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diff --git a/scripts/api_squad.py b/scripts/api_squad.py
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+# coding=utf-8
+# squad interface
+# Required parameters:
+# FLAGS_output_dir :the output path of the model training during training process, the output of the trained model, etc.; the output path of the model prediction during predicting process
+# FLAGS_init_checkpoint_squad : model initialization path, use bert pre-trained model for training; use the output path during training for prediction
+# FLAGS_predict_file : the file to be predicted, csv file
+# FLAGS_train_file : file to be trained, csv file
+# FLAGS_do_predict : whether to predict or not
+# FLAGS_do_train : whether to train or not
+# FLAGS_train_batch_size : the batch_size for training, default : 16
+# FLAGS_predict_batch_size : the batch_size when predicting, default: 8
+# FLAGS_learning_rate : the learning_rate at training time, default: 5e-5
+# FLAGS_num_train_epochs : epochs at training time, default: 3
+# FLAGS_max_answer_length : the maximum length of the answer, default: 100 characters
+# FLAGS_max_query_length : the maximum length of the question, default: 64
+# FLAGS_version_2_with_negative : whether there is no answer to the question, default false, must be set to False when reasoning
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import json
+import math
+import os
+import random
+import modeling
+import optimization
+import tokenization
+import six
+import tensorflow as tf
+import pandas as pd
+from global_setting import FLAGS_bert_config_file, FLAGS_vocab_file, FLAGS_init_checkpoint_squad
+
+
+
+
+FLAGS_max_seq_length = 512
+FLAGS_do_lower_case = True
+FLAGS_doc_stride = 128
+
+
+FLAGS_save_checkpoints_steps = 1000
+FLAGS_iterations_per_loop = 1000
+FLAGS_n_best_size = 20
+FLAGS_tpu_zone = None
+FLAGS_tpu_name = None
+FLAGS_num_tpu_cores = 8
+FLAGS_verbose_logging = False
+FLAGS_master = None
+FLAGS_use_tpu = False
+FLAGS_warmup_proportion = 0.1
+FLAGS_gcp_project = None
+FLAGS_null_score_diff_threshold = 0.0
+
+def make_json(input_file,questions):
+ print(input_file)
+ data_train = pd.read_excel(input_file)
+ print(444)
+ data_train.fillna(0,inplace=True)
+ data_train.index = [i for i in range(len(data_train))]
+ question = questions
+ res = {}
+ res['data'] = []
+ data_inside = {}
+ for i in data_train.index:
+ data_inside['title'] = 'Not available'
+ data_inside['paragraphs'] = []
+ paragraphs_inside = {}
+ paragraphs_inside['context'] = data_train.loc[i,'text']
+ paragraphs_inside['qas'] = []
+ for ques in question:
+ qas_inside = {}
+ qas_inside['answers'] = []
+ if data_train.loc[i,ques]:
+ answer_inside = {}
+ answer_inside['text'] = str(data_train.loc[i,ques])
+ answer_inside['answer_start'] = paragraphs_inside['context'].find(answer_inside['text'])
+ qas_inside['is_impossible'] = 0
+
+ else:
+ qas_inside['is_impossible'] = 1
+ answer_inside = {}
+ qas_inside['id'] = str(i) + ques
+ qas_inside['question'] = ques
+ qas_inside['answers'].append(answer_inside.copy())
+ paragraphs_inside['qas'].append(qas_inside.copy())
+ data_inside['paragraphs'].append(paragraphs_inside.copy())
+
+ res['data'].append(data_inside.copy())
+ print('make json done')
+ return json.dumps(res)
+
+
+
+
+class SquadExample(object):
+ """A single training/test example for simple sequence classification.
+
+ For examples without an answer, the start and end position are -1.
+ """
+
+ def __init__(self,
+ qas_id,
+ question_text,
+ doc_tokens,
+ orig_answer_text=None,
+ start_position=None,
+ end_position=None,
+ is_impossible=False):
+ self.qas_id = qas_id
+ self.question_text = question_text
+ self.doc_tokens = doc_tokens
+ self.orig_answer_text = orig_answer_text
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+ def __str__(self):
+ return self.__repr__()
+
+ def __repr__(self):
+ s = ""
+ s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+ s += ", question_text: %s" % (
+ tokenization.printable_text(self.question_text))
+ s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+ if self.start_position:
+ s += ", start_position: %d" % (self.start_position)
+ if self.start_position:
+ s += ", end_position: %d" % (self.end_position)
+ if self.start_position:
+ s += ", is_impossible: %r" % (self.is_impossible)
+ return s
+
+
+class InputFeatures(object):
+ """A single set of features of data."""
