Comments (1)
Please provide an example of your data? If you basically just have a document containing rows, each row for one word and a word vector, what you have is a word embedding table. What's the point of using codes in this git? Alexandra's codes implemented Kim's CNN model in 2014 paper on CNN text classification. Which is defaco for sentence or documents.
from cnn-for-sentence-classification-in-keras.
Related Issues (20)
- error when retraining word vector HOT 3
- Error in w2v.py line 52 HOT 4
- TypeError: __init__() takes at least 3 arguments (2 given) HOT 1
- Running instructions HOT 1
- expected input_4 to have shape (None, 185) but got array with shape (1665, 35) HOT 1
- how to run trained model on sample sentence HOT 1
- Trying to replicate the results obtained with denny brtiz's code HOT 8
- Using local directory dataset does not yield the marked results HOT 1
- Using Glove or GoogleNews? HOT 1
- Wrong model for Y.Kim's TextCNN HOT 1
- Using pre-trained google word embeddings HOT 3
- Negative dimension size caused by subtracting 3 from 1
- 问题咨询 HOT 8
- How to train the model with multi-class dataset HOT 2
- accuracy HOT 3
- The model always predicts the same label HOT 1
- Two fully-connected layers after convolutions HOT 1
- as for the CNN-non-static model initialization issue
- Multiple Dropouts different from Original Paper and Denny Britz
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from cnn-for-sentence-classification-in-keras.