def setUp(self): super(BertExampleTest, self).setUp() vocab_tokens = [ '[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e', "This", "is", "test", ".", "Test", "1", "2" ] vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt') with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer: vocab_writer.write(''.join([x + '\n' for x in vocab_tokens])) label_map = {'KEEP': 1, 'DELETE': 2} max_seq_length = 8 do_lower_case = False converter = tagging_converter.TaggingConverter([]) self._builder = bert_example.BertExampleBuilder(label_map, vocab_file, max_seq_length, do_lower_case, converter, "Normal") self._builder = bert_example.BertExampleBuilder(label_map, vocab_file, max_seq_length, do_lower_case, converter, "Normal") self._pos_builder = bert_example.BertExampleBuilder( label_map, vocab_file, max_seq_length, do_lower_case, converter, "POS") self._sentence_builder = bert_example.BertExampleBuilder( label_map, vocab_file, max_seq_length, do_lower_case, converter, "Sentence") self._label_map = label_map self._vocab_file = vocab_file
def test_first_deletion_idx_computation(self): converter = tagging_converter.TaggingConverter([]) tag_strs = ['KEEP', 'DELETE', 'DELETE', 'KEEP'] tags = [tagging.Tag(s) for s in tag_strs] source_token_idx = 3 idx = converter._find_first_deletion_idx(source_token_idx, tags) self.assertEqual(idx, 1)
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) num_predicted = 0 with tf.gfile.Open(FLAGS.output_file, 'w') as writer: for i, (sources, target) in enumerate(utils.yield_sources_and_targets( FLAGS.input_file, FLAGS.input_format)): logging.log_every_n( logging.INFO, f'{i} examples processed, {num_predicted} converted to tf.Example.', 100) prediction = predictor.predict(sources) writer.write(f'{" ".join(sources)}\t{prediction}\t{target}\n') num_predicted += 1 logging.info(f'{num_predicted} predictions saved to:\n{FLAGS.output_file}')
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_tfrecord') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) num_converted = 0 with tf.io.TFRecordWriter(FLAGS.output_tfrecord) as writer: for i, (sources, target) in enumerate(utils.yield_sources_and_targets( FLAGS.input_file, FLAGS.input_format)): logging.log_every_n( logging.INFO, f'{i} examples processed, {num_converted} converted to tf.Example.', 10000) example = builder.build_bert_example( sources, target, FLAGS.output_arbitrary_targets_for_infeasible_examples) if example is None: continue writer.write(example.to_tf_example().SerializeToString()) num_converted += 1 logging.info(f'Done. {num_converted} examples converted to tf.Example.') count_fname = _write_example_count(num_converted) logging.info(f'Wrote:\n{FLAGS.output_tfrecord}\n{count_fname}')
def test_construct_example(self): vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS/vocab.txt" label_map_file = "gs://publicly_available_models_yechen/best_hypertuned_POS/label_map.txt" enable_masking = False do_lower_case = True embedding_type = "POS" label_map = utils.read_label_map(label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), True) id_2_tag = { tag_id: tagging.Tag(tag) for tag, tag_id in label_map.items() } builder = bert_example.BertExampleBuilder(label_map, vocab_file, 10, do_lower_case, converter, embedding_type, enable_masking) inputs, example = construct_example("This is a test", builder) self.assertEqual( inputs, { 'input_ids': [2, 12, 1016, 6, 9, 6, 9, 10, 12, 3], 'input_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'segment_ids': [2, 16, 14, 14, 32, 14, 32, 5, 14, 41] })