+
+ def __init__(self,
+ unique_id,
+ example_index,
+ doc_span_index,
+ tokens,
+ token_to_orig_map,
+ token_is_max_context,
+ input_ids,
+ input_mask,
+ segment_ids,
+ start_position=None,
+ end_position=None,
+ is_impossible=None):
+ self.unique_id = unique_id
+ self.example_index = example_index
+ self.doc_span_index = doc_span_index
+ self.tokens = tokens
+ self.token_to_orig_map = token_to_orig_map
+ self.token_is_max_context = token_is_max_context
+ self.input_ids = input_ids
+ self.input_mask = input_mask
+ self.segment_ids = segment_ids
+ self.start_position = start_position
+ self.end_position = end_position
+ self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training,questions,FLAGS_version_2_with_negative):
+ """Read a SQuAD json file into a list of SquadExample."""
+ data = make_json(input_file,questions)
+ input_data = json.loads(data)["data"]
+
+ def is_whitespace(c):
+ if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+ return True
+ return False
+
+ examples = []
+ for entry in input_data:
+ for paragraph in entry["paragraphs"]:
+ paragraph_text = paragraph["context"]
+ doc_tokens = []
+ char_to_word_offset = []
+ prev_is_whitespace = True
+ for c in paragraph_text:
+ if is_whitespace(c):
+ prev_is_whitespace = True
+ else:
+ if prev_is_whitespace:
+ doc_tokens.append(c)
+ else:
+ doc_tokens[-1] += c
+ prev_is_whitespace = False
+ char_to_word_offset.append(len(doc_tokens) - 1)
+
+ for qa in paragraph["qas"]:
+ qas_id = qa["id"]
+ question_text = qa["question"]
+ start_position = None
+ end_position = None
+ orig_answer_text = None
+ is_impossible = False
+ if is_training:
+
+ if FLAGS_version_2_with_negative:
+ is_impossible = qa["is_impossible"]
+ if (len(qa["answers"]) != 1) and (not is_impossible):
+ raise ValueError(
+ "For training, each question should have exactly 1 answer.")
+ if not is_impossible:
+ answer = qa["answers"][0]
+ orig_answer_text = answer["text"]
+ answer_offset = answer["answer_start"]
+ answer_length = len(orig_answer_text)
+ start_position = char_to_word_offset[answer_offset]
+ end_position = char_to_word_offset[answer_offset + answer_length -
+ 1]
+ # Only add answers where the text can be exactly recovered from the
+ # document. If this CAN'T happen it's likely due to weird Unicode
+ # stuff so we will just skip the example.
+ #
+ # Note that this means for training mode, every example is NOT
+ # guaranteed to be preserved.
+ actual_text = " ".join(
+ doc_tokens[start_position:(end_position + 1)])
+ cleaned_answer_text = " ".join(
+ tokenization.whitespace_tokenize(orig_answer_text))
+ if actual_text.find(cleaned_answer_text) == -1:
+ tf.logging.warning("Could not find answer: '%s' vs. '%s'",
+ actual_text, cleaned_answer_text)
+ continue
+ else:
+ start_position = -1
+ end_position = -1
+ orig_answer_text = ""
+
+ example = SquadExample(
+ qas_id=qas_id,
+ question_text=question_text,
+ doc_tokens=doc_tokens,
+ orig_answer_text=orig_answer_text,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=is_impossible)
+ examples.append(example)
+
+ return examples
+
+
+def convert_examples_to_features(examples, tokenizer, max_seq_length,
+ doc_stride, max_query_length, is_training,
+ output_fn):
+ """Loads a data file into a list of `InputBatch`s."""
