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)