Comments (2)
<UNK>
means unknown token. This should be defined in the preprocessing step. ws
means word score, and it is calculated heuristically, so you can use it if you want, or not.
from pytorch-sgns.
Hi, yes, I understand what <UNK>
means. What I don't understand is why these dummy tokens (btw, I suppose they probably were meant to represent padding and not unknown words) are included in the output of skipgram function. I checked the original implementation of Word2Vec and I can't see there this step of including padding nor unknown tokens. What information should model learn from them being included as training examples?
from pytorch-sgns.
Related Issues (14)
- Confused by the loss function. HOT 2
- Whatβs the function of <UNK>? HOT 1
- Add requirements.txt
- Bug in the Loss Function HOT 2
- what is a good loss value finally? HOT 1
- myConfusion HOT 1
- An error occurred when testing the repo HOT 4
- Using of discard probabilities HOT 2
- Where is Expectation HOT 5
- why initialize the first line of embedding matrix as 0? t.zeros(1, self.embedding_size) HOT 5
- applying regularisation HOT 1
- Different embeddings for input/output words? HOT 2
- How to ensure that the negative sampled words are not the target word? HOT 1
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from pytorch-sgns.