+
+ unique_id = 1000000000
+
+ for (example_index, example) in enumerate(examples):
+ query_tokens = tokenizer.tokenize(example.question_text)
+
+ if len(query_tokens) > max_query_length:
+ query_tokens = query_tokens[0:max_query_length]
+
+ tok_to_orig_index = []
+ orig_to_tok_index = []
+ all_doc_tokens = []
+ for (i, token) in enumerate(example.doc_tokens):
+ orig_to_tok_index.append(len(all_doc_tokens))
+ sub_tokens = tokenizer.tokenize(token)
+ for sub_token in sub_tokens:
+ tok_to_orig_index.append(i)
+ all_doc_tokens.append(sub_token)
+
+ tok_start_position = None
+ tok_end_position = None
+ if is_training and example.is_impossible:
+ tok_start_position = -1
+ tok_end_position = -1
+ if is_training and not example.is_impossible:
+ tok_start_position = orig_to_tok_index[example.start_position]
+ if example.end_position < len(example.doc_tokens) - 1:
+ tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
+ else:
+ tok_end_position = len(all_doc_tokens) - 1
+ (tok_start_position, tok_end_position) = _improve_answer_span(
+ all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
+ example.orig_answer_text)
+
+ # The -3 accounts for [CLS], [SEP] and [SEP]
+ max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+ # We can have documents that are longer than the maximum sequence length.
+ # To deal with this we do a sliding window approach, where we take chunks
+ # of the up to our max length with a stride of `doc_stride`.
+ _DocSpan = collections.namedtuple( # pylint: disable=invalid-name
+ "DocSpan", ["start", "length"])
+ doc_spans = []
+ start_offset = 0
+ while start_offset < len(all_doc_tokens):
+ length = len(all_doc_tokens) - start_offset
+ if length > max_tokens_for_doc:
+ length = max_tokens_for_doc
+ doc_spans.append(_DocSpan(start=start_offset, length=length))
+ if start_offset + length == len(all_doc_tokens):
+ break
+ start_offset += min(length, doc_stride)
+
+ for (doc_span_index, doc_span) in enumerate(doc_spans):
+ tokens = []
+ token_to_orig_map = {}
+ token_is_max_context = {}
+ segment_ids = []
+ tokens.append("[CLS]")
+ segment_ids.append(0)
+ for token in query_tokens:
+ tokens.append(token)
+ segment_ids.append(0)
+ tokens.append("[SEP]")
+ segment_ids.append(0)
+
+ for i in range(doc_span.length):
+ split_token_index = doc_span.start + i
+ token_to_orig_map[len(
+ tokens)] = tok_to_orig_index[split_token_index]
+
+ is_max_context = _check_is_max_context(doc_spans, doc_span_index,
+ split_token_index)
+ token_is_max_context[len(tokens)] = is_max_context
+ tokens.append(all_doc_tokens[split_token_index])
+ segment_ids.append(1)
+ tokens.append("[SEP]")
+ segment_ids.append(1)
+
+ input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+ # The mask has 1 for real tokens and 0 for padding tokens. Only real
+ # tokens are attended to.
+ input_mask = [1] * len(input_ids)
+
+ # Zero-pad up to the sequence length.
+ while len(input_ids) < max_seq_length:
+ input_ids.append(0)
+ input_mask.append(0)
+ segment_ids.append(0)
+
+ assert len(input_ids) == max_seq_length
+ assert len(input_mask) == max_seq_length
+ assert len(segment_ids) == max_seq_length
+
+ start_position = None
+ end_position = None
+ if is_training and not example.is_impossible:
+ # For training, if our document chunk does not contain an annotation
+ # we throw it out, since there is nothing to predict.