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) sources_list = [] target_list = [] with tf.gfile.GFile(FLAGS.input_file) as f: for line in f: sources, target, lcs_rate = line.rstrip('\n').split('\t') sources_list.append([sources]) target_list.append(target) number = len(sources_list) # 总样本数 predict_batch_size = min(64, number) batch_num = math.ceil(float(number) / predict_batch_size) start_time = time.time() num_predicted = 0 with tf.gfile.Open(FLAGS.output_file, 'w') as writer: writer.write(f'source\tprediction\ttarget\n') for batch_id in range(batch_num): sources_batch = sources_list[batch_id * predict_batch_size:(batch_id + 1) * predict_batch_size] prediction_batch = predictor.predict_batch( sources_batch=sources_batch) assert len(prediction_batch) == len(sources_batch) num_predicted += len(prediction_batch) for id, [prediction, sources] in enumerate(zip(prediction_batch, sources_batch)): target = target_list[batch_id * predict_batch_size + id] writer.write(f'{"".join(sources)}\t{prediction}\t{target}\n') if batch_id % 20 == 0: cost_time = (time.time() - start_time) / 60.0 print( "%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin." % (curLine(), batch_id + 1, batch_num, num_predicted, number, cost_time)) cost_time = (time.time() - start_time) / 60.0 logging.info( f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted} min.' )
def test_no_match(self): input_texts = ['Turing was born in 1912 .', 'Turing died in 1954 .'] target = 'Turing was born in 1912 and died in 1954 .' task = tagging.EditingTask(input_texts) phrase_vocabulary = ['but'] converter = tagging_converter.TaggingConverter(phrase_vocabulary) tags = converter.compute_tags(task, target) # Vocabulary doesn't contain "and" so the inputs can't be converted to the # target. self.assertFalse(tags)
def main_sentence(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') # flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) print("FLAGS.vocab_file", FLAGS.vocab_file) print("FLAGS.max_seq_length", FLAGS.max_seq_length) print("FLAGS.do_lower_case", FLAGS.do_lower_case) print("converter", converter) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) # print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) # sources_list = [] # target_list = [] # with tf.io.gfile.GFile(FLAGS.input_file) as f: # for line in f: # sources = line.rstrip('\n') # sources_list.append([sources]) # # target_list.append(target) while True: sentence = input(">> ") batch_num = 1 start_time = time.time() num_predicted = 0 for batch_id in range(batch_num): # sources_batch = sources_list[batch_id * predict_batch_size: (batch_id + 1) * predict_batch_size] sources_batch = [sentence] prediction_batch = predictor.predict_batch( sources_batch=sources_batch) assert len(prediction_batch) == len(sources_batch) num_predicted += len(prediction_batch) for id, [prediction, sources] in enumerate(zip(prediction_batch, sources_batch)): # target = target_list[batch_id * predict_batch_size + id] print("原句sources: %s 拓展句predict: %s" % (sentence, prediction)) # cost_time = (time.time() - start_time) / 60.0 print("耗时", (time.time() - start_time) / 60.0, "s")
def setUp(self): super(BertExampleTest, self).setUp() vocab_tokens = ['[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e'] vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt') with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer: vocab_writer.write(''.join([x + '\n' for x in vocab_tokens])) max_seq_length = 8 do_lower_case = False converter = tagging_converter.TaggingConverter([]) self._builder = bert_example.BertExampleBuilder(vocab_file, max_seq_length, do_lower_case)
def setUp(self): super(PredictUtilsTest, self).setUp() vocab_tokens = ['[CLS]', '[SEP]', '[PAD]', 'a', 'b', 'c', '##d', '##e'] vocab_file = os.path.join(FLAGS.test_tmpdir, 'vocab.txt') with tf.io.gfile.GFile(vocab_file, 'w') as vocab_writer: vocab_writer.write(''.join([x + '\n' for x in vocab_tokens])) self._label_map = {'KEEP': 0, 'DELETE': 1, 'KEEP|and': 2} max_seq_length = 8 do_lower_case = False converter = tagging_converter.TaggingConverter([]) self._builder = bert_example.BertExampleBuilder( self._label_map, vocab_file, max_seq_length, do_lower_case, converter)
