def get_sentence_pair(top_k, d_list, p_level_results_list, is_training, debug_mode=False):
#
t_db_cursor = wiki_db_tool.get_cursor(config.WHOLE_PROCESS_FOR_RINDEX_DB)
#
# dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
# dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_list = d_list
# cur_dev_eval_results_list = common.load_jsonl(
# config.PRO_ROOT / "data/p_hotpotqa/hotpotqa_document_level/2019_4_17/dev_p_level_bert_v1_results.jsonl")
cur_dev_eval_results_list = p_level_results_list
if debug_mode:
dev_list = dev_list[:100]
id_set = set([item['_id'] for item in dev_list])
cur_dev_eval_results_list = [item for item in p_level_results_list if item['qid'] in id_set]
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(cur_dev_eval_results_list, copied_dev_o_dict,
'qid', 'fid', check=True)
cur_results_dict_top2 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=top_k, filter_value=None)
# print(cur_results_dict_top2)
fitems = build_sentence_forward_item(cur_results_dict_top2, dev_list, is_training=is_training,
db_cursor=t_db_cursor)
return fitems
def get_paragraph_forward_pair(tag,
ruleterm_doc_results,
is_training,
debug=False,
ignore_non_verifiable=False):
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
if debug:
d_list = d_list[:100]
ruleterm_doc_results = ruleterm_doc_results[:100]
ruleterm_doc_results_dict = list_dict_data_tool.list_to_dict(
ruleterm_doc_results, 'id')
db_cursor = fever_db.get_cursor()
fitems = build_full_wiki_document_forward_item(ruleterm_doc_results_dict,
d_list, is_training,
db_cursor,
ignore_non_verifiable)
return fitems
def get_sentences(tag, is_training, debug=False):
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
if debug:
# d_list = d_list[:10]
d_list = d_list[:50]
# d_list = d_list[:200]
doc_results = common.load_jsonl(
config.RESULT_PATH /
f"doc_retri_results/fever_results/merged_doc_results/m_doc_{tag}.jsonl"
)
doc_results_dict = list_dict_data_tool.list_to_dict(doc_results, 'id')
fever_db_cursor = fever_db.get_cursor(config.FEVER_DB)
forward_items = build_full_wiki_document_forward_item(
doc_results_dict,
d_list,
is_training=is_training,
db_cursor=fever_db_cursor)
return forward_items
def get_inference_pair(tag,
is_training,
sent_result_path,
debug_num=None,
evidence_filtering_threshold=0.01):
# sent_result_path = ""
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
if debug_num is not None:
# d_list = d_list[:10]
d_list = d_list[:50]
# d_list = d_list[:200]
d_dict = list_dict_data_tool.list_to_dict(d_list, 'id')
threshold_value = evidence_filtering_threshold
# sent_list = common.load_jsonl(
# config.RESULT_PATH / "doc_retri_results/fever_results/sent_results/4-14-sent_results_v0/train_sent_results.jsonl")
# sent_list = common.load_jsonl(
# config.RESULT_PATH / "doc_retri_results/fever_results/sent_results/4-14-sent_results_v0/i(5000)|e(0)|s01(0.9170917091709171)|s05(0.8842384238423843)|seed(12)_dev_sent_results.json")
# debug_num = None if not debug else 2971
# debug_num = None
if isinstance(sent_result_path, Path):
sent_list = common.load_jsonl(sent_result_path, debug_num)
elif isinstance(sent_result_path, list):
sent_list = sent_result_path
else:
raise ValueError(
f"{sent_result_path} is not of a valid argument type which should be [list, Path]."
)
list_dict_data_tool.append_subfield_from_list_to_dict(sent_list,
d_dict,
'oid',
'fid',
check=True)
filltered_sent_dict = select_top_k_and_to_results_dict(
d_dict, top_k=5, threshold=threshold_value)
list_dict_data_tool.append_item_from_dict_to_list(
d_list, filltered_sent_dict, 'id',
['predicted_evidence', 'predicted_scored_evidence'])
fever_db_cursor = fever_db.get_cursor(config.FEVER_DB)
forward_items = build_nli_forward_item(d_list,
is_training=is_training,
db_cursor=fever_db_cursor)
return forward_items, d_list
def get_nli_pair(tag,
is_training,
sent_level_results_list,
debug=None,
sent_top_k=5,
sent_filter_value=0.05):
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
if debug:
d_list = d_list[:100]
# sent_dict = list_dict_data_tool.list_to_dict(sent_level_results_list):
d_dict = list_dict_data_tool.list_to_dict(d_list, 'id')
if debug:
id_set = set([item['id'] for item in d_list])
new_sent_list = []
for item in sent_level_results_list:
if item["qid"] in id_set:
new_sent_list.append(item)
sent_level_results_list = new_sent_list
list_dict_data_tool.append_subfield_from_list_to_dict(
sent_level_results_list, d_dict, 'qid', 'fid', check=True)
filltered_sent_dict = select_top_k_and_to_results_dict(
d_dict,
score_field_name='prob',
top_k=sent_top_k,
filter_value=sent_filter_value,
result_field='predicted_evidence')
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
d_list, filltered_sent_dict, 'id',
['predicted_evidence', 'selected_scored_results'])
fever_db_cursor = fever_db.get_cursor(config.FEVER_DB)
forward_items = build_nli_forward_item(d_list,
is_training=is_training,
db_cursor=fever_db_cursor)
return forward_items, d_list
def eval_ensemble():
sent_file = config.PRO_ROOT / "data/p_fever/fever_sentence_level/04-24-00-11-19_fever_v0_slevel_retri_(ignore_non_verifiable-True)/fever_s_level_dev_results.jsonl"
dev_sent_filtering_prob = 0.01
tag = 'dev'
top_k = 5
# dev_list = common.load_jsonl(config.FEVER_DEV)
dev_sent_results_list = common.load_jsonl(sent_file)
dev_fitems, dev_list = get_nli_pair(
tag,
is_training=False,
sent_level_results_list=dev_sent_results_list,
debug=False,
sent_top_k=top_k,
sent_filter_value=dev_sent_filtering_prob)
pred_file_list = [
config.PRO_ROOT /
"data/p_fever/fever_nli/04-25-22:02:53_fever_v2_nli_th0.2/ema_i(20000)|e(3)|ss(0.7002700270027002)|ac(0.746024602460246)|pr(0.6141389138913633)|rec(0.8627362736273627)|f1(0.7175148212089147)|seed(12)/nli_dev_label_results_th0.2.jsonl",
config.PRO_ROOT /
"data/p_fever/fever_nli/04-26-10:15:39_fever_v2_nli_th0.2/ema_i(14000)|e(2)|ss(0.6991199119911992)|ac(0.7492249224922493)|pr(0.7129412941294097)|rec(0.8338583858385838)|f1(0.7686736484619933)|seed(12)/nli_dev_label_results_th0.2.jsonl",
config.PRO_ROOT /
"data/p_fever/fever_nli/04-27-10:03:27_fever_v2_nli_th0.2/ema_i(26000)|e(3)|ss(0.6958695869586958)|ac(0.7447744774477447)|pr(0.7129412941294097)|rec(0.8338583858385838)|f1(0.7686736484619933)|seed(12)/nli_dev_label_results_th0.2.jsonl",
]
pred_d_list = [common.load_jsonl(file) for file in pred_file_list]
final_list = ensemble_nli_results(pred_d_list)
pred_list = final_list
ema_results_dict = list_dict_data_tool.list_to_dict(pred_list, 'oid')
copied_dev_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_item_from_dict_to_list(copied_dev_list,
ema_results_dict, 'id',
'predicted_label')
dev_list = common.load_jsonl(config.FEVER_DEV)
mode = {'standard': True}
strict_score, acc_score, pr, rec, f1 = fever_scorer.fever_score(
copied_dev_list, dev_list, mode=mode, max_evidence=5)
logging_item = {
'ss': strict_score,
'ac': acc_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
print(logging_item)
def inspect_upstream_eval():
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
dev_eval_results_list = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_sentence_level/04-19-02:17:11_hotpot_v0_slevel_retri_(doc_top_k:2)/i(12000)|e(2)|v02_f1(0.7153646038858843)|v02_recall(0.7114645831323757)|v05_f1(0.7153646038858843)|v05_recall(0.7114645831323757)|seed(12)/dev_s_level_bert_v1_results.jsonl"
)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
dev_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
# 0.5
# cur_results_dict_v05 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5,
# score_field_name='prob',
# filter_value=0.5,
# result_field='sp')
cur_results_dict_v02 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.2,
result_field='sp')
# _, metrics_v5 = ext_hotpot_eval.eval(cur_results_dict_v05, dev_list, verbose=False)
_, metrics_v2 = ext_hotpot_eval.eval(cur_results_dict_v02,
dev_list,
verbose=False)
v02_sp_f1 = metrics_v2['sp_f1']
v02_sp_recall = metrics_v2['sp_recall']
v02_sp_prec = metrics_v2['sp_prec']
v05_sp_f1 = metrics_v5['sp_f1']
v05_sp_recall = metrics_v5['sp_recall']
v05_sp_prec = metrics_v5['sp_prec']
logging_item = {
'label': 'ema',
'v02': metrics_v2,
# 'v05': metrics_v5,
}
print(logging_item)
def eval_hotpot_s():
cur_dev_eval_results_list_out = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpot_p_level_effects/hotpot_s_level_dev_results_top_k_doc_100.jsonl"
)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_dev_eval_results_list_out,
copied_dev_o_dict,
'qid',
'fid',
check=True)
# 0.5
cur_results_dict_v05 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.5,
result_field='sp')
# cur_results_dict_v02 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5,
# score_field_name='prob',
# filter_value=0.2,
# result_field='sp')
_, metrics_v5 = ext_hotpot_eval.eval(cur_results_dict_v05,
dev_list,
verbose=False)
# _, metrics_v2 = ext_hotpot_eval.eval(cur_results_dict_v02, dev_list, verbose=False)
logging_item = {
# 'v02': metrics_v2,
'v05': metrics_v5,
}
print(logging_item)
f1 = metrics_v5['sp_f1']
em = metrics_v5['sp_em']
pr = metrics_v5['sp_prec']
rec = metrics_v5['sp_recall']
print(em, pr, rec, f1)
def get_sentence_forward_pair(tag,
ruleterm_doc_results,
is_training,
debug=False,
ignore_non_verifiable=False,
top_k=5,
filter_value=0.005):
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
if debug:
d_list = d_list[:100]
ruleterm_doc_results = ruleterm_doc_results[:100]
# ruleterm_doc_results_dict = list_dict_data_tool.list_to_dict(ruleterm_doc_results, 'id')
d_o_dict = list_dict_data_tool.list_to_dict(d_list, 'id')
copied_d_o_dict = copy.deepcopy(d_o_dict)
# copied_d_list = copy.deepcopy(d_list)
list_dict_data_tool.append_subfield_from_list_to_dict(ruleterm_doc_results,
copied_d_o_dict,
'qid',
'fid',
check=True)
cur_results_dict_filtered = select_top_k_and_to_results_dict(
copied_d_o_dict,
score_field_name='prob',
top_k=top_k,
filter_value=filter_value)
db_cursor = fever_db.get_cursor()
fitems = build_full_wiki_sentence_forward_item(cur_results_dict_filtered,
d_list, is_training,
db_cursor,
ignore_non_verifiable)
return fitems
def eitems_to_fitems(eitem_list,
tokenizer,
is_training,
max_tokens_for_doc=320,
max_query_length=64,
doc_stride=128,
debug=False):
fitem_list = [] # The output of all fitems
if debug:
eitem_list = eitem_list[:100]
for item in tqdm(eitem_list):
f_items = preprocssing_span_prediction_item_paired(
item, tokenizer, is_training, max_tokens_for_doc, max_query_length,
doc_stride)
fitem_list.extend(f_items)
fitem_dict = list_dict_data_tool.list_to_dict(fitem_list, 'fid')
return fitem_dict, fitem_list
def get_open_qa_item_with_upstream_paragraphs(d_list,
cur_eval_results_list,
is_training,
tokenizer: BertTokenizer,
max_context_length,
max_query_length,
doc_stride=128,
debug_mode=False,
top_k=10,
filter_value=0.1,
match_type='string'):
t_cursor = wiki_db_tool.get_cursor(config.WHOLE_WIKI_RAW_TEXT)
if debug_mode:
d_list = d_list[:100]
id_set = set([item['question'] for item in d_list])
cur_eval_results_list = [
item for item in cur_eval_results_list if item['qid'] in id_set