+ doc_start = doc_span.start
+ doc_end = doc_span.start + doc_span.length - 1
+ out_of_span = False
+ if not (tok_start_position >= doc_start and
+ tok_end_position <= doc_end):
+ out_of_span = True
+ if out_of_span:
+ start_position = 0
+ end_position = 0
+ else:
+ doc_offset = len(query_tokens) + 2
+ start_position = tok_start_position - doc_start + doc_offset
+ end_position = tok_end_position - doc_start + doc_offset
+
+ if is_training and example.is_impossible:
+ start_position = 0
+ end_position = 0
+
+ if example_index < 20:
+ tf.logging.info("*** Example ***")
+ tf.logging.info("unique_id: %s" % (unique_id))
+ tf.logging.info("example_index: %s" % (example_index))
+ tf.logging.info("doc_span_index: %s" % (doc_span_index))
+ tf.logging.info("tokens: %s" % " ".join(
+ [tokenization.printable_text(x) for x in tokens]))
+ tf.logging.info("token_to_orig_map: %s" % " ".join(
+ ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
+ tf.logging.info("token_is_max_context: %s" % " ".join([
+ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
+ ]))
+ tf.logging.info("input_ids: %s" %
+ " ".join([str(x) for x in input_ids]))
+ tf.logging.info(
+ "input_mask: %s" % " ".join([str(x) for x in input_mask]))
+ tf.logging.info(
+ "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
+ if is_training and example.is_impossible:
+ tf.logging.info("impossible example")
+ if is_training and not example.is_impossible:
+ answer_text = " ".join(
+ tokens[start_position:(end_position + 1)])
+ tf.logging.info("start_position: %d" % (start_position))
+ tf.logging.info("end_position: %d" % (end_position))
+ tf.logging.info(
+ "answer: %s" % (tokenization.printable_text(answer_text)))
+
+ feature = InputFeatures(
+ unique_id=unique_id,
+ example_index=example_index,
+ doc_span_index=doc_span_index,
+ tokens=tokens,
+ token_to_orig_map=token_to_orig_map,
+ token_is_max_context=token_is_max_context,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ segment_ids=segment_ids,
+ start_position=start_position,
+ end_position=end_position,
+ is_impossible=example.is_impossible)
+
+ # Run callback
+ output_fn(feature)
+
+ unique_id += 1
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+ orig_answer_text):
+ """Returns tokenized answer spans that better match the annotated answer."""
+
+ # The SQuAD annotations are character based. We first project them to
+ # whitespace-tokenized words. But then after WordPiece tokenization, we can
+ # often find a "better match". For example:
+ #
+ # Question: What year was John Smith born?
+ # Context: The leader was John Smith (1895-1943).
+ # Answer: 1895
+ #
+ # The original whitespace-tokenized answer will be "(1895-1943).". However
+ # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+ # the exact answer, 1895.
+ #
+ # However, this is not always possible. Consider the following:
+ #
+ # Question: What country is the top exporter of electornics?
+ # Context: The Japanese electronics industry is the lagest in the world.
+ # Answer: Japan
+ #
+ # In this case, the annotator chose "Japan" as a character sub-span of
+ # the word "Japanese". Since our WordPiece tokenizer does not split
+ # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+ # in SQuAD, but does happen.
+ tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+ for new_start in range(input_start, input_end + 1):
+ for new_end in range(input_end, new_start - 1, -1):
+ text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+ if text_span == tok_answer_text:
+ return (new_start, new_end)
+
+ return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+ """Check if this is the 'max context' doc span for the token."""
+
+ # Because of the sliding window approach taken to scoring documents, a single
+ # token can appear in multiple documents. E.g.
+ # Doc: the man went to the store and bought a gallon of milk
+ # Span A: the man went to the
+ # Span B: to the store and bought
+ # Span C: and bought a gallon of
+ # ...
+ #
+ # Now the word 'bought' will have two scores from spans B and C. We only
+ # want to consider the score with "maximum context", which we define as
+ # the *minimum* of its left and right context (the *sum* of left and
+ # right context will always be the same, of course).
+ #
+ # In the example the maximum context for 'bought' would be span C since
+ # it has 1 left context and 3 right context, while span B has 4 left context
+ # and 0 right context.
+ best_score = None
+ best_span_index = None
+ for (span_index, doc_span) in enumerate(doc_spans):
+ end = doc_span.start + doc_span.length - 1
+ if position < doc_span.start:
+ continue
+ if position > end:
+ continue
+ num_left_context = position - doc_span.start
+ num_right_context = end - position
+ score = min(num_left_context, num_right_context) + \
+ 0.01 * doc_span.length
+ if best_score is None or score > best_score:
+ best_score = score
+ best_span_index = span_index
+
+ return cur_span_index == best_span_index
+
+
+def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
+ use_one_hot_embeddings):
+ """Creates a classification model."""