def __init__(self): # if len(argv) > 1: # raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) self.predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map)
def test_invalid_embedding_type(self): with self.assertRaises(ValueError): # The embedding type is wrong, and return raise ValueError invalid_builder = bert_example.BertExampleBuilder( self._label_map, self._vocab_file, 8, True, tagging_converter.TaggingConverter([]), "Wrong Type")
def test_matching_conversion(self, input_texts, target, phrase_vocabulary, target_tags): task = tagging.EditingTask(input_texts) converter = tagging_converter.TaggingConverter(phrase_vocabulary) tags = converter.compute_tags(task, target) self.assertEqual(tags_to_str(tags), tags_to_str(target_tags))
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) sourcesA_list = [] sourcesB_list = [] target_list = [] with tf.gfile.GFile(FLAGS.input_file) as f: for line in f: sourceA, sourceB, label = line.rstrip('\n').split('\t') sourcesA_list.append([sourceA.strip(".")]) sourcesB_list.append([sourceB.strip(".")]) target_list.append(label) number = len(sourcesA_list) # 总样本数 predict_batch_size = min(32, number) batch_num = math.ceil(float(number) / predict_batch_size) start_time = time.time() num_predicted = 0 prediction_list = [] with tf.gfile.Open(FLAGS.output_file, 'w') as writer: for batch_id in range(batch_num): sources_batch = sourcesA_list[batch_id * predict_batch_size:(batch_id + 1) * predict_batch_size] batch_b = sourcesB_list[batch_id * predict_batch_size:(batch_id + 1) * predict_batch_size] location_batch = [] sources_batch.extend(batch_b) for source in sources_batch: location = list() for char in source[0]: if (char >= '0' and char <= '9') or char in '.- ' or ( char >= 'a' and char <= 'z') or (char >= 'A' and char <= 'Z'): location.append("1") # TODO TODO else: location.append("0") location_batch.append("".join(location)) prediction_batch = predictor.predict_batch( sources_batch=sources_batch, location_batch=location_batch) current_batch_size = int(len(sources_batch) / 2) assert len(prediction_batch) == current_batch_size * 2 for id in range(0, current_batch_size): target = target_list[num_predicted + id] prediction_A = prediction_batch[id] prediction_B = prediction_batch[current_batch_size + id] sourceA = "".join(sources_batch[id]) sourceB = "".join(sources_batch[current_batch_size + id]) if prediction_A == prediction_B: # 其中一个换为source lcsA = len(_compute_lcs(sourceA, prediction_A)) if lcsA < 8: # A的变化大 prediction_B = sourceB else: lcsB = len(_compute_lcs(sourceB, prediction_B)) if lcsA <= lcsB: # A的变化大 prediction_B = sourceB else: prediction_A = sourceA print(curLine(), batch_id, prediction_A, prediction_B, "target:", target, "current_batch_size=", current_batch_size, "lcsA=%d,lcsB=%d" % (lcsA, lcsB)) writer.write(f'{prediction_A}\t{prediction_B}\t{target}\n') prediction_list.append("%s\t%s\n" % (sourceA, prediction_A)) # print(curLine(), id,"sourceA:", sourceA, "sourceB:",sourceB, "target:", target) prediction_list.append("%s\t%s\n" % (sourceB, prediction_B)) num_predicted += current_batch_size if batch_id % 20 == 0: cost_time = (time.time() - start_time) / 60.0 print(curLine(), id, prediction_A, prediction_B, "target:", target, "current_batch_size=", current_batch_size) print(curLine(), id, "sourceA:", sourceA, "sourceB:", sourceB, "target:", target) print( "%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin." % (curLine(), batch_id + 1, batch_num, num_predicted, number, cost_time)) with open("prediction.txt", "w") as prediction_file: prediction_file.writelines(prediction_list) print(curLine(), "save to prediction_qa.txt.") cost_time = (time.time() - start_time) / 60.0 print(curLine(), id, prediction_A, prediction_B, "target:", target, "current_batch_size=", current_batch_size) print(curLine(), id, "sourceA:", sourceA, "sourceB:", sourceB, "target:", target) logging.info( f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted*60000}ms.' )
