# Copyright 2019 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. # ============================================================================== """Run ALBERT on SQuAD 1.1 and SQuAD 2.0 using sentence piece tokenization. The file is forked from: https://github.com/google-research/ALBERT/blob/master/run_squad_sp.py """ import collections import copy import json import math import os from absl import logging import numpy as np import tensorflow as tf from official.nlp.bert 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. """ def __init__(self, qas_id, question_text, paragraph_text, orig_answer_text=None, start_position=None, end_position=None, is_impossible=False): self.qas_id = qas_id self.question_text = question_text self.paragraph_text = paragraph_text 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 += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text)) 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, tok_start_to_orig_index, tok_end_to_orig_index, token_is_max_context, tokens, input_ids, input_mask, segment_ids, paragraph_len, class_index=None, paragraph_mask=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.tok_start_to_orig_index = tok_start_to_orig_index self.tok_end_to_orig_index = tok_end_to_orig_index self.token_is_max_context = token_is_max_context self.tokens = tokens self.input_ids = input_ids self.input_mask = input_mask self.paragraph_mask = paragraph_mask self.segment_ids = segment_ids self.paragraph_len = paragraph_len self.class_index = class_index self.start_position = start_position self.end_position = end_position self.is_impossible = is_impossible def read_squad_examples(input_file, is_training, version_2_with_negative): """Read a SQuAD json file into a list of SquadExample.""" del version_2_with_negative with tf.io.gfile.GFile(input_file, "r") as reader: input_data = json.load(reader)["data"] examples = [] for entry in input_data: for paragraph in entry["paragraphs"]: paragraph_text = paragraph["context"] for qa in paragraph["qas"]: qas_id = qa["id"] question_text = qa["question"] start_position = None orig_answer_text = None is_impossible = False if is_training: is_impossible = qa.get("is_impossible", False) 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"] start_position = answer["answer_start"] else: start_position = -1 orig_answer_text = "" example = SquadExample( qas_id=qas_id, question_text=question_text, paragraph_text=paragraph_text, orig_answer_text=orig_answer_text, start_position=start_position, is_impossible=is_impossible) examples.append(example) return examples def _convert_index(index, pos, m=None, is_start=True): """Converts index.""" if index[pos] is not None: return index[pos] n = len(index) rear = pos while rear < n - 1 and index[rear] is None: rear += 1 front = pos while front > 0 and index[front] is None: front -= 1 assert index[front] is not None or index[rear] is not None if index[front] is None: if index[rear] >= 1: if is_start: return 0 else: return index[rear] - 1 return index[rear] if index[rear] is None: if m is not None and index[front] < m - 1: if is_start: return index[front] + 1 else: return m - 1 return index[front] if is_start: if index[rear] > index[front] + 1: return index[front] + 1 else: return index[rear] else: if index[rear] > index[front] + 1: return index[rear] - 1 else: return index[front] def convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, output_fn, do_lower_case, xlnet_format=False, batch_size=None): """Loads a data file into a list of `InputBatch`s.""" cnt_pos, cnt_neg = 0, 0 base_id = 1000000000 unique_id = base_id max_n, max_m = 1024, 1024 f = np.zeros((max_n, max_m), dtype=np.float32) for (example_index, example) in enumerate(examples): if example_index % 100 == 0: logging.info("Converting %d/%d pos %d neg %d", example_index, len(examples), cnt_pos, cnt_neg) query_tokens = tokenization.encode_ids( tokenizer.sp_model, tokenization.preprocess_text( example.question_text, lower=do_lower_case)) if len(query_tokens) > max_query_length: query_tokens = query_tokens[0:max_query_length] paragraph_text = example.paragraph_text para_tokens = tokenization.encode_pieces( tokenizer.sp_model, tokenization.preprocess_text( example.paragraph_text, lower=do_lower_case)) chartok_to_tok_index = [] tok_start_to_chartok_index = [] tok_end_to_chartok_index = [] char_cnt = 0 for i, token in enumerate(para_tokens): new_token = token.replace(tokenization.SPIECE_UNDERLINE, " ") chartok_to_tok_index.extend([i] * len(new_token)) tok_start_to_chartok_index.append(char_cnt) char_cnt += len(new_token) tok_end_to_chartok_index.append(char_cnt - 1) tok_cat_text = "".join(para_tokens).replace(tokenization.SPIECE_UNDERLINE, " ") n, m = len(paragraph_text), len(tok_cat_text) if n > max_n or m > max_m: max_n = max(n, max_n) max_m = max(m, max_m) f = np.zeros((max_n, max_m), dtype=np.float32) g = {} # pylint: disable=cell-var-from-loop def _lcs_match(max_dist, n=n, m=m): """Longest-common-substring algorithm.""" f.fill(0) g.clear() ### longest common sub sequence # f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j)) for i in range(n): # unlike standard LCS, this is specifically optimized for the setting # because the mismatch between sentence pieces and original text will # be small for j in range(i - max_dist, i + max_dist): if j >= m or j < 0: continue if i > 0: g[(i, j)] = 0 f[i, j] = f[i - 1, j] if j > 0 and f[i, j - 1] > f[i, j]: g[(i, j)] = 1 f[i, j] = f[i, j - 1] f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0 if (tokenization.preprocess_text( paragraph_text[i], lower=do_lower_case, remove_space=False) == tok_cat_text[j] and f_prev + 1 > f[i, j]): g[(i, j)] = 2 f[i, j] = f_prev + 1 # pylint: enable=cell-var-from-loop max_dist = abs(n - m) + 5 for _ in range(2): _lcs_match(max_dist) if f[n - 1, m - 1] > 0.8 * n: break max_dist *= 2 orig_to_chartok_index = [None] * n chartok_to_orig_index = [None] * m i, j = n - 1, m - 1 while i >= 0 and j >= 0: if (i, j) not in g: break if g[(i, j)] == 2: orig_to_chartok_index[i] = j chartok_to_orig_index[j] = i i, j = i - 1, j - 1 elif g[(i, j)] == 1: j = j - 1 else: i = i - 1 if (all(v is None for v in orig_to_chartok_index) or f[n - 1, m - 1] < 0.8 * n): logging.info("MISMATCH DETECTED!") continue tok_start_to_orig_index = [] tok_end_to_orig_index = [] for i in range(len(para_tokens)): start_chartok_pos = tok_start_to_chartok_index[i] end_chartok_pos = tok_end_to_chartok_index[i] start_orig_pos = _convert_index( chartok_to_orig_index, start_chartok_pos, n, is_start=True) end_orig_pos = _convert_index( chartok_to_orig_index, end_chartok_pos, n, is_start=False) tok_start_to_orig_index.append(start_orig_pos) tok_end_to_orig_index.append(end_orig_pos) if not is_training: tok_start_position = tok_end_position = None if is_training and example.is_impossible: tok_start_position = 0 tok_end_position = 0 if is_training and not example.is_impossible: start_position = example.start_position end_position = start_position + len(example.orig_answer_text) - 1 start_chartok_pos = _convert_index( orig_to_chartok_index, start_position, is_start=True) tok_start_position = chartok_to_tok_index[start_chartok_pos] end_chartok_pos = _convert_index( orig_to_chartok_index, end_position, is_start=False) tok_end_position = chartok_to_tok_index[end_chartok_pos] assert tok_start_position <= tok_end_position def _piece_to_id(x): return tokenizer.sp_model.PieceToId(x) all_doc_tokens = list(map(_piece_to_id, para_tokens)) # 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_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 = [] cur_tok_start_to_orig_index = [] cur_tok_end_to_orig_index = [] # 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(tokenizer.sp_model.PieceToId("[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 cur_tok_start_to_orig_index.append( tok_start_to_orig_index[split_token_index]) cur_tok_end_to_orig_index.append( tok_end_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(tokenizer.sp_model.PieceToId("[SEP]")) segment_ids.append(seg_p) paragraph_mask.append(0) return len(tokens) def process_class(seg_class): class_index = len(segment_ids) tokens.append(tokenizer.sp_model.PieceToId("[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 paragraph_len = 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) paragraph_len = process_paragraph(seg_p) input_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 span_is_impossible = example.is_impossible start_position = None end_position = None if is_training and not span_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: # continue start_position = 0 end_position = 0 span_is_impossible = True else: 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 is_training and span_is_impossible: start_position = class_index end_position = class_index 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("tok_start_to_orig_index: %s", " ".join([str(x) for x in