# In[7]:
spreadsheet_names = {
#'train': 'data/sentences/sentence_labels_train.xlsx',
'dev': 'data/sentences/sentence_labels_dev.xlsx',
'test': 'data/sentences/sentence_labels_test.xlsx'
}
# In[8]:
candidate_dfs = {
key:load_candidate_dataframes(spreadsheet_names[key], "curated_cbg")
for key in spreadsheet_names
}
for key in candidate_dfs:
print("Size of {} set: {}".format(key, candidate_dfs[key].shape[0]))
# In[9]:
lfs = (
list(CG_LFS["CbG"].values()) +
list(DG_LFS["DaG"].values())[7:37] +
list(CD_LFS["CtD"].values())[3:25] +
list(GG_LFS["GiG"].values())[9:37]
quick_load = True
# ## Load the data for Generative Model Experiments
# In[6]:
spreadsheet_names = {
'train': '../../sentence_labels_train.xlsx',
'dev': '../../sentence_labels_dev.xlsx',
'test': '../../sentence_labels_test.xlsx'
}
# In[7]:
candidate_dfs = {
key: load_candidate_dataframes(spreadsheet_names[key])
for key in spreadsheet_names
}
for key in candidate_dfs:
print("Size of {} set: {}".format(key, candidate_dfs[key].shape[0]))
# In[8]:
label_functions = (list(DG_LFS["DaG"].values()))
if quick_load:
label_matricies = pickle.load(open("label_matricies.pkl", "rb"))
else:
#labeler = LabelAnnotator(lfs=label_functions)
label_matricies = {