]
d_o_dict = list_dict_data_tool.list_to_dict(d_list, 'question')
copied_d_o_dict = copy.deepcopy(d_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_d_o_dict, 'qid', 'fid', check=True)
cur_results_dict_top10 = od_sample_utils.select_top_k_and_to_results_dict(
copied_d_o_dict,
score_field_name='prob',
top_k=top_k,
filter_value=filter_value)
forward_example_items = build_open_qa_forword_item(cur_results_dict_top10,
d_list, is_training,
t_cursor, match_type)
forward_example_items = format_convert(forward_example_items, is_training)
fitems_dict, read_fitems_list = span_preprocess_tool.eitems_to_fitems(
forward_example_items, tokenizer, is_training, max_context_length,
max_query_length, doc_stride, False)
return fitems_dict, read_fitems_list, cur_results_dict_top10['pred_p_list']
def eval_p_level():
cur_eval_results_list = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/dev_p_level_bert_v1_results.jsonl"
)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
# Top_5
cur_results_dict_top5 = select_top_k_and_to_results_dict(copied_dev_o_dict,
top_k=5)
_, metrics_top5 = ext_hotpot_eval.eval(cur_results_dict_top5,
dev_list,
verbose=False)
print(metrics_top5)
def get_qa_item_with_upstream_sentence(d_list,
sentence_level_results,
is_training,
tokenizer: BertTokenizer,
max_context_length,
max_query_length,
doc_stride=128,
debug_mode=False,
top_k=5,
filter_value=0.2):
t_db_cursor = wiki_db_tool.get_cursor(config.WHOLE_PROCESS_FOR_RINDEX_DB)
if debug_mode:
d_list = d_list[:100]
id_set = set([item['_id'] for item in d_list])
sentence_level_results = [
item for item in sentence_level_results if item['qid'] in id_set
]
d_o_dict = list_dict_data_tool.list_to_dict(d_list, '_id')
copied_d_o_dict = copy.deepcopy(d_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
sentence_level_results, copied_d_o_dict, 'qid', 'fid', check=True)
cur_results_dict = select_top_k_and_to_results_dict(
copied_d_o_dict,
top_k=top_k,
score_field_name='prob',
filter_value=filter_value,
result_field='sp')
forward_example_items = build_qa_forword_item(cur_results_dict, d_list,
is_training, t_db_cursor)
forward_example_items = format_convert(forward_example_items, is_training)
fitems_dict, read_fitems_list = span_preprocess_tool.eitems_to_fitems(
forward_example_items, tokenizer, is_training, max_context_length,
max_query_length, doc_stride, False)
return fitems_dict, read_fitems_list, cur_results_dict['sp']
def evidence_adjustment(tag, sent_file, label_file, filter_prob=0.2, top_k=5):
dev_sent_filtering_prob = filter_prob
# dev_list = common.load_jsonl(config.FEVER_DEV)
dev_sent_results_list = common.load_jsonl(sent_file)
dev_fitems, dev_list = get_nli_pair(
tag,
is_training=False,
sent_level_results_list=dev_sent_results_list,
debug=False,
sent_top_k=top_k,
sent_filter_value=dev_sent_filtering_prob)
cur_eval_results_list = common.load_jsonl(label_file)
ema_results_dict = list_dict_data_tool.list_to_dict(
cur_eval_results_list, 'oid')
copied_dev_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_item_from_dict_to_list(copied_dev_list,
ema_results_dict, 'id',
'predicted_label')
mode = {'standard': True}
# delete_unused_evidence(copied_dev_list)
strict_score, acc_score, pr, rec, f1 = fever_scorer.fever_score(
copied_dev_list, dev_list, mode=mode, max_evidence=5)
logging_item = {
'ss': strict_score,
'ac': acc_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
print(logging_item)
def model_perf(dataset_name, task_name, data_file, model_prediction_file):
d_list = common.load_jsonl(data_file)
collected_data_dict = list_dict_data_tool.list_to_dict(d_list,
key_fields='uid')
model_prediction_dict = common.load_json(model_prediction_file)
results_dict, all_correct_set = calculate_divergence_bwt_model_human_simplify(
collected_data_dict, model_prediction_dict, task_name)
print('-' * 60)
print('Data:', dataset_name)
print("All Correct Count:", len(all_correct_set))
print('\t'.join([
'{:20s}'.format('Model Name'), '{:10s}'.format('JSD'),
'{:10s}'.format('KL'), '{:10s}'.format('Old Acc.'),
'{:10s}'.format('New Acc.')
]))
for model_name, model_item in results_dict.items():
print('\t'.join([
'{:20s}'.format(model_name),
'{:10s}'.format(format_number(model_item['average JS div'])),
'{:10s}'.format(format_number(model_item['average KL div'])),
'{:10s}'.format(format_number(model_item['o_acc'])),
'{:10s}'.format(format_number(model_item['m_acc'])),
]))
print('-' * 60)
def eval_model_for_downstream(model_saved_path):
bert_model_name = 'bert-base-uncased'
lazy = True
# lazy = True
forward_size = 64
# batch_size = 64
batch_size = 128
do_lower_case = True
debug_mode = False
max_l = 264
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
tag = 'test'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
# train_ruleterm_doc_results = common.load_jsonl(
# config.PRO_ROOT / "results/doc_retri_results/fever_results/merged_doc_results/m_doc_train.jsonl")
# dev_ruleterm_doc_results = train_ruleterm_doc_results
if tag == 'dev':
dev_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_dev.jsonl"
)
dev_list = common.load_jsonl(config.FEVER_DEV)
dev_fitems = fever_p_level_sampler.get_paragraph_forward_pair(
'dev',
dev_ruleterm_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False)
elif tag == 'train':
dev_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_train.jsonl"
)
dev_list = common.load_jsonl(config.FEVER_TRAIN)
dev_fitems = fever_p_level_sampler.get_paragraph_forward_pair(
'train',
dev_ruleterm_doc_results,
is_training=True,
debug=debug_mode,
ignore_non_verifiable=False)
elif tag == 'test':
dev_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_test.jsonl"
)
dev_list = common.load_jsonl(config.FEVER_TEST)
dev_fitems = fever_p_level_sampler.get_paragraph_forward_pair(
'test',
dev_ruleterm_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False)
else:
raise NotImplemented()
# dev_fitems = fever_p_level_sampler.get_paragraph_forward_pair('train', dev_ruleterm_doc_results,
# is_training=True, debug=debug_mode,
# ignore_non_verifiable=False)
# Just to show the information
fever_p_level_sampler.down_sample_neg(dev_fitems, None)
if debug_mode:
dev_list = dev_list[:100]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, 'id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=max_l,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
model.load_state_dict(torch.load(model_saved_path))
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
if debug_mode:
num_train_optimization_steps = 100
dev_instances = bert_cs_reader.read(dev_fitems)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model,
dev_iter,
device_num,
make_int=True,
with_probs=True,
show_progress=True)
common.save_jsonl(cur_eval_results_list,
f"fever_p_level_{tag}_results.jsonl")
if tag == 'test':
exit(0)
# common.save_jsonl(cur_eval_results_list, "fever_p_level_train_results_1.jsonl")
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
copied_dev_d_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th0_5 = select_top_k_and_to_results_dict(
copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.5)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_5, 'id', 'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list,
dev_list,
max_evidence=5)
score_05 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th0_2 = select_top_k_and_to_results_dict(
copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.2)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_2, 'id', 'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list,
dev_list,
max_evidence=5)
score_02 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th0_1 = select_top_k_and_to_results_dict(
copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.1)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_1, 'id', 'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list,
dev_list,
max_evidence=5)
score_01 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th00_1 = select_top_k_and_to_results_dict(
copied_dev_o_dict, score_field_name='prob', top_k=5, filter_value=0.01)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th00_1, 'id', 'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list,
dev_list,
max_evidence=5)
score_001 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th000_5 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
score_field_name='prob',
top_k=5,
filter_value=0.005)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th000_5, 'id', 'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(copied_dev_d_list,
dev_list,
max_evidence=5)
score_0005 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
logging_item = {
'score_0005': score_0005,
'score_001': score_001,
'score_01': score_01,
'score_02': score_02,
'score_05': score_05,
}
print(json.dumps(logging_item, indent=2))
def eval_model_for_downstream(model_saved_path):
seed = 12
torch.manual_seed(seed)
bert_model_name = 'bert-base-uncased'
# lazy = False
lazy = True
forward_size = 32
# batch_size = 64
batch_size = 128
do_lower_case = True
debug_mode = False
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels')
# Load Dataset
train_list = common.load_json(config.TRAIN_FILE)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" / "hotpot_dev_p_level_unlabeled.jsonl")
train_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" / "hotpot_train_p_level_labeled.jsonl")
test_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" / "hotpot_test_p_level_unlabeled.jsonl")
if debug_mode:
dev_list = dev_list[:10]
dev_fitems_list = dev_fitems_list[:296]
train_fitems_list = train_fitems_list[:300]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
train_o_dict = list_dict_data_tool.list_to_dict(train_list, '_id')
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(bert_tokenizer, lazy, is_paired=True,
example_filter=lambda x: len(x['context']) == 0, max_l=286)
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder, num_labels=num_class, num_of_pooling_layer=1,
act_type='tanh', use_pretrained_pooler=True, use_sigmoid=True)
model.load_state_dict(torch.load(model_saved_path))
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
dev_instances = bert_cs_reader.read(dev_fitems_list)
train_instance = bert_cs_reader.read(train_fitems_list)
test_instances = bert_cs_reader.read(test_fitems_list)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
# train_iter = biterator(train_instance, num_epochs=1, shuffle=False)
# dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
test_iter = biterator(test_instances, num_epochs=1, shuffle=False)
print(len(dev_fitems_list))
print(len(test_fitems_list))
print(len(train_fitems_list))
# cur_dev_eval_results_list = eval_model(model, dev_iter, device_num, with_probs=True, show_progress=True)
# cur_train_eval_results_list = eval_model(model, train_iter, device_num, with_probs=True, show_progress=True)
cur_test_eval_results_list = eval_model(model, test_iter, device_num, with_probs=True, show_progress=True)
common.save_jsonl(cur_test_eval_results_list, "test_p_level_bert_v1_results.jsonl")
print("Test write finished.")
