judge_embedding

first create a "data" and a "processed_data" folder Then put the "sentences_new" folder under "data" Then when you run the convert to binary program, the binaries are generated in the "processed_data" folder

Name		Name	Last commit message	Last commit date
.gitignore		.gitignore
BiLSTM_encoder.ipynb		BiLSTM_encoder.ipynb
Glove.ipynb		Glove.ipynb
Meta_data_preprocessing.ipynb		Meta_data_preprocessing.ipynb
Meta_data_preprocessing.py		Meta_data_preprocessing.py
README.md		README.md
circuit_district.ipynb		circuit_district.ipynb
circuit_district.py		circuit_district.py
citations_graph_preparation.ipynb		citations_graph_preparation.ipynb
district_id.ipynb		district_id.ipynb
district_id.py		district_id.py
embedding_visualization.ipynb		embedding_visualization.ipynb
embedding_visualization_centered.ipynb		embedding_visualization_centered.ipynb
embedding_visualization_no_federal.ipynb		embedding_visualization_no_federal.ipynb
embedding_visualization_no_federal_pred_ruling.ipynb		embedding_visualization_no_federal_pred_ruling.ipynb
model_centered_training.ipynb		model_centered_training.ipynb
model_no_federal.ipynb		model_no_federal.ipynb
model_no_federal.py		model_no_federal.py
model_training.ipynb		model_training.ipynb
opinion_preprocessing.ipynb		opinion_preprocessing.ipynb
party_affiliation_prediction.ipynb		party_affiliation_prediction.ipynb
party_affiliation_prediction.py		party_affiliation_prediction.py
pred_ruling_model_training.ipynb		pred_ruling_model_training.ipynb
pred_ruling_model_training.py		pred_ruling_model_training.py
requirements.txt		requirements.txt
sentences_to_csv.ipynb		sentences_to_csv.ipynb
train_centered_model.py		train_centered_model.py
train_model.py		train_model.py

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