# Copyright 2024 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Library to process data for SQuAD 1.1 and SQuAD 2.0.""" # pylint: disable=g-bad-import-order import collections import copy import json import math import os import six from absl import logging import tensorflow as tf, tf_keras from official.nlp.tools import tokenization class SquadExample(object): """A single training/test example for simple sequence classification. For examples without an answer, the start and end position are -1. Attributes: qas_id: ID of the question-answer pair. question_text: Original text for the question. doc_tokens: The list of tokens in the context obtained by splitting on whitespace only. orig_answer_text: Original text for the answer. start_position: Starting index of the answer in `doc_tokens`. end_position: Ending index of the answer in `doc_tokens`. is_impossible: Whether the question is impossible to answer given the context. Only used in SQuAD 2.0. """ 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, paragraph_mask=None, class_index=None, 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 self.paragraph_mask = paragraph_mask self.class_index = class_index 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 tf.io.gfile.makedirs(os.path.dirname(filename)) self._writer = tf.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 feature.paragraph_mask is not None: features["paragraph_mask"] = create_int_feature(feature.paragraph_mask) if feature.class_index is not None: features["class_index"] = create_int_feature([feature.class_index]) 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 read_squad_examples(input_file, is_training, version_2_with_negative, translated_input_folder=None): """Read a SQuAD json file into a list of SquadExample.""" with tf.io.gfile.GFile(input_file, "r") as reader: input_data = json.load(reader)["data"] if translated_input_folder is not None: translated_files = tf.io.gfile.glob( os.path.join(translated_input_folder, "*.json")) for file in translated_files: with tf.io.gfile.GFile(file, "r") as reader: input_data.extend(json.load(reader)["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 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: 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, xlnet_format=False, batch_size=None): """Loads a data file into a list of `InputBatch`s.""" base_id = 1000000000 unique_id = base_id feature = None 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 = [] # Paragraph mask used in XLNet. # 1 represents paragraph and class tokens. # 0 represents query and other special tokens. paragraph_mask = [] # pylint: disable=cell-var-from-loop def process_query(seg_q): for token in query_tokens: tokens.append(token) segment_ids.append(seg_q) paragraph_mask.append(0) tokens.append("[SEP]") segment_ids.append(seg_q) paragraph_mask.append(0) def process_paragraph(seg_p): 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(seg_p) paragraph_mask.append(1) tokens.append("[SEP]") segment_ids.append(seg_p) paragraph_mask.append(0) def process_class(seg_class): class_index = len(segment_ids) tokens.append("[CLS]") segment_ids.append(seg_class) paragraph_mask.append(1) return class_index if xlnet_format: seg_p, seg_q, seg_class, seg_pad = 0, 1, 2, 3 process_paragraph(seg_p) process_query(seg_q) class_index = process_class(seg_class) else: seg_p, seg_q, seg_class, seg_pad = 1, 0, 0, 0 class_index = process_class(seg_class) process_query(seg_q) process_paragraph(seg_p) 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(seg_pad) paragraph_mask.append(0) assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(paragraph_mask) == max_seq_length start_position = 0 end_position = 0 span_contains_answer = False 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 span_contains_answer = (tok_start_position >= doc_start and tok_end_position <= doc_end) if span_contains_answer: doc_offset = 0 if xlnet_format else len(query_tokens) + 2 start_position = tok_start_position - doc_start + doc_offset end_position = tok_end_position - doc_start + doc_offset if example_index < 20: logging.info("*** Example ***") logging.info("unique_id: %s", (unique_id)) logging.info("example_index: %s", (example_index)) logging.info("doc_span_index: %s", (doc_span_index)) logging.info("tokens: %s", " ".join([tokenization.printable_text(x) for x in tokens])) logging.info( "token_to_orig_map: %s", " ".join([ "%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map) ])) logging.info( "token_is_max_context: %s", " ".join([ "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context) ])) logging.info("input_ids: %s", " ".join([str(x) for x in input_ids])) logging.info("input_mask: %s", " ".join([str(x) for x in input_mask])) logging.info("segment_ids: %s", " ".join([str(x) for x in segment_ids])) logging.info("paragraph_mask: %s", " ".join( [str(x) for x in paragraph_mask])) logging.info("class_index: %d", class_index) if is_training: if span_contains_answer: answer_text = " ".join(tokens[start_position:(end_position + 1)]) logging.info("start_position: %d", (start_position)) logging.info("end_position: %d", (end_position)) logging.info("answer: %s", tokenization.printable_text(answer_text)) else: logging.info("document span doesn't contain answer") feature = InputFeatures( unique_id=unique_id, example_index=example_index, doc_span_index=doc_span_index, tokens=tokens, paragraph_mask=paragraph_mask, class_index=class_index, 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=not span_contains_answer) # Run callback if is_training: output_fn(feature) else: output_fn(feature, is_padding=False) unique_id += 1 if not is_training and feature: assert batch_size num_padding = 0 num_examples = unique_id - base_id if unique_id % batch_size != 0: num_padding = batch_size - (num_examples % batch_size) logging.info("Adding padding examples to make sure no partial batch.") logging.info("Adds %d padding examples for inference.", num_padding) dummy_feature = copy.deepcopy(feature) for _ in range(num_padding): dummy_feature.unique_id = unique_id # Run callback output_fn(feature, is_padding=True) unique_id += 1 return unique_id - base_id 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 