exit(0)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(cur_dev_eval_results_list, copied_dev_o_dict,
'qid', 'fid', check=True)
# Top_3
cur_results_dict_top3 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=3)
upperbound_results_dict_top3 = append_gt_downstream_to_get_upperbound_from_doc_retri(
cur_results_dict_top3,
dev_list)
# Top_5
cur_results_dict_top5 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5)
upperbound_results_dict_top5 = append_gt_downstream_to_get_upperbound_from_doc_retri(
cur_results_dict_top5,
dev_list)
cur_results_dict_top10 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=10)
upperbound_results_dict_top10 = append_gt_downstream_to_get_upperbound_from_doc_retri(
cur_results_dict_top10,
dev_list)
_, metrics_top3 = ext_hotpot_eval.eval(cur_results_dict_top3, dev_list, verbose=False)
_, metrics_top3_UB = ext_hotpot_eval.eval(upperbound_results_dict_top3, dev_list, verbose=False)
_, metrics_top5 = ext_hotpot_eval.eval(cur_results_dict_top5, dev_list, verbose=False)
_, metrics_top5_UB = ext_hotpot_eval.eval(upperbound_results_dict_top5, dev_list, verbose=False)
_, metrics_top10 = ext_hotpot_eval.eval(cur_results_dict_top10, dev_list, verbose=False)
_, metrics_top10_UB = ext_hotpot_eval.eval(upperbound_results_dict_top10, dev_list, verbose=False)
logging_item = {
'top3': metrics_top3,
'top3_UB': metrics_top3_UB,
'top5': metrics_top5,
'top5_UB': metrics_top5_UB,
'top10': metrics_top10,
'top10_UB': metrics_top10_UB,
}
print(logging_item)
common.save_jsonl(cur_train_eval_results_list, "train_p_level_bert_v1_results.jsonl")
common.save_jsonl(cur_dev_eval_results_list, "dev_p_level_bert_v1_results.jsonl")
def model_go():
seed = 12
torch.manual_seed(seed)
# bert_model_name = 'bert-large-uncased'
bert_model_name = 'bert-base-uncased'
experiment_name = 'hotpot_v0_cs'
lazy = False
# lazy = True
forward_size = 16
# batch_size = 64
batch_size = 128
gradient_accumulate_step = int(batch_size / forward_size)
warmup_proportion = 0.1
learning_rate = 5e-5
num_train_epochs = 5
eval_frequency = 5000
pos_ratio = 0.2
do_lower_case = True
debug_mode = False
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels')
# Load Dataset
train_list = common.load_json(config.TRAIN_FILE)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
dev_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" / "hotpot_dev_p_level_unlabeled.jsonl")
train_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" / "hotpot_train_p_level_labeled.jsonl")
if debug_mode:
dev_list = dev_list[:10]
dev_fitems_list = dev_fitems_list[:296]
train_fitems_list = train_fitems_list[:300]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
est_datasize = len(sampled_train_list)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(bert_tokenizer, lazy, is_paired=True,
example_filter=lambda x: len(x['context']) == 0, max_l=286)
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder, num_labels=num_class, num_of_pooling_layer=1,
act_type='tanh', use_pretrained_pooler=True, use_sigmoid=True)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
dev_instances = bert_cs_reader.read(dev_fitems_list)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
# # # Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name), 'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
train_instance = bert_cs_reader.read(sampled_train_list)
train_iter = biterator(train_instance, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter):
model.train()
batch = move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
labels_ids = batch['label']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
s1_span = batch['bert_s1_span']
s2_span = batch['bert_s2_span']
loss = model(paired_sequence, token_type_ids=paired_segments_ids, attention_mask=att_mask,
mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN,
labels=labels_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
print("Update steps:", update_step)
dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model, dev_iter, device_num, with_probs=True)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_dev_o_dict,
'qid', 'fid', check=True)
# Top_5
cur_results_dict_top5 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=5)
upperbound_results_dict_top5 = append_gt_downstream_to_get_upperbound_from_doc_retri(
cur_results_dict_top5,
dev_list)
cur_results_dict_top10 = select_top_k_and_to_results_dict(copied_dev_o_dict, top_k=10)
upperbound_results_dict_top10 = append_gt_downstream_to_get_upperbound_from_doc_retri(
cur_results_dict_top10,
dev_list)
_, metrics_top5 = ext_hotpot_eval.eval(cur_results_dict_top5, dev_list, verbose=False)
_, metrics_top5_UB = ext_hotpot_eval.eval(upperbound_results_dict_top5, dev_list, verbose=False)
_, metrics_top10 = ext_hotpot_eval.eval(cur_results_dict_top10, dev_list, verbose=False)
_, metrics_top10_UB = ext_hotpot_eval.eval(upperbound_results_dict_top10, dev_list, verbose=False)
# top5_doc_f1, top5_UB_sp_f1, top10_doc_f1, top10_Ub_sp_f1
# top5_doc_f1 = metrics_top5['doc_f1']
# top5_UB_sp_f1 = metrics_top5_UB['sp_f1']
# top10_doc_f1 = metrics_top10['doc_f1']
# top10_Ub_sp_f1 = metrics_top10_UB['sp_f1']
top5_doc_recall = metrics_top5['doc_recall']
top5_UB_sp_recall = metrics_top5_UB['sp_recall']
top10_doc_recall = metrics_top10['doc_recall']
top10_Ub_sp_recall = metrics_top10_UB['sp_recall']
logging_item = {
'top5': metrics_top5,
'top5_UB': metrics_top5_UB,
'top10': metrics_top10,
'top10_UB': metrics_top10_UB,
}
# print(logging_item)
save_file_name = f'i({update_step})|e({epoch_i})' \
f'|t5_doc_recall({top5_doc_recall})|t5_sp_recall({top5_UB_sp_recall})' \
f'|t10_doc_recall({top10_doc_recall})|t5_sp_recall({top10_Ub_sp_recall})|seed({seed})'
# print(save_file_name)
logging_agent.incorporate_results({}, save_file_name, logging_item)
logging_agent.logging_to_file(Path(file_path_prefix) / "log.json")
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = Path(file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(), str(output_model_file))
random.shuffle(pos_items)
random.shuffle(neg_items)
neg_sample_count = int(pos_count / ratio)
sampled_neg = neg_items[:neg_sample_count]
print(f"After Sampling, we have {pos_count}/{len(sampled_neg)} (pos/neg).")
sampled_list = sampled_neg + pos_items
random.shuffle(sampled_list)
return sampled_list
if __name__ == '__main__':
d_list = common.load_jsonl(config.FEVER_DEV)
doc_results = common.load_jsonl(
config.RESULT_PATH /
"doc_retri_results/fever_results/merged_doc_results/m_doc_dev.jsonl")
doc_results_dict = list_dict_data_tool.list_to_dict(doc_results, 'id')
fever_db_cursor = fever_db.get_cursor(config.FEVER_DB)
forward_items = build_full_wiki_document_forward_item(
doc_results_dict, d_list, is_training=False, db_cursor=fever_db_cursor)
# print(forward_items)
# for item in forward_items:
# if item['s_labels'] == 'true':
# print(item['query'], item['context'], item['sid'], item['cid'], item['fid'], item['s_labels'])
print(len(forward_items))
# down_sample_neg(forward_items, ratio=0.2)
def model_go(sent_filter_value, sent_top_k=5):
seed = 12
torch.manual_seed(seed)
bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert'
bert_model_name = "bert-base-uncased"
lazy = False
forward_size = 32
batch_size = 32
gradient_accumulate_step = int(batch_size / forward_size)
warmup_rate = 0.1
learning_rate = 5e-5
num_train_epochs = 5
eval_frequency = 1000
do_lower_case = True
debug = False
max_pre_context_length = 320
max_query_length = 64
doc_stride = 128
qa_num_of_layer = 2
do_ema = True
ema_device_num = 1
# s_filter_value = 0.5
s_filter_value = sent_filter_value
# s_top_k = 5
s_top_k = sent_top_k
experiment_name = f'hotpot_v0_qa_(s_top_k:{s_top_k},s_fv:{s_filter_value},qa_layer:{qa_num_of_layer})'
print("Potential total length:",
max_pre_context_length + max_query_length + 3)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case,
cache_dir=bert_pretrain_path)
