def main(_):
tf_logging.info("TripleBertMasking")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_cppnc_triple(FLAGS)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
def override_prediction_fn(predictions, model):
for key, value in model.get_predictions().items():
predictions[key] = value
return predictions
if FLAGS.modeling == "TripleBertMasking":
model_class = TripleBertMasking
elif FLAGS.modeling == "TripleBertWeighted":
model_class = TripleBertWeighted
else:
assert False
model_fn = model_fn_classification(config, train_config, model_class,
special_flags, override_prediction_fn)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Train albert")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_from_flags(input_fn_builder_classification, FLAGS)
model_fn = model_fn_classification(config, train_config, Albert.factory)
return run_estimator(model_fn, input_fn)
def main(_):
input_files = get_input_files_from_flags(FLAGS)
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
config,
train_config,
BertModel,
special_flags
)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
is_training = FLAGS.do_train
if FLAGS.do_train or FLAGS.do_eval:
input_fn = input_fn_builder_classification(input_files, FLAGS.max_seq_length, is_training, FLAGS,
num_cpu_threads=4,
repeat_for_eval=False)
else:
input_fn = input_fn_builder_classification_w_data_id2(
input_files,
FLAGS.max_seq_length,
FLAGS,
is_training,
num_cpu_threads=4)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
tf_logging.info("Train horizon classification")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_from_flags(input_fn_builder_classification, FLAGS)
model_fn = model_fn_classification(config, train_config,
BertologyFactory(HorizontalAlpha))
return run_estimator(model_fn, input_fn)
def main(_):
config = JsonConfig.from_json_file(FLAGS.bert_config_file)
sero_config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_from_flags(input_fn_builder_classification, FLAGS)
model_fn = model_fn_classification(config, train_config,
partial(DualSeroBertModel, sero_config),
FLAGS.special_flags.split(","))
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Run MSMarco")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
is_training = FLAGS.do_train
input_files = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder(input_files, FLAGS.max_seq_length, is_training)
model_fn = model_fn_classification(config, train_config, BertModel)
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("DualBertTwoInputModelEx")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_two_inputs_w_data_id(FLAGS)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
model_fn = model_fn_classification(config, train_config, DualBertTwoInputModelEx, special_flags)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Run NLI with BERT but with file that contain alt_emb_ids")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
is_training = FLAGS.do_train
input_files = get_input_files_from_flags(FLAGS)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_alt_emb2_classification(
input_files, FLAGS, is_training)
model_fn = model_fn_classification(config, train_config, BertModel)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("QCK with ME7")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_cppnc_multi_evidence(FLAGS)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
model_fn = model_fn_classification(config, train_config, ME7,
special_flags)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("ThreeInput QCK")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_dual_bert_double_length_input(FLAGS)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
model_fn = model_fn_classification(config, train_config,
DualBertTwoInputWithDoubleInputLength,
special_flags)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def run_classification_w_second_input():
input_files = get_input_files_from_flags(FLAGS)
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
model_fn = model_fn_classification(config, train_config, ME5_2,
special_flags)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
input_fn = input_fn_builder_cppnc_multi_evidence(FLAGS)
result = run_estimator(model_fn, input_fn)
return result
def run_classification_w_second_input():
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=BertModel,
special_flags=special_flags,
)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
input_fn = input_fn_builder_use_second_input(FLAGS)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=FreezeEmbedding,
special_flags=special_flags,
)
input_fn = input_fn_builder_classification_w_data_id(
input_files=input_files, flags=FLAGS, is_training=FLAGS.do_train)
result = run_estimator(model_fn, input_fn)
return result
def run_w_data_id():
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=BertModel,
special_flags=special_flags,
)
if FLAGS.do_predict:
tf_logging.addFilter(CounterFilter())
input_fn = input_fn_builder_classification_w_data_ids_typo(
input_files=input_files, flags=FLAGS, is_training=FLAGS.do_train)
result = run_estimator(model_fn, input_fn)
return result
def main_inner(model_class=None):
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
if model_class is None:
model_class = BertModel
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=model_class,
special_flags=special_flags,
)
input_fn = input_fn_from_flags(input_fn_builder, FLAGS)
r = run_estimator(model_fn, input_fn)
return r
def main(_):
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
def override_prediction_fn(predictions, model):
predictions['vector'] = model.get_output()
return predictions
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=MultiEvidenceUseFirst,
special_flags=special_flags,
override_prediction_fn=override_prediction_fn)
if FLAGS.do_predict:
tf_logging.addFilter(CounterFilter())
input_fn = input_fn_builder_use_second_input(FLAGS)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
tf_logging.info("Train MLM with alternative embedding2")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
is_training = FLAGS.do_train
input_files = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder_alt_emb2_classification(
input_files, FLAGS, is_training)
def model_constructor(config, is_training, input_ids, input_mask,
token_type_ids, use_one_hot_embeddings, features):
return EmbeddingReplacer2(config, is_training, input_ids, input_mask,
token_type_ids, use_one_hot_embeddings,
features)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
model_fn = model_fn_classification(config, train_config, model_constructor,
special_flags)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("DualBertTwoInputModel simple prediction")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_builder_two_inputs_w_data_id(FLAGS)
special_flags = FLAGS.special_flags.split(",")
special_flags.append("feed_features")
def override_prediction_fn(predictions, model):
predictions.pop('input_ids', None)
try:
predictions.pop('input_ids2', None)
except KeyError:
pass
return predictions
model_fn = model_fn_classification(config, train_config,
DualBertTwoInputModel, special_flags,
override_prediction_fn)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def run_classification_task(model_class):
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_from_flags(input_fn_builder_classification, FLAGS)
model_fn = model_fn_classification(config, train_config, model_class, FLAGS.special_flags.split(","))
return run_estimator(model_fn, input_fn)
