of dense layers in order to classify datapoints according to embeddings. But that would once again leave me with a problem of very imbalanced dataset.