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) num_predicted = 0 sources_list = [] location_list = [] corpus_id_list = [] entity_list = [] domainname_list = [] intentname_list = [] context_list = [] template_id_list = [] with open(FLAGS.input_file, "r") as f: corpus_json_list = json.load(f) # corpus_json_list = corpus_json_list[:100] for corpus_json in corpus_json_list: sources_list.append([corpus_json["oriText"]]) location_list.append(corpus_json["location"]) corpus_id_list.append(corpus_json["corpus_id"]) entity_list.append(corpus_json["entity"]) domainname_list.append(corpus_json["domainname"]) intentname_list.append(corpus_json["intentname"]) context_list.append(corpus_json["context"]) template_id_list.append(corpus_json["template_id"]) number = len(sources_list) # 总样本数 predict_batch_size = min(64, number) batch_num = math.ceil(float(number) / predict_batch_size) start_time = time.time() index = 0 for batch_id in range(batch_num): sources_batch = sources_list[batch_id * predict_batch_size:(batch_id + 1) * predict_batch_size] location_batch = location_list[batch_id * predict_batch_size:(batch_id + 1) * predict_batch_size] prediction_batch = predictor.predict_batch( sources_batch=sources_batch, location_batch=location_batch) assert len(prediction_batch) == len(sources_batch) num_predicted += len(prediction_batch) for id, [prediction, sources] in enumerate(zip(prediction_batch, sources_batch)): index = batch_id * predict_batch_size + id output_json = { "corpus_id": corpus_id_list[index], "oriText": prediction, "sources": sources[0], "entity": entity_list[index], "location": location_list[index], "domainname": domainname_list[index], "intentname": intentname_list[index], "context": context_list[index], "template_id": template_id_list[index] } corpus_json_list[index] = output_json if batch_id % 20 == 0: cost_time = (time.time() - start_time) / 60.0 print("%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin." % (curLine(), batch_id + 1, batch_num, num_predicted, number, cost_time)) assert len(corpus_json_list) == index + 1 with open(FLAGS.output_file, 'w', encoding='utf-8') as writer: json.dump(corpus_json_list, writer, ensure_ascii=False, indent=4) cost_time = (time.time() - start_time) / 60.0 logging.info( f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time/num_predicted} min.' )
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) predict_batch_size = 64 batch_num = 0 num_predicted = 0 with tf.gfile.Open(FLAGS.output_file, 'w') as writer: with open(FLAGS.input_file, "r") as f: sources_batch = [] previous_line_list = [] context_list = [] line_number = 0 start_time = time.time() while True: line_number += 1 line = f.readline().rstrip('\n').strip("\"").strip(" ") if len(line) == 0: break column_index = line.index(",") text = line[column_index + 1:].strip("\"") # context and query # for charChinese_id, char in enumerate(line[column_index+1:]): # if (char>='a' and char<='z') or (char>='A' and char<='Z'): # continue # else: # break source = remove_p(text) if source not in text: # TODO ignore的就给空字符串,这样输出也是空字符串 print(curLine(), "line_number=%d, ignore:%s" % (line_number, text), ",source:", len(source), source) source = "" # continue context_list.append(text[:text.index(source)]) previous_line_list.append(line) sources_batch.append(source) if len(sources_batch) == predict_batch_size: num_predicted, batch_num = predict_and_write( predictor, sources_batch, previous_line_list, context_list, writer, num_predicted, start_time, batch_num) sources_batch = [] previous_line_list = [] context_list = [] # if num_predicted > 1000: # break if len(context_list) > 0: num_predicted, batch_num = predict_and_write( predictor, sources_batch, previous_line_list, context_list, writer, num_predicted, start_time, batch_num) cost_time = (time.time() - start_time) / 60.0 logging.info( f'{curLine()} {num_predicted} predictions saved to:{FLAGS.output_file}, cost {cost_time} min, ave {cost_time / num_predicted/60} hours.' )