cur_tok_start_to_orig_index])) logging.info("tok_end_to_orig_index: %s", " ".join([str(x) for x in cur_tok_end_to_orig_index])) logging.info( "token_is_max_context: %s", " ".join( ["%d:%s" % (x, y) for (x, y) in token_is_max_context.items()])) logging.info( "input_pieces: %s", " ".join([tokenizer.sp_model.IdToPiece(x) for x in tokens])) 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 and span_is_impossible: logging.info("impossible example span") if is_training and not span_is_impossible: pieces = [ tokenizer.sp_model.IdToPiece(token) for token in tokens[start_position:(end_position + 1)] ] answer_text = tokenizer.sp_model.DecodePieces(pieces) logging.info("start_position: %d", (start_position)) logging.info("end_position: %d", (end_position)) logging.info("answer: %s", (tokenization.printable_text(answer_text))) # With multi processing, the example_index is actually the index # within the current process therefore we use example_index=None # to avoid being used in the future. # The current code does not use example_index of training data. if is_training: feat_example_index = None else: feat_example_index = example_index feature = InputFeatures( unique_id=unique_id, example_index=feat_example_index, doc_span_index=doc_span_index, tok_start_to_orig_index=cur_tok_start_to_orig_index, tok_end_to_orig_index=cur_tok_end_to_orig_index, token_is_max_context=token_is_max_context, tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens], input_ids=input_ids, input_mask=input_mask, paragraph_mask=paragraph_mask, segment_ids=segment_ids, paragraph_len=paragraph_len, class_index=class_index, start_position=start_position, end_position=end_position, is_impossible=span_is_impossible) # Run callback if is_training: output_fn(feature) else: output_fn(feature, is_padding=False) unique_id += 1 if span_is_impossible: cnt_neg += 1 else: cnt_pos += 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) 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 logging.info("Total number of instances: %d = pos %d neg %d", cnt_pos + cnt_neg, cnt_pos, cnt_neg) return unique_id - base_id 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, verbose=False): """Postprocess model output, to form predicton results.""" del do_lower_case, verbose # XLNet emits further predictions for start, end indexes and impossibility # classifications. xlnet_format = (hasattr(all_results[0], "start_indexes") and all_results[0].start_indexes is not None) 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] # 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] doc_offset = 0 if xlnet_format else feature.tokens.index("[SEP]") + 1 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 - doc_offset >= len(feature.tok_start_to_orig_index): continue if end_index - doc_offset >= len(feature.tok_end_to_orig_index): 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 - doc_offset, end_index=end_index - doc_offset, 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=-1, end_index=-1, 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_start_to_orig_index = feature.tok_start_to_orig_index tok_end_to_orig_index = feature.tok_end_to_orig_index start_orig_pos = tok_start_to_orig_index[pred.start_index] end_orig_pos = tok_end_to_orig_index[pred.end_index] paragraph_text = example.paragraph_text final_text = paragraph_text[start_orig_pos:end_orig_pos + 1].strip() 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 inlude 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: assert 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: # predict "" iff the null score - the score of best non-null > threshold 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 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_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 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: features["paragraph_mask"] = create_int_feature(feature.paragraph_mask) if feature.class_index: 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 generate_tf_record_from_json_file(input_file_path, sp_model_file, output_path, max_seq_length=384, do_lower_case=True, max_query_length=64, doc_stride=128, xlnet_format=False, version_2_with_negative=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) tokenizer = tokenization.FullSentencePieceTokenizer( sp_model_file=sp_model_file) 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, do_lower_case=do_lower_case) 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