def main(_):
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
train_config = TrainConfigEx.from_flags(FLAGS)
model_config = JsonConfig.from_json_file(FLAGS.model_config_file)
show_input_files(input_files)
model_fn = loss_diff_prediction_model(
bert_config=bert_config,
train_config=train_config,
model_class=BertModel,
model_config=model_config,
)
if FLAGS.do_train:
input_fn = input_fn_builder_masked(
input_files=input_files,
flags=FLAGS,
is_training=True)
elif FLAGS.do_eval or FLAGS.do_predict:
input_fn = input_fn_builder_masked(
input_files=input_files,
flags=FLAGS,
is_training=False)
else:
raise Exception()
run_estimator(model_fn, input_fn)
def main(_):
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_files = flags_wrapper.get_input_files()
input_fn = input_fn_builder_unmasked(input_files, FLAGS, False, True)
model_fn = model_fn_share_fetch_grad(config, train_config, SharingFetchGradModel)
run_estimator(model_fn, input_fn)
def main(_):
is_training = FLAGS.do_train
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
input_fn = input_fn_builder_unmasked(input_files, FLAGS, is_training)
model_fn = model_fn_builder()
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Train nli_lm_shared")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
is_training = FLAGS.do_train
input_files = flags_wrapper.get_input_files()
input_fn = input_fn_builder(input_files, FLAGS, is_training, True)
model_fn = model_fn_nli_lm(config, train_config, SimpleSharingModel)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Train MLM")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
is_training = FLAGS.do_train
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
input_fn = input_fn_builder_masked2(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, BertModel)
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.setLevel(logging.INFO)
if FLAGS.log_debug:
tf_logging.setLevel(logging.DEBUG)
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
is_training = FLAGS.do_train
input_fn = input_fn_builder(get_input_files_from_flags(FLAGS), FLAGS,
is_training)
model_fn = mask_lm_as_seq2seq(config, train_config)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Train MLM with BERT like")
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_unmasked_alt_emb2(input_files, FLAGS,
is_training)
model_fn = model_fn_lm(config, train_config, BertModel,
get_masked_lm_output)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("text pair ranking")
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_fn = input_fn_perspective_passage(FLAGS)
model_fn = text_pair_ranking_pairwise_model(config, train_config, BertModel, "")
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(",")
special_flags.append("feed_features")
is_training = FLAGS.do_train
if FLAGS.do_train or FLAGS.do_eval:
model_fn = model_fn_with_loss(
config,
train_config,
MES_hinge,
)
input_fn = input_fn_builder_pairwise(FLAGS.max_d_seq_length, FLAGS)
else:
model_fn = model_fn_with_loss(
config,
train_config,
MES_pred,
)
input_fn = input_fn_builder_classification_w_data_id2(
input_files,
FLAGS.max_seq_length,
FLAGS,
is_training,
num_cpu_threads=4)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
result = run_estimator(model_fn, input_fn)
return result
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(_):
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.io.gfile.glob(input_pattern))
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
ssdr_config = JsonConfig.from_json_file(FLAGS.model_config_file)
model_fn = model_fn_apr_classification(
bert_config=bert_config,
ssdr_config=ssdr_config,
train_config=train_config,
dict_run_config=DictRunConfig.from_flags(FLAGS),
)
if FLAGS.do_train:
input_fn = input_fn_builder(input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
is_training=True)
elif FLAGS.do_eval or FLAGS.do_predict:
input_fn = input_fn_builder(input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
is_training=False)
else:
raise Exception()
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Run generative predictor")
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_generative_predictor(config, train_config)
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(_):
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_files = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder(input_files, FLAGS, False)
model_fn = model_fn_lm(bert_config, train_config, BertModel)
return run_estimator(model_fn, input_fn)
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_inner():
train_config = TrainConfigEx.from_flags(FLAGS)
model_fn = model_fn_classification(
train_config=train_config,
)
input_fn = input_fn_from_flags(input_fn_builder, FLAGS)
r = run_estimator(model_fn, input_fn)
return r
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(_):
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("label_as_token")
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_unmasked(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, BertModel)
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(_):
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, BertModel)
r = run_estimator(model_fn, input_fn)
return r
def main(_):
tf_logging.info("Train topic_vector")
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_topic_fn(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, TopicVectorBert.factory,
get_masked_lm_output, True)
return run_estimator(model_fn, input_fn)
def main(_):
config = JsonConfig.from_json_file(FLAGS.model_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_file = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder_classification_w_data_id(input_file,
FLAGS,
FLAGS.do_train)
model_fn = model_fn_pooling_long_things(config, train_config, BertModel)
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)
is_training = FLAGS.do_train
input_files = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder_unmasked(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, Albert.factory,
get_masked_lm_output_albert)
return run_estimator(model_fn, input_fn)
def main(_):
set_level_debug()
tf_logging.info("Train horizon")
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_unmasked(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, BertologyFactory(HorizontalAlpha), get_masked_lm_output_albert)
return run_estimator(model_fn, input_fn)
def main(_):
set_level_debug()
tf_logging.info("Train reshape bert")
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_unmasked(input_files, FLAGS, is_training)
model_fn = model_fn_lm(config, train_config, ReshapeBertModel)
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Classification with alt loss")
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_with_alt_loss(config, train_config, BertModel)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Train MLM with alternative embedding")
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_unmasked_alt_emb(input_files, FLAGS,
is_training)
def model_constructor(config, is_training, input_ids, input_mask,
token_type_ids, use_one_hot_embeddings, features):
return EmbeddingReplacer(config, is_training, input_ids, input_mask,
token_type_ids, use_one_hot_embeddings,
features)
model_fn = model_fn_lm(config, train_config, model_constructor,
get_masked_lm_output, True)
run_estimator(model_fn, input_fn)
def main(_):
tf_logging.info("Classification with confidence")
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)
model_fn = model_fn_classification_with_confidence(config, train_config)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
return run_estimator(model_fn, input_fn)
