airalcorn2 / deep-semantic-similarity-model Goto Github PK

neg_l_Ds = [[] for j in range(J)] for i in range(sample_size): possibilities = list(range(sample_size)) possibilities.remove(i) negatives = np.random.choice(possibilities, J, replace = False) for j in range(J): negative = negatives[j] neg_l_Ds[j].append(pos_l_Ds[negative])
I think negative sample is not the corresponding search result. So why neg_l_Ds are derived from pos_l_Ds?

the length of sentences are variable? How you deal with this ?

Hey.. Can an example be provided on how to train the network and then use it for a pair of documents? Thanks!

Hi, I stumbled upon your implementation of dssm and was wondering the following: should the different weights matrices be shared between query and doc?

Hi! Thank you for making this code! I'm studying CDSSM and trying to import dataset in. I'm new to this so I'm a little confused, so sorry if this question is too easy. If I port data from a dataset, should it be in an array of one-hot vectors?

Hello sir

I have been working on implementing DSSM and CDSSM for a while now. Your code is excellent, easy to understand and the comments help to follow it in perfect sync with the paper.
The entire code is running. However, I wanted to see the value of R_Q_D_p or R_Q_D_ns in the end, and could not to do that. Backend's eval function gives an error, and I am unable to view the actual score inside the tensor.

I would be grateful if you could tell how to view the value/scores.

Once again, I really appreciate your efforts and well-written code!

Thanks for this! Your code is readable and properly commented.
Could you recommend any datasets to train this on?

I have trouble understanding this fitting process:

for i in range(sample_size):
        history = model.fit([l_Qs[i], pos_l_Ds[i]] + [neg_l_Ds[j][i] for j in range(J)], y, epochs = 1, verbose = 0)

where each of the training sample goes through the network only once. I don't think it is applicable.

On the other hand, I don't think using a padded input is applicable, either. It just doesn't match the original method. Could someone give me some advice on how to deal with variable inputs (in the paper)?

I know question-question match is a text similarity problem.
What about question-answer match or question-doc match? It is used in information retrieval.

Thank you!!

Hi,

Thank you so much for making this code!

For the variable length input, if I understand you correctly, it just can fit one query by one query instead of fitting all data? So I would like to know, whether there exist some tricks to fit all of the variable length input in one batch?

I am encountering an error while running the file using python3