+ model = modeling.BertModel(
+ config=bert_config,
+ is_training=is_training,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ token_type_ids=segment_ids,
+ use_one_hot_embeddings=use_one_hot_embeddings)
+
+ final_hidden = model.get_sequence_output()
+
+ final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
+ batch_size = final_hidden_shape[0]
+ seq_length = final_hidden_shape[1]
+ hidden_size = final_hidden_shape[2]
+
+ output_weights = tf.get_variable(
+ "cls/squad/output_weights", [2, hidden_size],
+ initializer=tf.truncated_normal_initializer(stddev=0.02))
+
+ output_bias = tf.get_variable(
+ "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
+
+ final_hidden_matrix = tf.reshape(final_hidden,
+ [batch_size * seq_length, hidden_size])
+ logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
+ logits = tf.nn.bias_add(logits, output_bias)
+
+ logits = tf.reshape(logits, [batch_size, seq_length, 2])
+ logits = tf.transpose(logits, [2, 0, 1])
+
+ unstacked_logits = tf.unstack(logits, axis=0)
+
+ (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
+
+ return (start_logits, end_logits)
+
+
+def model_fn_builder(bert_config, init_checkpoint, learning_rate,
+ num_train_steps, num_warmup_steps, use_tpu,
+ use_one_hot_embeddings):
+ """Returns `model_fn` closure for TPUEstimator."""
+
+ def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
+ """The `model_fn` for TPUEstimator."""
+
+ tf.logging.info("*** Features ***")
+ for name in sorted(features.keys()):
+ tf.logging.info(" name = %s, shape = %s" %
+ (name, features[name].shape))
+
+ unique_ids = features["unique_ids"]
+ input_ids = features["input_ids"]
+ input_mask = features["input_mask"]
+ segment_ids = features["segment_ids"]
+
+ is_training = (mode == tf.estimator.ModeKeys.TRAIN)
+
+ (start_logits, end_logits) = create_model(
+ bert_config=bert_config,
+ is_training=is_training,
+ input_ids=input_ids,
+ input_mask=input_mask,
+ segment_ids=segment_ids,
+ use_one_hot_embeddings=use_one_hot_embeddings)
+
+ tvars = tf.trainable_variables()
+
+ initialized_variable_names = {}
+ scaffold_fn = None
+ if init_checkpoint:
+ (assignment_map, initialized_variable_names
+ ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
+ if use_tpu:
+
+ def tpu_scaffold():
+ tf.train.init_from_checkpoint(
+ init_checkpoint, assignment_map)
+ return tf.train.Scaffold()
+
+ scaffold_fn = tpu_scaffold
+ else:
+ tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
+
+ tf.logging.info("**** Trainable Variables ****")
+ for var in tvars:
+ init_string = ""
+ if var.name in initialized_variable_names:
+ init_string = ", *INIT_FROM_CKPT*"
+ tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
+ init_string)
+
+ output_spec = None
+ if mode == tf.estimator.ModeKeys.TRAIN:
+ seq_length = modeling.get_shape_list(input_ids)[1]
+
+ def compute_loss(logits, positions):
+ one_hot_positions = tf.one_hot(
+ positions, depth=seq_length, dtype=tf.float32)
+ log_probs = tf.nn.log_softmax(logits, axis=-1)
+ loss = -tf.reduce_mean(
+ tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
+ return loss
+
+ start_positions = features["start_positions"]
+ end_positions = features["end_positions"]
+
+ start_loss = compute_loss(start_logits, start_positions)
+ end_loss = compute_loss(end_logits, end_positions)
+
+ total_loss = (start_loss + end_loss) / 2.0
+
+ train_op = optimization.create_optimizer(
+ total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
+
+ output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+ mode=mode,
+ loss=total_loss,
+ train_op=train_op,
+ scaffold_fn=scaffold_fn)
+ elif mode == tf.estimator.ModeKeys.PREDICT:
+ predictions = {
+ # "unique_ids": unique_ids,
+ "start_logits": start_logits,
+ "end_logits": end_logits,
+ }
+ output_spec = tf.contrib.tpu.TPUEstimatorSpec(
+ mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
+ else:
+ raise ValueError(
+ "Only TRAIN and PREDICT modes are supported: %s" % (mode))
+
+ return output_spec
+
+ return model_fn
+
+
+def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
+ """Creates an `input_fn` closure to be passed to TPUEstimator."""
+
+ name_to_features = {
+ "unique_ids": tf.FixedLenFeature([], tf.int64),
+ "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
+ "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
+ "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
+ }
+
+ if is_training:
+ name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
+ name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
+
+ def _decode_record(record, name_to_features):
+ """Decodes a record to a TensorFlow example."""
+ example = tf.parse_single_example(record, name_to_features)
+
+ # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
+ # So cast all int64 to int32.
