sweta20 / detecting-cyberbullying-across-smps Goto Github PK
View Code? Open in Web Editor NEWCode for the paper 'Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms', ECIR'18
Code for the paper 'Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms', ECIR'18
Ma'am, I am working on this very project as my minor project for engineering but I am facing problems in running the code. Can u please tell me on which python version have u created the pickle file and I am facing an error in the below mentioned line of code.
x_text.append(p.tokenize((data[i]['text']).encode('utf-8')))
the error is type error it says can't apply string pattern on bytes type object
What are the versions for the used libraries? A requirements.txt file with exact versions would be great for reproducibility.
Hey there,
Some colleagues and I are doing a reproducibility study for this paper. Sadly the link called "Sentiment Specific word embeddings (SSWE)" in the README is down.
I searched around and can't seem to find a backup anywhere. The file should be called "embedding-results.zip".
It'd be awesome if somebody could provide a version of the embeddings.
Thanks a lot in advance :)
In your project in the function train i got the following error
model = get_model(model_type, trainX.shape[1], vocab_size, embed_size, NUM_CLASSES, LEARN_RATE)
initial_weights = model.get_weights()
shuffle_weights(model,model.get_weights())
TypeError: get_weights() takes exactly 2 arguments (1 given)
what to do to solve the error
When I'm reproducing this article, I always get an index overflow problem, as shown below
IndexError: index 18831 is out of bounds for axis 0 with size 18831
Apply node that caused the error: AdvancedSubtensor1(embedding_2/embedding_3/embedding_4/embedding_10/embeddings, Elemwise{Cast{int32}}.0)
Toposort index: 77
Inputs types: [TensorType(float32, matrix), TensorType(int32, vector)]
Inputs shapes: [(18831, 25), (7936,)]
Inputs strides: [(100, 4), (4,)]
Inputs values: ['not shown', 'not shown']
Inputs type_num: [11, 5]
Outputs clients: [[Reshape{3}(AdvancedSubtensor1.0, MakeVector{dtype='int64'}.0)]]
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