def sentence_retrieval(self):
print('- sentence retrieval: initialise')
word_dict = pickle.load(open(self.path_word_dict_stage_2, "rb"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(self.path_stage_2_model)
vocab_size = checkpoint["model"]["_word_embedding.weight"].size(0)
embedding_dim = checkpoint["model"]['_word_embedding.weight'].size(1)
hidden_size = checkpoint["model"]["_projection.0.weight"].size(0)
num_classes = checkpoint["model"]["_classification.4.weight"].size(0)
use_oov_flag = 0
if 'oov' in self.embeddings_settings_sentence_retrieval_list:
use_oov_flag = 1
use_pos_tag_flag = 0
if 'pos' in self.embeddings_settings_sentence_retrieval_list:
use_pos_tag_flag = 1
model = ESIM(vocab_size,
embedding_dim,
hidden_size,
num_classes=num_classes,
use_pos_tag_flag=use_pos_tag_flag,
use_oov_flag=use_oov_flag,
device=device).to(device)
model.load_state_dict(checkpoint["model"])
model.eval()
print('- sentence retrieval: iterate through claims')
for claim_nr in tqdm(range(self.nr_claims)):
path_claim = os.path.join(self.path_document_retrieval_dir,
str(claim_nr) + '.json')
claim_dict = dict_load_json(path_claim)
list_prob = []
list_doc_nr = []
list_line_nr = []
for doc_nr in claim_dict['document_retrieval']:
for line_nr in claim_dict['document_retrieval'][doc_nr]:
if 'sentence_retrieval' not in claim_dict:
claim_dict['sentence_retrieval'] = {}
if doc_nr not in claim_dict['sentence_retrieval']:
claim_dict['sentence_retrieval'][doc_nr] = {}
if line_nr not in claim_dict['sentence_retrieval'][doc_nr]:
claim_dict['sentence_retrieval'][doc_nr][line_nr] = {}
prob = compute_prob_stage_2(model, claim_dict, doc_nr,
line_nr, device)
claim_dict['sentence_retrieval'][doc_nr][line_nr][
'prob'] = prob
list_doc_nr.append(doc_nr)
list_line_nr.append(line_nr)
list_prob.append(prob)
sorted_list_doc_nr = sort_list(list_doc_nr, list_prob)[-5:]
sorted_list_line_nr = sort_list(list_line_nr, list_prob)[-5:]
sorted_list_prob = sort_list(list_prob, list_prob)[-5:]
claim_dict['sentence_retrieval'][
'doc_nr_list'] = sorted_list_doc_nr
claim_dict['sentence_retrieval'][
'line_nr_list'] = sorted_list_line_nr
claim_dict['sentence_retrieval']['prob_list'] = sorted_list_prob
claim_dict['predicted_evidence'] = []
for i in range(len(sorted_list_doc_nr)):
doc_nr = sorted_list_doc_nr[i]
title = wiki_database.get_title_from_id(int(doc_nr))
line_nr = int(sorted_list_line_nr[i])
claim_dict['predicted_evidence'].append([title, line_nr])
path_save = os.path.join(self.path_sentence_retrieval_dir,
str(claim_nr) + '.json')
self.save_dict(claim_dict, path_save)
def label_prediction(self):
print('- label prediction: initialise')
word_dict = pickle.load(open(self.path_word_dict_stage_3, "rb"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
checkpoint = torch.load(self.path_stage_3_model)
vocab_size = checkpoint["model"]["_word_embedding.weight"].size(0)
embedding_dim = checkpoint["model"]['_word_embedding.weight'].size(1)
hidden_size = checkpoint["model"]["_projection.0.weight"].size(0)
num_classes = checkpoint["model"]["_classification.4.weight"].size(0)
use_oov_flag = 0
if 'oov' in self.embeddings_settings_label_prediction_list:
use_oov_flag = 1
use_pos_tag_flag = 0
if 'pos' in self.embeddings_settings_label_prediction_list:
use_pos_tag_flag = 1
model = ESIM(vocab_size,
embedding_dim,
hidden_size,
num_classes=num_classes,
use_pos_tag_flag=use_pos_tag_flag,
use_oov_flag=use_oov_flag,
device=device).to(device)
model.load_state_dict(checkpoint["model"])
model.eval()
print('- label prediction: iterate through claims')
for claim_nr in tqdm(range(self.nr_claims)):
path_claim = os.path.join(self.path_sentence_retrieval_dir,
str(claim_nr) + '.json')
claim_dict = dict_load_json(path_claim)
prob_list = []
prob_list_supported = []
prob_list_refuted = []
for i in range(len(
claim_dict['sentence_retrieval']['doc_nr_list'])):
doc_nr = claim_dict['sentence_retrieval']['doc_nr_list'][i]
line_nr = claim_dict['sentence_retrieval']['line_nr_list'][i]
if doc_nr in claim_dict['document_retrieval']:
if line_nr in claim_dict['document_retrieval'][doc_nr]:
prob = compute_prob_stage_3(model, claim_dict, doc_nr,
line_nr, device)
prob_list.append(prob)
prob_list_supported.append(prob[2])
prob_list_refuted.append(prob[1])
else:
print('line_nr not in list', line_nr)
else:
print('doc_nr not in list', doc_nr)
if max(prob_list_supported) > 0.5:
claim_dict['predicted_label'] = 'SUPPORTS'
elif max(prob_list_refuted) > 0.5:
claim_dict['predicted_label'] = 'REFUTES'
else:
claim_dict['predicted_label'] = 'NOT ENOUGH INFO'
path_save = os.path.join(self.path_label_prediction_dir,
str(claim_nr) + '.json')
self.save_dict(claim_dict, path_save)