electronics? # 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 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, version_2_with_negative=False, null_score_diff_threshold=0.0, verbose=False): """Write final predictions to the json file and log-odds of null if needed.""" logging.info("Writing predictions to: %s", (output_prediction_file)) logging.info("Writing nbest to: %s", (output_nbest_file)) all_predictions, all_nbest_json, scores_diff_json = ( postprocess_output( all_examples=all_examples, all_features=all_features, all_results=all_results, n_best_size=n_best_size, max_answer_length=max_answer_length, do_lower_case=do_lower_case, version_2_with_negative=version_2_with_negative, null_score_diff_threshold=null_score_diff_threshold, verbose=verbose)) write_to_json_files(all_predictions, output_prediction_file) write_to_json_files(all_nbest_json, output_nbest_file) if version_2_with_negative: write_to_json_files(scores_diff_json, output_null_log_odds_file) def postprocess_output(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, version_2_with_negative=False, null_score_diff_threshold=0.0, xlnet_format=False, verbose=False): """Postprocess model output, to form predicton results.""" 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): if feature.unique_id not in unique_id_to_result: logging.info("Skip eval example %s, not in pred.", feature.unique_id) continue result = unique_id_to_result[feature.unique_id] # if we could have irrelevant answers, get the min score of irrelevant if version_2_with_negative: if xlnet_format: feature_null_score = result.class_logits else: 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, start_logit, end_index, end_logit) in _get_best_indexes_and_logits( result=result, n_best_size=n_best_size, xlnet_format=xlnet_format): # 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=start_logit, end_logit=end_logit)) if version_2_with_negative and not xlnet_format: prelim_predictions.append( _PrelimPrediction( feature_index=min_null_feature_index, start_index=0, end_index=0, start_logit=null_start_logit, end_logit=null_end_logit)) 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 or xlnet_format: # 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, verbose=verbose) 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)) # if we didn't include the empty option in the n-best, include it if version_2_with_negative and not xlnet_format: if "" not in seen_predictions: nbest.append( _NbestPrediction( text="", start_logit=null_start_logit, end_logit=null_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 if not version_2_with_negative: all_predictions[example.qas_id] = nbest_json[0]["text"] else: # pytype: disable=attribute-error # predict "" iff the null score - the score of best non-null > threshold if best_non_null_entry is not None: if xlnet_format: score_diff = score_null scores_diff_json[example.qas_id] = score_diff all_predictions[example.qas_id] = best_non_null_entry.text else: score_diff = score_null - best_non_null_entry.start_logit - ( best_non_null_entry.end_logit) scores_diff_json[example.qas_id] = score_diff if score_diff > null_score_diff_threshold: all_predictions[example.qas_id] = "" else: all_predictions[example.qas_id] = best_non_null_entry.text else: logging.warning("best_non_null_entry is None") scores_diff_json[example.qas_id] = score_null all_predictions[example.qas_id] = "" # pytype: enable=attribute-error all_nbest_json[example.qas_id] = nbest_json return all_predictions, all_nbest_json, scores_diff_json def write_to_json_files(json_records, json_file): with tf.io.gfile.GFile(json_file, "w") as writer: writer.write(json.dumps(json_records, indent=4) + "\n") def get_final_text(pred_text, orig_text, do_lower_case, verbose=False): """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-character 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 verbose: 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 verbose: 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 verbose: 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 verbose: 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_and_logits(result, n_best_size, xlnet_format=False): """Generates the n-best indexes and logits from a list.""" if xlnet_format: for i in range(n_best_size): for j in range(n_best_size): j_index = i * n_best_size + j yield (result.start_indexes[i], result.start_logits[i], result.end_indexes[j_index], result.end_logits[j_index]) else: start_index_and_score = sorted(enumerate(result.start_logits), key=lambda x: x[1], reverse=True) end_index_and_score = sorted(enumerate(result.end_logits), key=lambda x: x[1], reverse=True) for i in range(len(start_index_and_score)): if i >= n_best_size: break for j in range(len(end_index_and_score)): if j >= n_best_size: break yield (start_index_and_score[i][0], start_index_and_score[i][1], end_index_and_score[j][0], end_index_and_score[j][1]) 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 def generate_tf_record_from_json_file(input_file_path, vocab_file_path, output_path, translated_input_folder=None, max_seq_length=384, do_lower_case=True, max_query_length=64, doc_stride=128, version_2_with_negative=False, xlnet_format=False): """Generates and saves training data into a tf record file.""" train_examples = read_squad_examples( input_file=input_file_path, is_training=True, version_2_with_negative=version_2_with_negative, translated_input_folder=translated_input_folder) tokenizer = tokenization.FullTokenizer( vocab_file=vocab_file_path, do_lower_case=do_lower_case) train_writer = FeatureWriter(filename=output_path, is_training=True) number_of_examples = convert_examples_to_features( examples=train_examples, tokenizer=tokenizer, max_seq_length=max_seq_length, doc_stride=doc_stride, max_query_length=max_query_length, is_training=True, output_fn=train_writer.process_feature, xlnet_format=xlnet_format) train_writer.close() meta_data = { "task_type": "bert_squad", "train_data_size": number_of_examples, "max_seq_length": max_seq_length, "max_query_length": max_query_length, "doc_stride": doc_stride, "version_2_with_negative": version_2_with_negative, } return meta_data