# Load Dataset.
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
train_list = common.load_json(config.TRAIN_FILE)
dev_sentence_level_results = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_sentence_level/04-19-02:17:11_hotpot_v0_slevel_retri_(doc_top_k:2)/i(12000)|e(2)|v02_f1(0.7153646038858843)|v02_recall(0.7114645831323757)|v05_f1(0.7153646038858843)|v05_recall(0.7114645831323757)|seed(12)/dev_s_level_bert_v1_results.jsonl"
)
train_sentence_level_results = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_sentence_level/04-19-02:17:11_hotpot_v0_slevel_retri_(doc_top_k:2)/i(12000)|e(2)|v02_f1(0.7153646038858843)|v02_recall(0.7114645831323757)|v05_f1(0.7153646038858843)|v05_recall(0.7114645831323757)|seed(12)/train_s_level_bert_v1_results.jsonl"
)
dev_fitem_dict, dev_fitem_list, dev_sp_results_dict = get_qa_item_with_upstream_sentence(
dev_list,
dev_sentence_level_results,
is_training=False,
tokenizer=tokenizer,
max_context_length=max_pre_context_length,
max_query_length=max_query_length,
filter_value=s_filter_value,
doc_stride=doc_stride,
top_k=s_top_k,
debug_mode=debug)
train_fitem_dict, train_fitem_list, _ = get_qa_item_with_upstream_sentence(
train_list,
train_sentence_level_results,
is_training=True,
tokenizer=tokenizer,
max_context_length=max_pre_context_length,
max_query_length=max_query_length,
filter_value=s_filter_value,
doc_stride=doc_stride,
top_k=s_top_k,
debug_mode=debug)
# print(len(dev_fitem_list))
# print(len(dev_fitem_dict))
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
if debug:
dev_list = dev_list[:100]
eval_frequency = 2
est_datasize = len(train_fitem_list)
span_pred_reader = BertPairedSpanPredReader(bert_tokenizer=tokenizer,
lazy=lazy,
example_filter=None)
bert_encoder = BertModel.from_pretrained(bert_model_name,
cache_dir=bert_pretrain_path)
model = BertSpan(bert_encoder, qa_num_of_layer)
ema = None
if do_ema:
ema = EMA(model, model.named_parameters(), device_num=ema_device_num)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
iterator = BasicIterator(batch_size=batch_size)
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
print("Total train instances:", len(train_fitem_list))
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
if debug:
num_train_optimization_steps = 100
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_rate,
t_total=num_train_optimization_steps)
dev_instances = span_pred_reader.read(dev_fitem_list)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
# # # Create Log File
file_path_prefix = None
if not debug:
file_path_prefix, date = save_tool.gen_file_prefix(
f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
print("Resampling:")
train_fitem_dict, train_fitem_list, _ = get_qa_item_with_upstream_sentence(
train_list,
train_sentence_level_results,
is_training=True,
tokenizer=tokenizer,
max_context_length=max_pre_context_length,
max_query_length=max_query_length,
filter_value=s_filter_value,
doc_stride=doc_stride,
top_k=s_top_k,
debug_mode=debug)
random.shuffle(train_fitem_list)
train_instances = span_pred_reader.read(train_fitem_list)
train_iter = iterator(train_instances, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter, desc="Batch Loop"):
model.train()
batch = allen_util.move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
gt_span = batch['gt_span']
loss = model(mode=BertSpan.ForwardMode.TRAIN,
input_ids=paired_sequence,
token_type_ids=paired_segments_ids,
attention_mask=att_mask,
gt_span=gt_span)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
if ema is not None and do_ema:
updated_model = model.module if hasattr(
model, 'module') else model
ema(updated_model.named_parameters())
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
# print("Non-EMA EVAL:")
# eval_iter = iterator(dev_instances, num_epochs=1, shuffle=False)
# cur_eitem_list, cur_eval_dict = span_eval(model, eval_iter, do_lower_case, dev_fitem_dict,
# device_num)
# cur_results_dict = dict()
# cur_results_dict['p_answer'] = cur_eval_dict
# cur_results_dict['sp'] = dev_sp_results_dict
#
# _, metrics = ext_hotpot_eval.eval(cur_results_dict, dev_list, verbose=False)
# # print(metrics)
#
# logging_item = {
# 'score': metrics,
# }
#
# joint_f1 = metrics['joint_f1']
# joint_em = metrics['joint_em']
#
# print(logging_item)
#
# if not debug:
# save_file_name = f'i({update_step})|e({epoch_i})' \
# f'|j_f1({joint_f1})|j_em({joint_em})|seed({seed})'
#
# # print(save_file_name)
# logging_agent.incorporate_results({}, save_file_name, logging_item)
# logging_agent.logging_to_file(Path(file_path_prefix) / "log.json")
#
# model_to_save = model.module if hasattr(model, 'module') else model
# output_model_file = Path(file_path_prefix) / save_file_name
# torch.save(model_to_save.state_dict(), str(output_model_file))
if do_ema and ema is not None:
print("EMA EVAL")
ema_model = ema.get_inference_model()
ema_inference_device_ids = get_ema_gpu_id_list(
master_device_num=ema_device_num)
ema_model = ema_model.to(ema_device_num)
ema_model = torch.nn.DataParallel(
ema_model, device_ids=ema_inference_device_ids)
dev_iter = iterator(dev_instances,
num_epochs=1,
shuffle=False)
cur_eitem_list, cur_eval_dict = span_eval(
ema_model,
dev_iter,
do_lower_case,
dev_fitem_dict,
ema_device_num,
show_progress=False)
cur_results_dict = dict()
cur_results_dict['p_answer'] = cur_eval_dict
cur_results_dict['sp'] = dev_sp_results_dict
_, metrics = ext_hotpot_eval.eval(cur_results_dict,
dev_list,
verbose=False)
print(metrics)
print("---------------" * 3)
logging_item = {
'label': 'ema',
'score': metrics,
}
joint_f1 = metrics['joint_f1']
joint_em = metrics['joint_em']
print(logging_item)
if not debug:
save_file_name = f'ema_i({update_step})|e({epoch_i})' \
f'|j_f1({joint_f1})|j_em({joint_em})|seed({seed})'
# print(save_file_name)
logging_agent.incorporate_results({},
save_file_name,
logging_item)
logging_agent.logging_to_file(
Path(file_path_prefix) / "log.json")
model_to_save = ema_model.module if hasattr(
ema_model, 'module') else ema_model
output_model_file = Path(
file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(),
str(output_model_file))
def fever_retrieval_v0(term_retrieval_top_k=3, match_filtering_k=2, tag='dev'):
# term_retrieval_top_k = 20
# term_retrieval_top_k = 20
# term_retrieval_top_k = 3
# match_filtering_k = 2
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
else:
raise ValueError(f"Tag:{tag} not supported.")
d_tf_idf = common.load_jsonl(
config.RESULT_PATH /
f"doc_retri_results/term_based_methods_results/fever_tf_idf_{tag}.jsonl"
)
tf_idf_dict = list_dict_data_tool.list_to_dict(d_tf_idf, 'id')
r_list = []
ner_set = get_title_entity_set()
g_score_dict = dict()
load_from_file(
g_score_dict, config.PDATA_ROOT /
"reverse_indexing/abs_rindexdb/scored_db/default-tf-idf.score.txt")
keyword_processor = KeywordProcessor(case_sensitive=True)
keyword_processor_disamb = KeywordProcessor(case_sensitive=True)
print("Build Processor")
for kw in tqdm(ner_set):
if filter_word(kw) or filter_document_id(kw):
continue # if the keyword is filtered by above function or is stopwords
else:
# matched_key_word is the original matched span. we need to save it for group ordering.
matched_obj = _MatchedObject(matched_key_word=kw,
matched_keywords_info={kw: 'kwm'})
keyword_processor.add_keyword(kw, matched_obj)
for kw in wiki_util.title_entities_set.disambiguation_group:
if filter_word(kw) or filter_document_id(kw):
continue # if the keyword is filtered by above function or is stopwords
else:
if kw in keyword_processor:
# if the kw existed in the kw_processor, we update its dict to add more disamb items
existing_matched_obj: _MatchedObject = keyword_processor.get_keyword(
kw)
for disamb_kw in wiki_util.title_entities_set.disambiguation_group[
kw]:
if filter_document_id(disamb_kw):
continue
if disamb_kw not in existing_matched_obj.matched_keywords_info:
existing_matched_obj.matched_keywords_info[
disamb_kw] = 'kwm_disamb'
else: # If not we add it to the keyword_processor_disamb, which is set to be lower priority
# new_dict = dict()
matched_obj = _MatchedObject(matched_key_word=kw,
matched_keywords_info=dict())
for disamb_kw in wiki_util.title_entities_set.disambiguation_group[
kw]:
if filter_document_id(disamb_kw):
continue
matched_obj.matched_keywords_info[disamb_kw] = 'kwm_disamb'
# new_dict[disamb_kw] = 'kwm_disamb'
keyword_processor_disamb.add_keyword(kw, matched_obj)
for item in tqdm(d_list):
cur_id = str(item['id'])
query = item['claim']
query_terms = get_query_ngrams(query)
valid_query_terms = [
term for term in query_terms if term in g_score_dict
]
retrieved_set = RetrievedSet()
# print(tf_idf_doc_list)
get_kw_matching_results(query, valid_query_terms, retrieved_set,
match_filtering_k, g_score_dict,
keyword_processor, keyword_processor_disamb)
tf_idf_doc_list = tf_idf_dict[cur_id]['retrieved_list']
added_count = 0
for score, title in sorted(tf_idf_doc_list,
key=lambda x: x[0],
reverse=True)[:term_retrieval_top_k + 3]:
if not filter_word(title) and not filter_document_id(
title) and not title.startswith('List of '):
retrieved_set.add_item(RetrievedItem(title, 'tf-idf'))
added_count += 1
if term_retrieval_top_k is not None and added_count >= term_retrieval_top_k:
break
predicted_docids = retrieved_set.to_id_list()
# print(retrieved_set)
# print(item['claim'], predicted_docids)
r_item = dict()
r_item['id'] = int(cur_id)
r_item['claim'] = item['claim']
r_item['predicted_docids'] = predicted_docids
if tag != 'test':
r_item['label'] = item['label']
r_list.append(r_item)
# r_list = common.load_jsonl('dev-debug.jsonl')
# We need to modify the existing retrieved document for naming consistency
for i, item in enumerate(r_list):
predicted_docids = item['predicted_docids']
modified_docids = []
for docid in predicted_docids:
docid = docid.replace(' ', '_')
docid = reverse_convert_brc(docid)
modified_docids.append(docid)
item['predicted_docids'] = modified_docids
# Modify finished
# print(r_list[0:10])
len_list = []
for rset in r_list:
len_list.append(len(rset['predicted_docids']))
print(collections.Counter(len_list).most_common(10000))
print(np.mean(len_list))
print(np.std(len_list))
print(np.max(len_list))
print(np.min(len_list))
common.save_jsonl(
r_list, f'fever_term_based_retri_results_'
f'{tag}_term_topk:{term_retrieval_top_k}_match_filtering_k:{match_filtering_k}.jsonl'
)
mode = {'standard': False, 'check_doc_id_correct': True}
# fever_scorer.fever_score_analysis(r_list, d_list, mode=mode, max_evidence=None)
fever_scorer.fever_score(r_list, d_list, mode=mode, max_evidence=None)
def merge_results_with_haonao_module(term_retrieval_top_k=3,
match_filtering_k=2,
haonan_topk=10,
tag='dev',
save=False):
if tag == 'dev':
d_list = common.load_jsonl(config.FEVER_DEV)
task_name = 'shared_task_dev'
elif tag == 'train':
d_list = common.load_jsonl(config.FEVER_TRAIN)
task_name = 'train'
elif tag == 'test':
d_list = common.load_jsonl(config.FEVER_TEST)
task_name = 'shared_task_test'
else:
raise ValueError(f"Tag:{tag} not supported.")