def main(argv): if len(argv) > 1: raise app.UsageError('Too many command-line arguments.') flags.mark_flag_as_required('input_file') flags.mark_flag_as_required('input_format') flags.mark_flag_as_required('output_file') flags.mark_flag_as_required('label_map_file') flags.mark_flag_as_required('vocab_file') flags.mark_flag_as_required('saved_model') label_map = utils.read_label_map(FLAGS.label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), FLAGS.enable_swap_tag) builder = bert_example.BertExampleBuilder(label_map, FLAGS.vocab_file, FLAGS.max_seq_length, FLAGS.do_lower_case, converter) predictor = predict_utils.LaserTaggerPredictor( tf.contrib.predictor.from_saved_model(FLAGS.saved_model), builder, label_map) print(colored("%s input file:%s" % (curLine(), FLAGS.input_file), "red")) sourcesA_list = [] with open(FLAGS.input_file) as f: for line in f: json_map = json.loads(line.rstrip('\n')) sourcesA_list.append(json_map["questions"]) print(curLine(), len(sourcesA_list), "sourcesA_list:", sourcesA_list[-1]) start_time = time.time() num_predicted = 0 with tf.gfile.Open(FLAGS.output_file, 'w') as writer: for batch_id, sources_batch in enumerate(sourcesA_list): # sources_batch = sourcesA_list[batch_id * predict_batch_size: (batch_id + 1) * predict_batch_size] location_batch = [] for source in sources_batch: location = list() for char in source[0]: if (char >= '0' and char <= '9') or char in '.- ' or ( char >= 'a' and char <= 'z') or (char >= 'A' and char <= 'Z'): location.append("1") # TODO TODO else: location.append("0") location_batch.append("".join(location)) prediction_batch = predictor.predict_batch( sources_batch=sources_batch, location_batch=location_batch) expand_list = [] for prediction in prediction_batch: # TODO if prediction in sources_batch: continue expand_list.append(prediction) json_map = {"questions": sources_batch, "expands": expand_list} json_str = json.dumps(json_map, ensure_ascii=False) writer.write("%s\n" % json_str) # input(curLine()) num_predicted += len(expand_list) if batch_id % 20 == 0: cost_time = (time.time() - start_time) / 60.0 print( "%s batch_id=%d/%d, predict %d/%d examples, cost %.2fmin." % (curLine(), batch_id + 1, len(sourcesA_list), num_predicted, num_predicted, cost_time)) cost_time = (time.time() - start_time) / 60.0
try: nltk.download('averaged_perceptron_tagger') except FileExistsError: print("NLTK averaged_perceptron_tagger exist") if embedding_type == "Normal" or embedding_type == "Sentence": vocab_file = "gs://lasertagger_training_yechen/cased_L-12_H-768_A-12/vocab.txt" elif embedding_type == "POS": vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS/vocab.txt" elif embedding_type == "POS_concise": vocab_file = "gs://bert_traning_yechen/trained_bert_uncased/bert_POS_concise/vocab.txt" else: raise ValueError("Unrecognized embedding type") label_map = utils.read_label_map(label_map_file) converter = tagging_converter.TaggingConverter( tagging_converter.get_phrase_vocabulary_from_label_map(label_map), True) id_2_tag = {tag_id: tagging.Tag(tag) for tag, tag_id in label_map.items()} builder = bert_example.BertExampleBuilder(label_map, vocab_file, 128, do_lower_case, converter, embedding_type, enable_masking) grammar_vocab_file = "gs://publicly_available_models_yechen/grammar_checker/vocab.txt" grammar_builder = bert_example_classifier.BertGrammarExampleBuilder( grammar_vocab_file, 128, False) def predict_json(project, model, instances, version=None): """ Send a json object to GCP deployed model for prediction. Args: project: name of the project where the model is in