.. | ||
conv_net_classes.py | ||
evaluate.py | ||
get_params.py | ||
predictions.py | ||
process4T6SA.py | ||
README.md | ||
run.py | ||
score.py | ||
T6SA_conv_net_sentence.py | ||
T6SA_process_data.py |
Requirements
Python version - 2.7
You need to download Google news vectors 300 dimensional bin file and place it in the main directory to run the code You will need to have Theano=0.7 to run the code. Please refer : https://github.com/nestle1993/SE16-Task6-Stance-Detection
Usage
Training and Testing
python run.py <Path to the Data Directory>
Printing the scores for each dataset within data directory
python score.py <Path to the Data Directory>
Printing the tuned hyperparameters for each dataset within data directory
python get_params.py <Path to the Data Directory>
Note: The Data Directory be one of the preprocessed folders created by the preprocessing script. Example directory structure of the data directory for SemEval Data:
Data_SemE_P
├── AT
| ├── test_clean.txt
| ├── test_preprocessed.csv
| ├── train_clean.txt
| └── train_preprocessed.csv
├── CC
| ├── test_clean.txt
| ├── test_preprocessed.csv
| ├── train_clean.txt
| └── train_preprocessed.csv
├── FM
| ├── test_clean.txt
| ├── test_preprocessed.csv
| ├── train_clean.txt
| └── train_preprocessed.csv
├── HC
| ├── test_clean.txt
| ├── test_preprocessed.csv
| ├── train_clean.txt
| └── train_preprocessed.csv
└── LA
├── test_clean.txt
├── test_preprocessed.csv
├── train_clean.txt
└── train_preprocessed.csv