# r_list = common.load_jsonl(config.RESULT_PATH / f'doc_retri_results/fever_results/standard_term_based_results/'
# f'fever_term_based_retri_results_{tag}_term_topk:{term_retrieval_top_k}_match_filtering_k:{match_filtering_k}.jsonl')
r_list = common.load_jsonl(
config.RESULT_PATH /
f'doc_retri_results/fever_results/standard_term_based_results/'
f'fever_term_based_retri_results_{tag}_term_topk:{term_retrieval_top_k}_match_filtering_k:{match_filtering_k}.jsonl'
)
old_result_list = common.load_jsonl(
config.RESULT_PATH /
f"doc_retri_results/fever_results/haonans_results/dr_{tag}.jsonl")
item_resorting(old_result_list, top_k=haonan_topk)
old_result_dict = list_dict_data_tool.list_to_dict(old_result_list, 'id')
for i, item in enumerate(r_list):
predicted_docids = item['predicted_docids']
modified_docids = []
for docid in predicted_docids:
docid = docid.replace(' ', '_')
docid = reverse_convert_brc(docid)
modified_docids.append(docid)
item['predicted_docids'] = modified_docids
# item['predicted_docids'] = []
merged_result_list = []
for item in tqdm(r_list):
cur_id = int(item['id'])
old_retrieval_doc = old_result_dict[cur_id]['predicted_docids']
new_retrieval_doc = item['predicted_docids']
m_predicted_docids = set.union(set(old_retrieval_doc),
set(new_retrieval_doc))
# print(m_predicted_docids)
m_predicted_docids = [
docid for docid in m_predicted_docids
if not docid.startswith('List_of_')
]
item['predicted_docids'] = list(m_predicted_docids)
# print(item['predicted_docids'])
mode = {'standard': False, 'check_doc_id_correct': True}
if tag != 'test':
fever_scorer.fever_score_analysis(r_list,
d_list,
mode=mode,
max_evidence=None)
if save:
print("Saved to:")
common.save_jsonl(
r_list, config.RESULT_PATH /
f"doc_retri_results/fever_results/merged_doc_results/m_doc_{tag}.jsonl"
)
# States information.
len_list = []
for rset in r_list:
len_list.append(len(rset['predicted_docids']))
print(collections.Counter(len_list).most_common(10000))
print(np.mean(len_list))
print(np.std(len_list))
print(np.max(len_list))
print(np.min(len_list))
def eval_trainset_for_train_nli(model_path):
tag = 'test'
is_training = False
seed = 12
torch.manual_seed(seed)
bert_model_name = 'bert-base-uncased'
lazy = False
# lazy = True
forward_size = 128
# batch_size = 64
# batch_size = 192
batch_size = 128
do_lower_case = True
debug_mode = False
# debug_mode = True
num_class = 1
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
train_fitems_list = get_sentences(tag,
is_training=is_training,
debug=debug_mode)
est_datasize = len(train_fitems_list)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=128)
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
model.load_state_dict(torch.load(model_path))
print("Estimated training size", est_datasize)
print("Estimated forward steps:", est_datasize / forward_size)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
train_instance = bert_cs_reader.read(train_fitems_list)
train_iter = biterator(train_instance, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model,
train_iter,
device_num,
with_probs=True,
make_int=True,
show_progress=True)
if debug_mode:
train_list = common.load_jsonl(config.FEVER_TRAIN)
train_list = train_list[:50]
set_gt_nli_label(train_list)
train_o_dict = list_dict_data_tool.list_to_dict(train_list, 'id')
copied_dev_o_dict = copy.deepcopy(train_o_dict)
copied_dev_d_list = copy.deepcopy(train_list)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'oid', 'fid', check=True)
print("Threshold 0.5:")
cur_results_dict_th0_5 = select_top_k_and_to_results_dict(
copied_dev_o_dict, top_k=5, threshold=0.1)
list_dict_data_tool.append_item_from_dict_to_list(
copied_dev_d_list, cur_results_dict_th0_5, 'id',
'predicted_evidence')
mode = {'standard': True, 'check_sent_id_correct': True}
strict_score, acc_score, pr, rec, f1 = fever_scorer.fever_score(
copied_dev_d_list, train_list, mode=mode, max_evidence=5)
print(strict_score, acc_score, pr, rec, f1)
common.save_jsonl(cur_eval_results_list,
f'{tag}_sent_results_labeled:{is_training}.jsonl')
def eval_model_for_downstream_ablation(model_saved_path, top_k_doc):
bert_model_name = 'bert-base-uncased'
lazy = True
# lazy = True
forward_size = 128
# batch_size = 64
# batch_size = 128
do_lower_case = True
debug_mode = False
max_l = 128
# est_datasize = 900_000
tag = 'dev'
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
train_upstream_doc_results = common.load_jsonl(
config.PRO_ROOT /
"data/p_fever/fever_paragraph_level/04-22-15:05:45_fever_v0_plevel_retri_(ignore_non_verifiable:True)/"
"i(5000)|e(0)|v02_ofever(0.8947894789478947)|v05_ofever(0.8555355535553555)|seed(12)/fever_p_level_train_results.jsonl"
)
dev_upstream_doc_results = common.load_jsonl(
config.PRO_ROOT /
"data/p_fever/fever_paragraph_level/04-22-15:05:45_fever_v0_plevel_retri_(ignore_non_verifiable:True)/"
"i(5000)|e(0)|v02_ofever(0.8947894789478947)|v05_ofever(0.8555355535553555)|seed(12)/fever_p_level_dev_results.jsonl"
)
test_upstream_doc_results = common.load_jsonl(
config.PRO_ROOT /
"data/p_fever/fever_paragraph_level/04-22-15:05:45_fever_v0_plevel_retri_(ignore_non_verifiable:True)/"
"i(5000)|e(0)|v02_ofever(0.8947894789478947)|v05_ofever(0.8555355535553555)|seed(12)/fever_p_level_test_results.jsonl"
)
train_list = common.load_jsonl(config.FEVER_TRAIN)
dev_list = common.load_jsonl(config.FEVER_DEV)
test_list = common.load_jsonl(config.FEVER_TEST)
# dev_list = common.load_jsonl(config.FEVER_DEV)
if tag == 'dev':
dev_fitems = fever_s_level_sampler.get_sentence_forward_pair(
'dev',
dev_upstream_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False,
top_k=top_k_doc,
filter_value=0.00000)
fever_p_level_sampler.down_sample_neg(dev_fitems, None)
elif tag == 'train':
train_fitems = fever_s_level_sampler.get_sentence_forward_pair(
'train',
train_upstream_doc_results,
is_training=True,
debug=debug_mode,
ignore_non_verifiable=False,
top_k=top_k_doc,
filter_value=0.00000)
fever_p_level_sampler.down_sample_neg(train_fitems, None)
elif tag == 'test':
test_fitems = fever_s_level_sampler.get_sentence_forward_pair(
'test',
test_upstream_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False,
top_k=top_k_doc,
filter_value=0.00000)
fever_p_level_sampler.down_sample_neg(test_fitems, None)
# Just to show the information
if debug_mode:
dev_list = dev_list[:100]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, 'id')
test_o_dict = list_dict_data_tool.list_to_dict(test_list, 'id')
train_o_dict = list_dict_data_tool.list_to_dict(train_list, 'id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=max_l,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
model.load_state_dict(torch.load(model_saved_path))
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
if tag == 'dev':
dev_instances = bert_cs_reader.read(dev_fitems)
dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model,
dev_iter,
device_num,
make_int=True,
with_probs=True,
show_progress=True)
common.save_jsonl(
cur_eval_results_list,
f"fever_s_level_{tag}_results_top_k_doc_{top_k_doc}.jsonl")
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
copied_dev_d_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list, copied_dev_o_dict, 'qid', 'fid', check=True)
cur_results_dict_th0_5 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
score_field_name='prob',
top_k=5,
filter_value=0.2,
result_field='predicted_evidence')
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_5, 'id',
'predicted_evidence')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_sent_only(
copied_dev_d_list, dev_list, max_evidence=5)
score_05 = {
'top_k_doc': top_k_doc,
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
print("Top_k doc:", top_k_doc)
print(score_05)
common.save_json(
score_05,
f"top_k_doc:{top_k_doc}_ss:{strict_score}_pr:{pr}_rec:{rec}_f1:{f1}"
)
elif tag == 'test':
test_instances = bert_cs_reader.read(test_fitems)
test_iter = biterator(test_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model,
test_iter,
device_num,
make_int=True,
with_probs=True,
show_progress=True)
common.save_jsonl(cur_eval_results_list,
f"fever_s_level_{tag}_results.jsonl")
# copied_test_o_dict = copy.deepcopy(test_o_dict)
# copied_test_d_list = copy.deepcopy(test_list)
# list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_test_o_dict,
# 'qid', 'fid', check=True)
#
# cur_results_dict_th0_5 = select_top_k_and_to_results_dict(copied_test_o_dict,
# score_field_name='prob',
# top_k=5, filter_value=0.5,
# result_field='predicted_evidence')
#
# list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_test_d_list,
# cur_results_dict_th0_5,
# 'id', 'predicted_evidence')
# mode = {'standard': False, 'check_doc_id_correct': True}
# copied_train_o_dict = copy.deepcopy(train_o_dict)
# copied_train_d_list = copy.deepcopy(train_list)
# list_dict_data_tool.append_subfield_from_list_to_dict(cur_eval_results_list, copied_train_o_dict,
# 'qid', 'fid', check=True)
#
# cur_results_dict_th0_5 = select_top_k_and_to_results_dict(copied_train_o_dict,
# score_field_name='prob',
# top_k=5, filter_value=0.5,
# result_field='predicted_evidence')
#
# list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(copied_train_d_list,
# cur_results_dict_th0_5,
# 'id', 'predicted_evidence')
# # mode = {'standard': False, 'check_doc_id_correct': True}
# strict_score, pr, rec, f1 = fever_scorer.fever_sent_only(copied_train_d_list, train_list,
# max_evidence=5)
# score_05 = {
# 'ss': strict_score,
# 'pr': pr, 'rec': rec, 'f1': f1,
# }
#
# print(score_05)
elif tag == 'train':
train_instances = bert_cs_reader.read(train_fitems)
train_iter = biterator(train_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model,
train_iter,
device_num,
make_int=True,
with_probs=True,
show_progress=True)
common.save_jsonl(cur_eval_results_list,
f"fever_s_level_{tag}_results.jsonl")
copied_train_o_dict = copy.deepcopy(train_o_dict)
copied_train_d_list = copy.deepcopy(train_list)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list,
copied_train_o_dict,
'qid',
'fid',
check=True)
cur_results_dict_th0_5 = select_top_k_and_to_results_dict(
copied_train_o_dict,
score_field_name='prob',
top_k=5,
filter_value=0.5,
result_field='predicted_evidence')
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_train_d_list, cur_results_dict_th0_5, 'id',
'predicted_evidence')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_sent_only(
copied_train_d_list, train_list, max_evidence=5)
score_05 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
print(score_05)
def multitask_model_go():
seed = 12
torch.manual_seed(seed)
# bert_model_name = 'bert-large-uncased'
bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert'
bert_model_name = 'bert-base-uncased'
lazy = False
# lazy = True
forward_size = 64
# batch_size = 64
batch_size = 128
gradient_accumulate_step = int(batch_size / forward_size)
warmup_proportion = 0.1
learning_rate = 5e-5
num_train_epochs = 1
eval_frequency = 5000
hotpot_pos_ratio = 0.2
do_lower_case = True
max_l = 264
experiment_name = f'mtr_p_level_(num_train_epochs:{num_train_epochs})'
debug_mode = False
do_ema = True
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Hotpot Dataset
hotpot_train_list = common.load_json(config.TRAIN_FILE)
hotpot_dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
hotpot_dev_o_dict = list_dict_data_tool.list_to_dict(
hotpot_dev_list, '_id')
# Load Hotpot upstream paragraph forward item
hotpot_dev_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" /
"hotpot_dev_p_level_unlabeled.jsonl")
hotpot_train_fitems_list = common.load_jsonl(
config.PDATA_ROOT / "content_selection_forward" /
"hotpot_train_p_level_labeled.jsonl")
hotpot_train_fitems_list = hotpot_sampler_utils.field_name_convert(
hotpot_train_fitems_list, 'doc_t', 'element')
hotpot_dev_fitems_list = hotpot_sampler_utils.field_name_convert(
hotpot_dev_fitems_list, 'doc_t', 'element')
# Load FEVER Dataset
# fever_train_list = common.load_json(config.FEVER_TRAIN)
fever_dev_list = common.load_jsonl(config.FEVER_DEV)
fever_dev_o_dict = list_dict_data_tool.list_to_dict(fever_dev_list, 'id')
train_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_train.jsonl"
)
dev_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_dev.jsonl"
)
fever_train_fitems_list = fever_p_level_sampler.get_paragraph_forward_pair(
'train',
train_ruleterm_doc_results,
is_training=True,
debug=debug_mode,
ignore_non_verifiable=True)
fever_dev_fitems_list = fever_p_level_sampler.get_paragraph_forward_pair(
'dev',
dev_ruleterm_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False)
if debug_mode:
hotpot_dev_list = hotpot_dev_list[:10]
hotpot_dev_fitems_list = hotpot_dev_fitems_list[:296]
hotpot_train_fitems_list = hotpot_train_fitems_list[:300]
fever_dev_list = fever_dev_list[:100]
