Social_Media/Stance-Detection-in-Web-and-Social-Media
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corrected readme file

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Siddharth 2019-06-18 22:17:43 +05:30
parent a86f074dca
commit 226cbc05fd
1 changed files with 3 additions and 6 deletions
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TAN/README.md
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@ -1,4 +1,4 @@
# networks.py # networks.py
## `class LSTM_TAN(nn.Module):` ## `class LSTM_TAN(nn.Module):`
``` ```
def __init__(self,version,embedding_dim, hidden_dim, vocab_size, n_targets,embedding_matrix,dropout = 0.5): def __init__(self,version,embedding_dim, hidden_dim, vocab_size, n_targets,embedding_matrix,dropout = 0.5):
@ -24,7 +24,7 @@ __Args__
2. target - a numpy array of shape [1xM] and dtype int, where M is the length of the target and each entry is the corresponding index of the word in the `embedding_matrix 2. target - a numpy array of shape [1xM] and dtype int, where M is the length of the target and each entry is the corresponding index of the word in the `embedding_matrix
__Returns__ __Returns__
1. target_scores - a torch float Tensor of shape [1xn_targets], where N is the number of dataset classes. This is the log likelihood probabilities of all the classes 1. target_scores - a torch float Tensor of shape [1xn_targets], where N is the number of dataset classes. This is the log likelihood probabilities of all the classes
@ -59,10 +59,7 @@ if dataset == 'EC':
topic = "climate change is a real concern" topic = "climate change is a real concern"
elif dataset == 'FM' : elif dataset == 'FM' :
topic = "feminist movement" topic = "feminist movement"
elif dataset == 'VCA':
topic = "vaccines cause autism"
elif dataset == 'VTI':
topic = "vaccines treat influenza"
``` ```
2. version is one of ["lstm","tan-","tan+"] 2. version is one of ["lstm","tan-","tan+"]