yumeng5 / topclus Goto Github PK

Hello,
Could you please share the script used for evaluation? The obtained results may differ with different evaluation scripts.
Thanks a lot!

Best wishes,
Lei

Dear authors,
Thanks for your excellent work! I'm running the model on my own dataset and trying to understand the code. However, I always get cluster_loss = 0. I am using a Twitter dataset which is noisy and sparse, I have changed the BERT sequense length to 128 acoordingly in the pre-processing step. I have tried several different batch size and lr. (It seems to be working fine on the given yelp examples). Does anyone has the same problem or perhaps know why..? Thanks in advance!

Hi,

I've read your paper and I like this approach. Thank you for sharing the code. I've one question regarding the pretrained language models (PLMs) that you use for getting the contextualized word representations. I saw in the source code that the model you use is fixed, and it's the classical 'bert-base-uncased':

Suppose I'm interested on using this method on a corpus of italian texts. In that case, would it be possible to change this model and use a bert-base-multilingual-uncased instead?

If that's possible, can we make pretrained_lm a parameter of the TopClusTrainer?

Thank you.

Hi, thank you for sharing the code:)

Since I am a novice here, It may be a trivial question; but it seems that NMI is tested on the training dataset.

Is this a typical setting for measuring the NMI? (measuring on the training set)

Thank you

Thank you for sharing the code. As far as I understand, this stage only trains autoencoder parameters, so It seems that

'''
Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=self.args.lr)
'''
should be converted to

'''
Adam(filter(lambda p: p.requires_grad, model.ae.parameters()), lr=self.args.lr).
'''

If it is not, please let me know.

I like your paper but I think it's confusing that how to tackle multiple embeddings of the same word/token. I wonder is there any chance that different embeddings of the same word are mapped to different clusters and all of them are quite close to the cluster center in the spherical space. How do you deal with that?