eval_frequency = 2
# Down_sample for hotpot.
hotpot_sampled_train_list = down_sample_neg(hotpot_train_fitems_list,
ratio=hotpot_pos_ratio)
hotpot_est_datasize = len(hotpot_sampled_train_list)
fever_est_datasize = len(fever_train_fitems_list)
print("Hotpot Train Size:", hotpot_est_datasize)
print("Fever Train Size:", fever_est_datasize)
est_datasize = hotpot_est_datasize + fever_est_datasize
bert_tokenizer = BertTokenizer.from_pretrained(
bert_model_name,
do_lower_case=do_lower_case,
cache_dir=bert_pretrain_path)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=max_l,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name,
cache_dir=bert_pretrain_path)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
ema = None
if do_ema:
ema = EMA(model, model.named_parameters(), device_num=1)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
if debug_mode:
num_train_optimization_steps = 100
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
hotpot_dev_instances = bert_cs_reader.read(hotpot_dev_fitems_list)
fever_dev_instances = bert_cs_reader.read(fever_dev_fitems_list)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
file_path_prefix = '.'
if not debug_mode:
# # # Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(
f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
hotpot_sampled_train_list = down_sample_neg(hotpot_train_fitems_list,
ratio=hotpot_pos_ratio)
all_train_data = hotpot_sampled_train_list + fever_train_fitems_list
random.shuffle(all_train_data)
train_instance = bert_cs_reader.read(all_train_data)
train_iter = biterator(train_instance, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter):
model.train()
batch = move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
labels_ids = batch['label']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
s1_span = batch['bert_s1_span']
s2_span = batch['bert_s2_span']
loss = model(
paired_sequence,
token_type_ids=paired_segments_ids,
attention_mask=att_mask,
mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN,
labels=labels_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
if ema is not None and do_ema:
updated_model = model.module if hasattr(
model, 'module') else model
ema(updated_model.named_parameters())
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
print("Update steps:", update_step)
# Eval FEVER
eval_fever_procedure(biterator, fever_dev_instances, model,
device_num, 1, fever_dev_list,
fever_dev_o_dict, debug_mode,
logging_agent, update_step, epoch_i,
file_path_prefix, do_ema, ema, seed)
eval_hotpot_procedure(biterator, hotpot_dev_instances,
model, device_num, 1,
hotpot_dev_list, hotpot_dev_o_dict,
debug_mode, logging_agent,
update_step, epoch_i,
file_path_prefix, do_ema, ema, seed)
if not debug_mode:
print("Final Saving.")
save_file_name = f'i({update_step})|e({num_train_epochs})_final_model'
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = Path(file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(), str(output_model_file))
if do_ema and ema is not None:
print("Final EMA Saving")
ema_model = ema.get_inference_model()
save_file_name = f'i({update_step})|e({num_train_epochs})_final_ema_model'
model_to_save = ema_model.module if hasattr(
ema_model, 'module') else ema_model
output_model_file = Path(file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(), str(output_model_file))
def model_go():
seed = 12
torch.manual_seed(seed)
# bert_model_name = 'bert-large-uncased'
bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert'
bert_model_name = 'bert-base-uncased'
lazy = False
# lazy = True
forward_size = 128
# batch_size = 64
batch_size = 128
gradient_accumulate_step = int(batch_size / forward_size)
warmup_proportion = 0.1
learning_rate = 5e-5
num_train_epochs = 5
eval_frequency = 2000
pos_ratio = 0.2
do_lower_case = True
document_top_k = 2
experiment_name = f'hotpot_v0_slevel_retri_(doc_top_k:{document_top_k})'
debug_mode = False
do_ema = True
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
train_list = common.load_json(config.TRAIN_FILE)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
# train_fitems = sentence_level_sampler.get_train_sentence_pair(document_top_k, True, debug_mode)
# dev_fitems = sentence_level_sampler.get_dev_sentence_pair(document_top_k, False, debug_mode)
# Load train eval results list
cur_train_eval_results_list = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/"
"i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/train_p_level_bert_v1_results.jsonl"
)
cur_dev_eval_results_list = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/"
"i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/dev_p_level_bert_v1_results.jsonl"
)
train_fitems = get_sentence_pair(document_top_k,
train_list,
cur_train_eval_results_list,
is_training=True,
debug_mode=debug_mode)
dev_fitems = get_sentence_pair(document_top_k,
dev_list,
cur_dev_eval_results_list,
is_training=False,
debug_mode=debug_mode)
if debug_mode:
dev_list = dev_list[:100]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
est_datasize = len(train_fitems)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(
bert_model_name,
do_lower_case=do_lower_case,
cache_dir=bert_pretrain_path)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=128,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name,
cache_dir=bert_pretrain_path)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
ema = None
if do_ema:
ema = EMA(model, model.named_parameters(), device_num=1)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
if debug_mode:
num_train_optimization_steps = 100
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
dev_instances = bert_cs_reader.read(dev_fitems)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
# # # Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
random.shuffle(train_fitems)
train_instance = bert_cs_reader.read(train_fitems)
train_iter = biterator(train_instance, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter):
model.train()
batch = move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
labels_ids = batch['label']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
s1_span = batch['bert_s1_span']
s2_span = batch['bert_s2_span']
loss = model(
paired_sequence,
token_type_ids=paired_segments_ids,
attention_mask=att_mask,
mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN,
labels=labels_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
if ema is not None and do_ema:
updated_model = model.module if hasattr(
model, 'module') else model
ema(updated_model.named_parameters())
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
print("Update steps:", update_step)
dev_iter = biterator(dev_instances,
num_epochs=1,
shuffle=False)
cur_eval_results_list = eval_model(model,
dev_iter,
device_num,
with_probs=True)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list,
copied_dev_o_dict,
'qid',
'fid',
check=True)
# 0.5
cur_results_dict_v05 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.5,
result_field='sp')
cur_results_dict_v02 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.2,
result_field='sp')
_, metrics_v5 = ext_hotpot_eval.eval(cur_results_dict_v05,
dev_list,
verbose=False)
_, metrics_v2 = ext_hotpot_eval.eval(cur_results_dict_v02,
dev_list,
verbose=False)
v02_sp_f1 = metrics_v2['sp_f1']
v02_sp_recall = metrics_v2['sp_recall']
v02_sp_prec = metrics_v2['sp_prec']
v05_sp_f1 = metrics_v5['sp_f1']
v05_sp_recall = metrics_v5['sp_recall']
v05_sp_prec = metrics_v5['sp_prec']
logging_item = {
'v02': metrics_v2,
'v05': metrics_v5,
}
print(logging_item)
# print(logging_item)
if not debug_mode:
save_file_name = f'i({update_step})|e({epoch_i})' \
f'|v02_f1({v02_sp_f1})|v02_recall({v02_sp_recall})' \
f'|v05_f1({v05_sp_f1})|v05_recall({v05_sp_recall})|seed({seed})'
# print(save_file_name)
logging_agent.incorporate_results({}, save_file_name,
logging_item)
logging_agent.logging_to_file(
Path(file_path_prefix) / "log.json")
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = Path(
file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(),
str(output_model_file))
if do_ema and ema is not None:
ema_model = ema.get_inference_model()
master_device_num = 1
ema_inference_device_ids = get_ema_gpu_id_list(
master_device_num=master_device_num)
ema_model = ema_model.to(master_device_num)
ema_model = torch.nn.DataParallel(
ema_model, device_ids=ema_inference_device_ids)
dev_iter = biterator(dev_instances,
num_epochs=1,
shuffle=False)
cur_eval_results_list = eval_model(ema_model,
dev_iter,
master_device_num,
with_probs=True)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list,
copied_dev_o_dict,
'qid',
'fid',
check=True)
# 0.5
cur_results_dict_v05 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.5,
result_field='sp')
cur_results_dict_v02 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.2,
result_field='sp')
_, metrics_v5 = ext_hotpot_eval.eval(
cur_results_dict_v05, dev_list, verbose=False)
_, metrics_v2 = ext_hotpot_eval.eval(
cur_results_dict_v02, dev_list, verbose=False)
v02_sp_f1 = metrics_v2['sp_f1']
v02_sp_recall = metrics_v2['sp_recall']
v02_sp_prec = metrics_v2['sp_prec']
v05_sp_f1 = metrics_v5['sp_f1']
v05_sp_recall = metrics_v5['sp_recall']
v05_sp_prec = metrics_v5['sp_prec']
logging_item = {
'label': 'ema',
'v02': metrics_v2,
'v05': metrics_v5,
}
print(logging_item)
if not debug_mode:
save_file_name = f'ema_i({update_step})|e({epoch_i})' \
f'|v02_f1({v02_sp_f1})|v02_recall({v02_sp_recall})' \
f'|v05_f1({v05_sp_f1})|v05_recall({v05_sp_recall})|seed({seed})'
# print(save_file_name)
logging_agent.incorporate_results({},
save_file_name,
logging_item)
logging_agent.logging_to_file(
Path(file_path_prefix) / "log.json")
model_to_save = ema_model.module if hasattr(
ema_model, 'module') else ema_model
output_model_file = Path(
file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(),
str(output_model_file))
def model_go():
seed = 12
torch.manual_seed(seed)
# bert_model_name = 'bert-large-uncased'
bert_model_name = 'bert-base-uncased'
lazy = False
# lazy = True
forward_size = 64
# batch_size = 64
batch_size = 128
gradient_accumulate_step = int(batch_size / forward_size)
warmup_proportion = 0.1
learning_rate = 5e-5
num_train_epochs = 5
eval_frequency = 5000
do_lower_case = True
ignore_non_verifiable = True
experiment_name = f'fever_v0_plevel_retri_(ignore_non_verifiable:{ignore_non_verifiable})'
debug_mode = False
max_l = 264
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
train_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_train.jsonl"
)
dev_ruleterm_doc_results = common.load_jsonl(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/merged_doc_results/m_doc_dev.jsonl"
)
# train_list = common.load_json(config.TRAIN_FILE)
dev_list = common.load_jsonl(config.FEVER_DEV)
train_fitems = fever_p_level_sampler.get_paragraph_forward_pair(
'train',
train_ruleterm_doc_results,
is_training=True,
debug=debug_mode,
ignore_non_verifiable=True)
dev_fitems = fever_p_level_sampler.get_paragraph_forward_pair(
'dev',
dev_ruleterm_doc_results,
is_training=False,
debug=debug_mode,
ignore_non_verifiable=False)
# Just to show the information
fever_p_level_sampler.down_sample_neg(train_fitems, None)
fever_p_level_sampler.down_sample_neg(dev_fitems, None)
if debug_mode:
dev_list = dev_list[:100]
eval_frequency = 2
# print(dev_list[-1]['_id'])
# exit(0)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
est_datasize = len(train_fitems)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, 'id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=max_l,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
if debug_mode:
num_train_optimization_steps = 100
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
dev_instances = bert_cs_reader.read(dev_fitems)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
if not debug_mode:
# # # Create Log File
file_path_prefix, date = save_tool.gen_file_prefix(
