Comments (2)
phillynch7
commented on July 24, 2024
1
Ahh, I see that now in the paper. Should have read more carefully, was mostly focused on working out the loss function.
Appreciate your help, thanks!
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theeluwin
commented on July 24, 2024
This implementation is totally based on the very first word2vec paper (https://arxiv.org/abs/1310.4546). Using the same embedding (also known as a siamese modeling) also works but according to the paper, the overall embedding is considered as 2-layered neural network with 1 hidden layer, interpreting W1 as 'input vector' and W2 as 'output vector'.
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Related Issues (14)
- Confused by the loss function. HOT 2
- What‘s 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
- Purpose of unks in skipgram function HOT 2
- 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
- How to ensure that the negative sampled words are not the target word? HOT 1
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