+ for name in list(example.keys()):
+ t = example[name]
+ if t.dtype == tf.int64:
+ t = tf.to_int32(t)
+ example[name] = t
+
+ return example
+
+ def input_fn(params):
+ """The actual input function."""
+ batch_size = params["batch_size"]
+
+ # For training, we want a lot of parallel reading and shuffling.
+ # For eval, we want no shuffling and parallel reading doesn't matter.
+ d = tf.data.TFRecordDataset(input_file)
+ if is_training:
+ d = d.repeat()
+ d = d.shuffle(buffer_size=100)
+
+ d = d.apply(
+ tf.contrib.data.map_and_batch(
+ lambda record: _decode_record(record, name_to_features),
+ batch_size=batch_size,
+ drop_remainder=drop_remainder))
+
+ return d
+
+ return input_fn
+
+
+RawResult = collections.namedtuple("RawResult",
+ ["unique_id", "start_logits", "end_logits"])
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+ max_answer_length, do_lower_case, output_prediction_file,
+ output_nbest_file, output_null_log_odds_file):
+ """Write final predictions to the json file and log-odds of null if needed."""
+ tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
+ tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
+
+ example_index_to_features = collections.defaultdict(list)
+ for feature in all_features:
+ example_index_to_features[feature.example_index].append(feature)
+
+ unique_id_to_result = {}
+ for result in all_results:
+ unique_id_to_result[result.unique_id] = result
+
+ _PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "PrelimPrediction",
+ ["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
+
+ all_predictions = collections.OrderedDict()
+ all_nbest_json = collections.OrderedDict()
+ scores_diff_json = collections.OrderedDict()
+
+ for (example_index, example) in enumerate(all_examples):
+ features = example_index_to_features[example_index]
+
+ prelim_predictions = []
+ # keep track of the minimum score of null start+end of position 0
+ score_null = 1000000 # large and positive
+ min_null_feature_index = 0 # the paragraph slice with min mull score
+ null_start_logit = 0 # the start logit at the slice with min null score
+ null_end_logit = 0 # the end logit at the slice with min null score
+ for (feature_index, feature) in enumerate(features):
+ result = unique_id_to_result[feature.unique_id]
+ start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+ end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+ # if we could have irrelevant answers, get the min score of irrelevant
+ if FLAGS_version_2_with_negative:
+ feature_null_score = result.start_logits[0] + \
+ result.end_logits[0]
+ if feature_null_score < score_null:
+ score_null = feature_null_score
+ min_null_feature_index = feature_index
+ null_start_logit = result.start_logits[0]
+ null_end_logit = result.end_logits[0]
+ for start_index in start_indexes:
+ for end_index in end_indexes:
+ # We could hypothetically create invalid predictions, e.g., predict
+ # that the start of the span is in the question. We throw out all
+ # invalid predictions.
+ if start_index >= len(feature.tokens):
+ continue
+ if end_index >= len(feature.tokens):
+ continue
+ if start_index not in feature.token_to_orig_map:
+ continue
+ if end_index not in feature.token_to_orig_map:
+ continue
+ if not feature.token_is_max_context.get(start_index, False):
+ continue
+ if end_index < start_index:
+ continue
+ length = end_index - start_index + 1
+ if length > max_answer_length:
+ continue
+ prelim_predictions.append(
+ _PrelimPrediction(
+ feature_index=feature_index,
+ start_index=start_index,
+ end_index=end_index,
+ start_logit=result.start_logits[start_index],
+ end_logit=result.end_logits[end_index]))
+
+ prelim_predictions = sorted(
+ prelim_predictions,
+ key=lambda x: (x.start_logit + x.end_logit),
+ reverse=True)
+
+ _NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
+ "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+ seen_predictions = {}
+ nbest = []
+ for pred in prelim_predictions:
+ if len(nbest) >= n_best_size:
+ break
+ feature = features[pred.feature_index]
+ if pred.start_index > 0: # this is a non-null prediction
+ tok_tokens = feature.tokens[pred.start_index:(
+ pred.end_index + 1)]
+ orig_doc_start = feature.token_to_orig_map[pred.start_index]
+ orig_doc_end = feature.token_to_orig_map[pred.end_index]
+ orig_tokens = example.doc_tokens[orig_doc_start:(
+ orig_doc_end + 1)]
+ tok_text = " ".join(tok_tokens)
+
+ # De-tokenize WordPieces that have been split off.