f"{experiment_name}")
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
random.shuffle(train_fitems)
train_instance = bert_cs_reader.read(train_fitems)
train_iter = biterator(train_instance, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter):
model.train()
batch = move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
labels_ids = batch['label']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
s1_span = batch['bert_s1_span']
s2_span = batch['bert_s2_span']
loss = model(
paired_sequence,
token_type_ids=paired_segments_ids,
attention_mask=att_mask,
mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN,
labels=labels_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
print("Update steps:", update_step)
dev_iter = biterator(dev_instances,
num_epochs=1,
shuffle=False)
cur_eval_results_list = eval_model(model,
dev_iter,
device_num,
make_int=True,
with_probs=True)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
copied_dev_d_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list,
copied_dev_o_dict,
'qid',
'fid',
check=True)
cur_results_dict_th0_5 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
score_field_name='prob',
top_k=5,
filter_value=0.5)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_5, 'id',
'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(
copied_dev_d_list, dev_list, max_evidence=5)
score_05 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_eval_results_list,
copied_dev_o_dict,
'qid',
'fid',
check=True)
cur_results_dict_th0_2 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
score_field_name='prob',
top_k=5,
filter_value=0.2)
list_dict_data_tool.append_item_from_dict_to_list_hotpot_style(
copied_dev_d_list, cur_results_dict_th0_2, 'id',
'predicted_docids')
# mode = {'standard': False, 'check_doc_id_correct': True}
strict_score, pr, rec, f1 = fever_scorer.fever_doc_only(
copied_dev_d_list, dev_list, max_evidence=5)
score_02 = {
'ss': strict_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
logging_item = {
'score_02': score_02,
'score_05': score_05,
}
print(logging_item)
s02_ss_score = score_02['ss']
s05_ss_score = score_05['ss']
if not debug_mode:
save_file_name = f'i({update_step})|e({epoch_i})' \
f'|v02_ofever({s02_ss_score})' \
f'|v05_ofever({s05_ss_score})|seed({seed})'
# print(save_file_name)
logging_agent.incorporate_results({}, save_file_name,
logging_item)
logging_agent.logging_to_file(
Path(file_path_prefix) / "log.json")
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = Path(
file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(),
str(output_model_file))
def eval_model_for_downstream_ablation(model_saved_path,
doc_top_k=2,
tag='dev'):
print(f"Run doc_top_k:{doc_top_k}")
bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert'
seed = 12
torch.manual_seed(seed)
bert_model_name = 'bert-base-uncased'
# lazy = False
lazy = True
# forward_size = 256
forward_size = 256
# batch_size = 64
batch_size = 128
do_lower_case = True
document_top_k = doc_top_k
debug_mode = False
# est_datasize = 900_000
num_class = 1
# num_train_optimization_steps
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace("false", namespace="labels") # 0
vocab.add_token_to_namespace("true", namespace="labels") # 1
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
train_list = common.load_json(config.TRAIN_FILE)
dev_list = common.load_json(config.DEV_FULLWIKI_FILE)
test_list = common.load_json(config.TEST_FULLWIKI_FILE)
# Load train eval results list
# cur_train_eval_results_list = common.load_jsonl(
# config.PRO_ROOT / "data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/"
# "i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/train_p_level_bert_v1_results.jsonl")
cur_dev_eval_results_list = common.load_jsonl(
config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/"
"i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/dev_p_level_bert_v1_results.jsonl"
)
# cur_test_eval_results_list = common.load_jsonl(
# config.PRO_ROOT / "data/p_hotpotqa/hotpotqa_paragraph_level/04-10-17:44:54_hotpot_v0_cs/"
# "i(40000)|e(4)|t5_doc_recall(0.8793382849426064)|t5_sp_recall(0.879496479212887)|t10_doc_recall(0.888656313301823)|t5_sp_recall(0.8888325134240054)|seed(12)/test_p_level_bert_v1_results.jsonl")
# if tag == 'train':
# train_fitems = get_sentence_pair(document_top_k, train_list, cur_train_eval_results_list, is_training=True,
# debug_mode=debug_mode)
if tag == 'dev':
dev_fitems = get_sentence_pair(document_top_k,
dev_list,
cur_dev_eval_results_list,
is_training=False,
debug_mode=debug_mode)
# elif tag == 'test':
# test_fitems = get_sentence_pair(document_top_k, test_list, cur_test_eval_results_list, is_training=False,
# debug_mode=debug_mode)
if debug_mode:
eval_frequency = 2
# dev_list = dev_list[:10]
# dev_fitems_list = dev_fitems_list[:296]
# train_fitems_list = train_fitems_list[:300]
# print(dev_list[-1]['_id'])
# exit(0)
dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, '_id')
train_o_dict = list_dict_data_tool.list_to_dict(train_list, '_id')
bert_tokenizer = BertTokenizer.from_pretrained(
bert_model_name,
do_lower_case=do_lower_case,
cache_dir=bert_pretrain_path)
bert_cs_reader = BertContentSelectionReader(
bert_tokenizer,
lazy,
is_paired=True,
example_filter=lambda x: len(x['context']) == 0,
max_l=128,
element_fieldname='element')
bert_encoder = BertModel.from_pretrained(bert_model_name,
cache_dir=bert_pretrain_path)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=True)
model.load_state_dict(torch.load(model_saved_path))
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
#
if tag == 'train':
train_instance = bert_cs_reader.read(train_fitems)
elif tag == 'dev':
dev_instances = bert_cs_reader.read(dev_fitems)
elif tag == 'test':
test_instances = bert_cs_reader.read(test_fitems)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
if tag == 'train':
train_iter = biterator(train_instance, num_epochs=1, shuffle=False)
print(len(train_fitems))
elif tag == 'dev':
dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
print(len(dev_fitems))
elif tag == 'test':
test_iter = biterator(test_instances, num_epochs=1, shuffle=False)
print(len(test_fitems))
print("Forward size:", forward_size)
if tag == 'train':
cur_train_eval_results_list_out = eval_model(model,
train_iter,
device_num,
with_probs=True,
show_progress=True)
common.save_jsonl(
cur_train_eval_results_list_out, config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_sentence_level/04-19-02:17:11_hotpot_v0_slevel_retri_(doc_top_k:2)/i(12000)|e(2)|v02_f1(0.7153646038858843)|v02_recall(0.7114645831323757)|v05_f1(0.7153646038858843)|v05_recall(0.7114645831323757)|seed(12)/train_s_level_bert_v1_results.jsonl"
)
elif tag == 'dev':
cur_dev_eval_results_list_out = eval_model(model,
dev_iter,
device_num,
with_probs=True,
show_progress=True)
common.save_jsonl(
cur_dev_eval_results_list_out,
f"hotpot_s_level_{tag}_results_top_k_doc_{document_top_k}.jsonl")
elif tag == 'test':
cur_test_eval_results_list_out = eval_model(model,
test_iter,
device_num,
with_probs=True,
show_progress=True)
common.save_jsonl(
cur_test_eval_results_list_out, config.PRO_ROOT /
"data/p_hotpotqa/hotpotqa_sentence_level/04-19-02:17:11_hotpot_v0_slevel_retri_(doc_top_k:2)/i(12000)|e(2)|v02_f1(0.7153646038858843)|v02_recall(0.7114645831323757)|v05_f1(0.7153646038858843)|v05_recall(0.7114645831323757)|seed(12)/test_s_level_bert_v1_results.jsonl"
)
if tag == 'train' or tag == 'test':
exit(0)
copied_dev_o_dict = copy.deepcopy(dev_o_dict)
list_dict_data_tool.append_subfield_from_list_to_dict(
cur_dev_eval_results_list_out,
copied_dev_o_dict,
'qid',
'fid',
check=True)
# 0.5
cur_results_dict_v05 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.5,
result_field='sp')
cur_results_dict_v02 = select_top_k_and_to_results_dict(
copied_dev_o_dict,
top_k=5,
score_field_name='prob',
filter_value=0.2,
result_field='sp')
_, metrics_v5 = ext_hotpot_eval.eval(cur_results_dict_v05,
dev_list,
verbose=False)
_, metrics_v2 = ext_hotpot_eval.eval(cur_results_dict_v02,
dev_list,
verbose=False)
logging_item = {
'v02': metrics_v2,
'v05': metrics_v5,
}
print(logging_item)
f1 = metrics_v5['sp_f1']
em = metrics_v5['sp_em']
pr = metrics_v5['sp_prec']
rec = metrics_v5['sp_recall']
common.save_json(
logging_item,
f"top_k_doc:{document_top_k}_em:{em}_pr:{pr}_rec:{rec}_f1:{f1}")
def model_go_with_old_data():
seed = 12
torch.manual_seed(seed)
# bert_model_name = 'bert-large-uncased'
bert_model_name = 'bert-base-uncased'
experiment_name = 'fever_v1_nli'
lazy = False
# lazy = True
forward_size = 16
# batch_size = 64
# batch_size = 192
batch_size = 32
gradient_accumulate_step = int(batch_size / forward_size)
warmup_proportion = 0.1
learning_rate = 5e-5
num_train_epochs = 3
eval_frequency = 2000
do_lower_case = True
pair_order = 'cq'
# debug_mode = True
debug_mode = False
# est_datasize = 900_000
num_class = 3
# num_train_optimization_steps
train_sent_filtering_prob = 0.35
dev_sent_filtering_prob = 0.1
# dev_sent_results_file = config.RESULT_PATH / "doc_retri_results/fever_results/sent_results/4-14-sent_results_v0/i(5000)|e(0)|s01(0.9170917091709171)|s05(0.8842384238423843)|seed(12)_dev_sent_results.json"
# train_sent_results_file = config.RESULT_PATH / "doc_retri_results/fever_results/sent_results/4-14-sent_results_v0/train_sent_results.jsonl"
from utest.utest_format_converter_for_old_sent.tool import format_convert
dev_sent_results_file = format_convert(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/sent_results/old_sent_data_by_NSMN/4-15-dev_sent_pred_scores_old_format.jsonl"
)
train_sent_results_file = format_convert(
config.PRO_ROOT /
"results/doc_retri_results/fever_results/sent_results/old_sent_data_by_NSMN/train_sent_scores_old_format.jsonl"
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace('SUPPORTS', namespace='labels')
vocab.add_token_to_namespace('REFUTES', namespace='labels')
vocab.add_token_to_namespace('NOT ENOUGH INFO', namespace='labels')
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden",
-2,
namespace='labels')
# Load Dataset
# train_fitems_list = get_inference_pair('train', True, train_sent_results_file, debug_mode, train_sent_filtering_prob)
dev_debug_num = 2481 if debug_mode else None
dev_fitems_list, dev_list = get_inference_pair('dev', False,
dev_sent_results_file,
dev_debug_num,
dev_sent_filtering_prob)
# = common.load_jsonl(config.FEVER_DEV)
if debug_mode:
dev_list = dev_list[:50]
eval_frequency = 1
# print(dev_list[-1]['_id'])
# exit(0)
# sampled_train_list = down_sample_neg(train_fitems_list, ratio=pos_ratio)
train_debug_num = 2971 if debug_mode else None
train_fitems_list, _ = get_inference_pair('train', True,
train_sent_results_file,
train_debug_num,
train_sent_filtering_prob)
est_datasize = len(train_fitems_list)
# dev_o_dict = list_dict_data_tool.list_to_dict(dev_list, 'id')
# print(dev_o_dict)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name,
do_lower_case=do_lower_case)
bert_cs_reader = BertFeverNLIReader(bert_tokenizer,
lazy,
is_paired=True,
query_l=64,
example_filter=None,
max_l=364,
pair_order=pair_order)
bert_encoder = BertModel.from_pretrained(bert_model_name)
model = BertMultiLayerSeqClassification(bert_encoder,
num_labels=num_class,
num_of_pooling_layer=1,
act_type='tanh',
use_pretrained_pooler=True,
use_sigmoid=False)
#
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
num_train_optimization_steps = int(est_datasize / forward_size / gradient_accumulate_step) * \
num_train_epochs
if debug_mode:
num_train_optimization_steps = 100
print("Estimated training size", est_datasize)
print("Number of optimization steps:", num_train_optimization_steps)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
dev_instances = bert_cs_reader.read(dev_fitems_list)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
forbackward_step = 0
update_step = 0
logging_agent = save_tool.ScoreLogger({})
file_path_prefix = '.'
