Comments (1)
The 4th notebook is an implementation of the model from this paper which only has dropout after the max pooling. The 2nd notebook was just an introduction of concepts used in sentiment analysis, which do commonly use dropout on the embeddings so they're included there.
So no, it's not a mistake.
from pytorch-sentiment-analysis.
Related Issues (20)
- The train_data built from my own dataset after following the Appendix A looks wrong HOT 1
- migrating to the new API HOT 4
- .squeeze(1) HOT 5
- for word embedding in RNN model HOT 2
- Representation of similar words HOT 1
- Using a target size (torch.Size([64, 1])) that is different to the input size (torch.Size([304800, 1])) is deprecated. Please ensure they have the same size HOT 7
- train_test_split in LSTM HOT 2
- pad sequence in my dataset
- 6 - Transformers for Sentiment Analysis HOT 1
- Question in fasttext HOT 1
- ModuleNotFoundError
- How can I predict on one example?
- User Interface
- Got error" 'lengths' argument should be a 1D CPU int64 tensor, but got 1D cuda:0 Long tensor " HOT 10
- where is the trained model parameters ? HOT 1
- TypeError HOT 2
- Multi-class Sentiment Analysis: How to use custom dataset?
- how did you build torchtext from source HOT 4
- how does pytorch pad sentences ? HOT 3
- Pad Sequence error HOT 2
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from pytorch-sentiment-analysis.