+ tok_text = tok_text.replace(" ##", "")
+ tok_text = tok_text.replace("##", "")
+
+ # Clean whitespace
+ tok_text = tok_text.strip()
+ tok_text = " ".join(tok_text.split())
+ orig_text = " ".join(orig_tokens)
+
+ final_text = get_final_text(tok_text, orig_text, do_lower_case)
+ if final_text in seen_predictions:
+ continue
+
+ seen_predictions[final_text] = True
+ else:
+ final_text = ""
+ seen_predictions[final_text] = True
+
+ nbest.append(
+ _NbestPrediction(
+ text=final_text,
+ start_logit=pred.start_logit,
+ end_logit=pred.end_logit))
+
+ # In very rare edge cases we could have no valid predictions. So we
+ # just create a nonce prediction in this case to avoid failure.
+ if not nbest:
+ nbest.append(
+ _NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
+
+ assert len(nbest) >= 1
+
+ total_scores = []
+ best_non_null_entry = None
+ for entry in nbest:
+ total_scores.append(entry.start_logit + entry.end_logit)
+ if not best_non_null_entry:
+ if entry.text:
+ best_non_null_entry = entry
+
+ probs = _compute_softmax(total_scores)
+
+ nbest_json = []
+ for (i, entry) in enumerate(nbest):
+ output = collections.OrderedDict()
+ output["text"] = entry.text
+ output["probability"] = probs[i]
+ output["start_logit"] = entry.start_logit
+ output["end_logit"] = entry.end_logit
+ nbest_json.append(output)
+
+ assert len(nbest_json) >= 1
+
+ all_predictions[example.qas_id] = nbest_json[0]["text"]
+
+ all_nbest_json[example.qas_id] = nbest_json
+
+ with tf.gfile.GFile(output_prediction_file, "w") as writer:
+ writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case):
+ """Project the tokenized prediction back to the original text."""
+
+ # When we created the data, we kept track of the alignment between original
+ # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+ # now `orig_text` contains the span of our original text corresponding to the
+ # span that we predicted.
+ #
+ # However, `orig_text` may contain extra characters that we don't want in
+ # our prediction.
+ #
+ # For example, let's say:
+ # pred_text = steve smith
+ # orig_text = Steve Smith's
+ #
+ # We don't want to return `orig_text` because it contains the extra "'s".
+ #
+ # We don't want to return `pred_text` because it's already been normalized
+ # (the SQuAD eval script also does punctuation stripping/lower casing but
+ # our tokenizer does additional normalization like stripping accent
+ # characters).
+ #
+ # What we really want to return is "Steve Smith".
+ #
+ # Therefore, we have to apply a semi-complicated alignment heruistic between
+ # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+ # can fail in certain cases in which case we just return `orig_text`.
+
+ def _strip_spaces(text):
+ ns_chars = []
+ ns_to_s_map = collections.OrderedDict()
+ for (i, c) in enumerate(text):
+ if c == " ":
+ continue
+ ns_to_s_map[len(ns_chars)] = i
+ ns_chars.append(c)
+ ns_text = "".join(ns_chars)
+ return (ns_text, ns_to_s_map)
+
+ # We first tokenize `orig_text`, strip whitespace from the result
+ # and `pred_text`, and check if they are the same length. If they are
+ # NOT the same length, the heuristic has failed. If they are the same
+ # length, we assume the characters are one-to-one aligned.
+ tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+ tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+ start_position = tok_text.find(pred_text)
+ if start_position == -1:
+ if FLAGS_verbose_logging:
+ tf.logging.info(
+ "Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+ return orig_text
+ end_position = start_position + len(pred_text) - 1
+
+ (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+ (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+ if len(orig_ns_text) != len(tok_ns_text):
+ if FLAGS_verbose_logging:
+ tf.logging.info("Length not equal after stripping spaces: '%s' vs '%s'",
+ orig_ns_text, tok_ns_text)
+ return orig_text
+
+ # We then project the characters in `pred_text` back to `orig_text` using
+ # the character-to-character alignment.