if not debug_mode:
file_path_prefix, date = save_tool.gen_file_prefix(
f"{experiment_name}")
# # # Create Log File
# Save the source code.
script_name = os.path.basename(__file__)
with open(os.path.join(file_path_prefix, script_name),
'w') as out_f, open(__file__, 'r') as it:
out_f.write(it.read())
out_f.flush()
# # # Log File end
for epoch_i in range(num_train_epochs):
print("Epoch:", epoch_i)
train_fitems_list, _ = get_inference_pair('train', True,
train_sent_results_file,
train_debug_num,
train_sent_filtering_prob)
random.shuffle(train_fitems_list)
train_instance = bert_cs_reader.read(train_fitems_list)
train_iter = biterator(train_instance, num_epochs=1, shuffle=True)
for batch in tqdm(train_iter):
model.train()
batch = move_to_device(batch, device_num)
paired_sequence = batch['paired_sequence']
paired_segments_ids = batch['paired_segments_ids']
labels_ids = batch['label']
att_mask, _ = torch_util.get_length_and_mask(paired_sequence)
s1_span = batch['bert_s1_span']
s2_span = batch['bert_s2_span']
loss = model(
paired_sequence,
token_type_ids=paired_segments_ids,
attention_mask=att_mask,
mode=BertMultiLayerSeqClassification.ForwardMode.TRAIN,
labels=labels_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if gradient_accumulate_step > 1:
loss = loss / gradient_accumulate_step
loss.backward()
forbackward_step += 1
if forbackward_step % gradient_accumulate_step == 0:
optimizer.step()
optimizer.zero_grad()
update_step += 1
if update_step % eval_frequency == 0:
print("Update steps:", update_step)
dev_iter = biterator(dev_instances,
num_epochs=1,
shuffle=False)
cur_eval_results_list = eval_model(model,
dev_iter,
device_num,
with_probs=True,
make_int=True)
results_dict = list_dict_data_tool.list_to_dict(
cur_eval_results_list, 'oid')
copied_dev_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_item_from_dict_to_list(
copied_dev_list, results_dict, 'id', 'predicted_label')
mode = {'standard': True}
strict_score, acc_score, pr, rec, f1 = fever_scorer.fever_score(
copied_dev_list,
dev_fitems_list,
mode=mode,
max_evidence=5)
logging_item = {
'ss': strict_score,
'ac': acc_score,
'pr': pr,
'rec': rec,
'f1': f1,
}
save_file_name = f'i({update_step})|e({epoch_i})' \
f'|ss({strict_score})|ac({acc_score})|pr({pr})|rec({rec})|f1({f1})' \
f'|seed({seed})'
common.save_jsonl(
copied_dev_list,
Path(file_path_prefix) /
f"{save_file_name}_dev_nli_results.json")
# print(save_file_name)
logging_agent.incorporate_results({}, save_file_name,
logging_item)
logging_agent.logging_to_file(
Path(file_path_prefix) / "log.json")
model_to_save = model.module if hasattr(
model, 'module') else model
output_model_file = Path(file_path_prefix) / save_file_name
torch.save(model_to_save.state_dict(),
str(output_model_file))
def model_eval_ablation(model_path, filter_value=0.2, top_k_sent=5):
bert_model_name = 'bert-base-uncased'
bert_pretrain_path = config.PRO_ROOT / '.pytorch_pretrained_bert'
lazy = False
forward_size = 32
do_lower_case = True
pair_order = 'cq'
debug_mode = False
maxout_model = False
num_class = 3
tag = 'dev'
exp = 'no_re_train'
print("Filter value:", filter_value)
print("top_k_sent:", top_k_sent)
train_sent_filtering_prob = 0.2
dev_sent_filtering_prob = filter_value
test_sent_filtering_prob = 0.2
# Data dataset and upstream sentence results.
dev_sent_results_list = common.load_jsonl(
config.PRO_ROOT / "data/p_fever/fever_sentence_level/04-24-00-11-19_fever_v0_slevel_retri_(ignore_non_verifiable-True)/fever_s_level_dev_results.jsonl")
# train_sent_results_list = common.load_jsonl(
# config.PRO_ROOT / "data/p_fever/fever_sentence_level/04-24-00-11-19_fever_v0_slevel_retri_(ignore_non_verifiable-True)/fever_s_level_train_results.jsonl")
test_sent_results_list = common.load_jsonl(
config.PRO_ROOT / "data/p_fever/fever_sentence_level/04-24-00-11-19_fever_v0_slevel_retri_(ignore_non_verifiable-True)/fever_s_level_test_results.jsonl")
dev_fitems, dev_list = get_nli_pair('dev', is_training=False,
sent_level_results_list=dev_sent_results_list, debug=debug_mode,
sent_top_k=top_k_sent, sent_filter_value=dev_sent_filtering_prob)
# train_fitems, train_list = get_nli_pair('train', is_training=True,
# sent_level_results_list=train_sent_results_list, debug=debug_mode,
# sent_top_k=5, sent_filter_value=train_sent_filtering_prob)
test_fitems, test_list = get_nli_pair('test', is_training=False,
sent_level_results_list=test_sent_results_list, debug=debug_mode,
sent_top_k=top_k_sent, sent_filter_value=test_sent_filtering_prob)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device_num = 0 if torch.cuda.is_available() else -1
n_gpu = torch.cuda.device_count()
unk_token_num = {'tokens': 1} # work around for initiating vocabulary.
vocab = ExVocabulary(unk_token_num=unk_token_num)
vocab.add_token_to_namespace('SUPPORTS', namespace='labels')
vocab.add_token_to_namespace('REFUTES', namespace='labels')
vocab.add_token_to_namespace('NOT ENOUGH INFO', namespace='labels')
vocab.add_token_to_namespace("hidden", namespace="labels")
vocab.change_token_with_index_to_namespace("hidden", -2, namespace='labels')
if debug_mode:
dev_list = dev_list[:100]
# train_list = train_list[:100]
test_list = test_list[:100]
eval_frequency = 2
# est_datasize = len(train_fitems)
bert_tokenizer = BertTokenizer.from_pretrained(bert_model_name, do_lower_case=do_lower_case,
cache_dir=bert_pretrain_path)
bert_cs_reader = BertFeverNLIReader(bert_tokenizer, lazy, is_paired=True, query_l=64,
example_filter=None, max_l=384, pair_order=pair_order)
bert_encoder = BertModel.from_pretrained(bert_model_name, cache_dir=bert_pretrain_path)
if not maxout_model:
model = BertMultiLayerSeqClassification(bert_encoder, num_labels=num_class, num_of_pooling_layer=1,
act_type='tanh', use_pretrained_pooler=True, use_sigmoid=False)
else:
model = BertPairMaxOutMatcher(bert_encoder, num_of_class=num_class, act_type="gelu", num_of_out_layers=2)
model.load_state_dict(torch.load(model_path))
dev_instances = bert_cs_reader.read(dev_fitems)
# train_instances = bert_cs_reader.read(train_fitems)
test_instances = bert_cs_reader.read(test_fitems)
biterator = BasicIterator(batch_size=forward_size)
biterator.index_with(vocab)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if tag == 'dev':
dev_iter = biterator(dev_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model, dev_iter, device_num, with_probs=True, make_int=True,
feed_input_span=maxout_model, show_progress=True)
common.save_jsonl(cur_eval_results_list, f"nli_{tag}_label_results_th{dev_sent_filtering_prob}_{exp}.jsonl")
ema_results_dict = list_dict_data_tool.list_to_dict(cur_eval_results_list, 'oid')
copied_dev_list = copy.deepcopy(dev_list)
list_dict_data_tool.append_item_from_dict_to_list(copied_dev_list, ema_results_dict,
'id', 'predicted_label')
common.save_jsonl(copied_dev_list, f"nli_{tag}_cp_results_th{dev_sent_filtering_prob}_{exp}.jsonl")
mode = {'standard': True}
strict_score, acc_score, pr, rec, f1 = fever_scorer.fever_score(copied_dev_list, dev_list,
mode=mode, max_evidence=5)
logging_item = {
'ss': strict_score, 'ac': acc_score,
'pr': pr, 'rec': rec, 'f1': f1,
}
print(logging_item)
common.save_json(logging_item,
f"nli_th{dev_sent_filtering_prob}_{exp}_ss:{strict_score}_ac:{acc_score}_pr:{pr}_rec:{rec}_f1:{f1}.jsonl")
elif tag == 'test':
test_iter = biterator(test_instances, num_epochs=1, shuffle=False)
cur_eval_results_list = eval_model(model, test_iter, device_num, with_probs=True, make_int=True,
feed_input_span=maxout_model, show_progress=True)
common.save_jsonl(cur_eval_results_list, f"nli_{tag}_label_results_th{test_sent_filtering_prob}.jsonl")
ema_results_dict = list_dict_data_tool.list_to_dict(cur_eval_results_list, 'oid')
copied_test_list = copy.deepcopy(test_list)
list_dict_data_tool.append_item_from_dict_to_list(copied_test_list, ema_results_dict,
'id', 'predicted_label')
common.save_jsonl(copied_test_list, f"nli_{tag}_cp_results_th{test_sent_filtering_prob}.jsonl")
