Comments (7)
Agreed, it's opaque how to use this for new documents after training, which went great. Any chance of some more insight into how to apply it?
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Same question !
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That would be Very helpful, I hope you can provide us with such functions
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Same question
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any update ? I have the same question
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Same question!
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I am on the same issue - there are various places where you can get a return value which is the size of your topics, which is what you want, from looking at the eval script I have this:
NUM_TOPICS = 128
def predict(normd_bow):
thetaAvg = torch.zeros(1, NUM_TOPICS)
sums = normd_bow.sum(1).unsqueeze(1)
thetaWeightedAvg = torch.zeros(1, NUM_TOPICS)
theta, _ = model.get_theta(normd_bow)
thetaAvg += theta.sum(0).unsqueeze(0) / normd_bow.shape[0]
weighed_theta = sums * theta
thetaWeightedAvg += weighed_theta.sum(0).unsqueeze(0)
return thetaWeightedAvg
Created from this code.
EDIT: I have no idea if this is correct!
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Related Issues (20)
- perform bad on classifiction task HOT 3
- FileNotFoundError: [Errno 2] No such file or directory: 'data/20ng_embeddings.txt'
- How to get the topic vector? HOT 3
- rising KL_theta values
- 代码运行 HOT 1
- Is that true that a lot of repeated topics appear? HOT 7
- Topic Coherence Computation: Division by 45? HOT 2
- How to modify the code to number of topics other than 50? HOT 1
- Validation set loss is being calculated on the Test set.
- Negative coherence on short texts HOT 1
- Run ETM on my own dataset HOT 3
- How to obtain document-topic proportions (the thetas) for each document HOT 3
- a bug in test dataset splitting HOT 1
- evaluate
- Add predictive measure to utils.py
- dataset
- embedding HOT 3
- Confuse about the data loader function HOT 6
- read embedding matrix when not using trained embeddings HOT 1
- args.clip
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