+ tok_s_to_ns_map = {}
+ for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+ tok_s_to_ns_map[tok_index] = i
+
+ orig_start_position = None
+ if start_position in tok_s_to_ns_map:
+ ns_start_position = tok_s_to_ns_map[start_position]
+ if ns_start_position in orig_ns_to_s_map:
+ orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+ if orig_start_position is None:
+ if FLAGS_verbose_logging:
+ tf.logging.info("Couldn't map start position")
+ return orig_text
+
+ orig_end_position = None
+ if end_position in tok_s_to_ns_map:
+ ns_end_position = tok_s_to_ns_map[end_position]
+ if ns_end_position in orig_ns_to_s_map:
+ orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+ if orig_end_position is None:
+ if FLAGS_verbose_logging:
+ tf.logging.info("Couldn't map end position")
+ return orig_text
+
+ output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+ return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+ """Get the n-best logits from a list."""
+ index_and_score = sorted(
+ enumerate(logits), key=lambda x: x[1], reverse=True)
+
+ best_indexes = []
+ for i in range(len(index_and_score)):
+ if i >= n_best_size:
+ break
+ best_indexes.append(index_and_score[i][0])
+ return best_indexes
+
+
+def _compute_softmax(scores):
+ """Compute softmax probability over raw logits."""
+ if not scores:
+ return []
+
+ max_score = None
+ for score in scores:
+ if max_score is None or score > max_score:
+ max_score = score
+
+ exp_scores = []
+ total_sum = 0.0
+ for score in scores:
+ x = math.exp(score - max_score)
+ exp_scores.append(x)
+ total_sum += x
+
+ probs = []
+ for score in exp_scores:
+ probs.append(score / total_sum)
+ return probs
+
+
+class FeatureWriter(object):
+ """Writes InputFeature to TF example file."""
+
+ def __init__(self, filename, is_training):
+ self.filename = filename
+ self.is_training = is_training
+ self.num_features = 0
+ self._writer = tf.python_io.TFRecordWriter(filename)
+
+ def process_feature(self, feature):
+ """Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
+ self.num_features += 1
+
+ def create_int_feature(values):
+ feature = tf.train.Feature(
+ int64_list=tf.train.Int64List(value=list(values)))
+ return feature
+
+ features = collections.OrderedDict()
+ features["unique_ids"] = create_int_feature([feature.unique_id])
+ features["input_ids"] = create_int_feature(feature.input_ids)
+ features["input_mask"] = create_int_feature(feature.input_mask)
+ features["segment_ids"] = create_int_feature(feature.segment_ids)
+
+ if self.is_training:
+ features["start_positions"] = create_int_feature(
+ [feature.start_position])
+ features["end_positions"] = create_int_feature(
+ [feature.end_position])
+ impossible = 0
+ if feature.is_impossible:
+ impossible = 1
+ features["is_impossible"] = create_int_feature([impossible])
+
+ tf_example = tf.train.Example(
+ features=tf.train.Features(feature=features))
+ self._writer.write(tf_example.SerializeToString())
+
+ def close(self):
+ self._writer.close()
+
+
+def validate_flags_or_throw(bert_config):
+ """Validate the input FLAGS or throw an exception."""
+ tokenization.validate_case_matches_checkpoint(FLAGS_do_lower_case,
+ FLAGS_init_checkpoint_squad)
+
+ # if not FLAGS_do_train and not FLAGS_do_predict:
+ # raise ValueError(
+ # "At least one of `do_train` or `do_predict` must be True.")
+
+ # if FLAGS_do_train:
+ # if not FLAGS_train_file:
+ # raise ValueError(
+ # "If `do_train` is True, then `train_file` must be specified.")
+ # if FLAGS_do_predict:
+ # if not FLAGS_predict_file:
+ # raise ValueError(
+ # "If `do_predict` is True, then `predict_file` must be specified.")
+
+ # if FLAGS_max_seq_length > bert_config.max_position_embeddings:
+ # raise ValueError(
+ # "Cannot use sequence length %d because the BERT model "
+ # "was only trained up to sequence length %d" %
+ # (FLAGS_max_seq_length, bert_config.max_position_embeddings))
+
+ # if FLAGS_max_seq_length <= FLAGS_max_query_length + 3:
+ # raise ValueError(
+ # "The max_seq_length (%d) must be greater than max_query_length "
+ # "(%d) + 3" % (FLAGS_max_seq_length, FLAGS_max_query_length))