Comments (4)
Hi, I think, if self.separate_qa=True, qa_data shape is [BS, Seqlen] (line 72) with value from 0 to 2*n_question+1 (line 45) and self.qa_embed_diff = nn.Embedding(2 * self.n_question + 1, embed_l). When self.separate_qa=False, qa_data shape is [BS, Seqlen] (line 76) with value from 0 to 1 (line 48) and self.qa_embed = nn.Embedding(2, embed_l).
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When self.separate_qa=False, qa_data shape is [BS, Seqlen] (line 76) with value from 0 to 1 (line 48) and self.qa_embed = nn.Embedding(2, embed_l). But, if self.n_pid > 0, (line 83) qa_embed_diff_data = self.qa_embed_diff(qa_data), and (line 42) self.qa_embed_diff = nn.Embedding(2 * self.n_question + 1, embed_l), the shape of qa_data is inconsistent with "2 * self.n_question + 1".
Please check it again! Thanks!
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Yes, you are right. only 0 and 1 is used, rest of them are not updated. You can change line 42 depending on the condition. It would be the same.
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Related Issues (16)
- Comparison to other models (SAKT, DKT, DKVMN) HOT 2
- What is PID assist2009_pid HOT 1
- why in test() function. The qa_data pass into model? HOT 6
- kq_same? HOT 1
- Why don't all the models care repeated response sequences with different skill tagging? HOT 1
- "key_padding_mask" in attention mechanism not be implemented? HOT 2
- Masking or slicing allowing the model to use previous interactions HOT 4
- statics data: some users occur in a same dataset more than once or occur in both training and validation/testing data
- hyper-parameters of AKT for achieving the best mean test AUC
- dropout in attention function
- Padding Problem
- target response issue in AKT model
- How to draw the Fig.2?
- Issue while running AKT with individual snp data
- Loss of Model Performance after Removing 'with torch.no_grad()' at Line 304 in akt.py
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