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Works for BERT
This commit is contained in:
LysandreJik
parent
a7dafe2f41
commit
a5a8a6175f
@ -3,6 +3,7 @@ import collections
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import logging
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import os
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import json
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import numpy as np
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from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
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from .utils import DataProcessor, InputExample, InputFeatures
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@ -13,10 +14,68 @@ if is_tf_available():
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logger = logging.getLogger(__name__)
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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def _new_check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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# if len(doc_spans) == 1:
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# return True
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span["start"] + doc_span["length"] - 1
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if position < doc_span["start"]:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span["start"]
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span["length"]
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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def _is_whitespace(c):
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if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
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return True
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return False
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def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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doc_stride, max_query_length, is_training,
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cls_token_at_end=False,
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cls_token_at_end=True,
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cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
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sequence_a_segment_id=0, sequence_b_segment_id=1,
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cls_token_segment_id=0, pad_token_segment_id=0,
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@ -24,57 +83,184 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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sequence_a_is_doc=False):
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"""Loads a data file into a list of `InputBatch`s."""
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# Defining helper methods
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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# Defining helper methods
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unique_id = 1000000000
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features = []
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new_features = []
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for (example_index, example) in enumerate(tqdm(examples)):
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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doc_tokens = []
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char_to_word_offset = []
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prev_is_whitespace = True
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# Split on whitespace so that different tokens may be attributed to their original position.
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for c in example.context_text:
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if _is_whitespace(c):
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prev_is_whitespace = True
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else:
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if prev_is_whitespace:
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doc_tokens.append(c)
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else:
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doc_tokens[-1] += c
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prev_is_whitespace = False
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char_to_word_offset.append(len(doc_tokens) - 1)
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if is_training:
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# Get start and end position
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answer_length = len(example.answer_text)
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start_position = char_to_word_offset[example.start_position]
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end_position = char_to_word_offset[example.start_position + answer_length - 1]
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# If the answer cannot be found in the text, then skip this example.
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actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
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cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
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if actual_text.find(cleaned_answer_text) == -1:
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logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
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continue
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tok_to_orig_index = []
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orig_to_tok_index = []
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all_doc_tokens = []
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for (i, token) in enumerate(example.doc_tokens):
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for (i, token) in enumerate(doc_tokens):
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orig_to_tok_index.append(len(all_doc_tokens))
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sub_tokens = tokenizer.tokenize(token)
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for sub_token in sub_tokens:
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tok_to_orig_index.append(i)
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all_doc_tokens.append(sub_token)
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spans = []
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truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
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sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence
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sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
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encoded_dict = tokenizer.encode_plus(
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truncated_query,
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all_doc_tokens,
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max_length=max_seq_length,
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padding_strategy='right',
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stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
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return_overflowing_tokens=True,
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truncation_strategy='only_second'
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)
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ids = encoded_dict['input_ids']
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print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
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non_padded_ids = ids[:ids.index(tokenizer.pad_token_id)] if tokenizer.pad_token_id in ids else ids
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paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
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tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
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token_to_orig_map = {}
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for i in range(paragraph_len):
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token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[0 + i]
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encoded_dict["paragraph_len"] = paragraph_len
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encoded_dict["tokens"] = tokens
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encoded_dict["token_to_orig_map"] = token_to_orig_map
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encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
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encoded_dict["token_is_max_context"] = {}
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encoded_dict["start"] = 0
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encoded_dict["length"] = paragraph_len
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spans.append(encoded_dict)
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print("YESSIR", len(spans) * doc_stride < len(all_doc_tokens), "overflowing_tokens" in encoded_dict)
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while len(spans) * doc_stride < len(all_doc_tokens) and "overflowing_tokens" in encoded_dict:
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overflowing_tokens = encoded_dict['overflowing_tokens']
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print("OVERFLOW", len(overflowing_tokens))
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encoded_dict = tokenizer.encode_plus(
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truncated_query,
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overflowing_tokens,
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max_length=max_seq_length,
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return_overflowing_tokens=True,
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padding_strategy='right',
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stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
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truncation_strategy='only_second'
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)
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ids = encoded_dict['input_ids']
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print("Ids computes; position of the first padding", ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in ids else None)
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# Length of the document without the query
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paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
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non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
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tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
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token_to_orig_map = {}
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for i in range(paragraph_len):
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token_to_orig_map[len(truncated_query) + sequence_added_tokens + i] = tok_to_orig_index[len(spans) * doc_stride + i]
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encoded_dict["paragraph_len"] = paragraph_len
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encoded_dict["tokens"] = tokens
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encoded_dict["token_to_orig_map"] = token_to_orig_map
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encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
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encoded_dict["token_is_max_context"] = {}
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encoded_dict["start"] = len(spans) * doc_stride
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encoded_dict["length"] = paragraph_len
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# split_token_index = doc_span.start + i
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# token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
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# is_max_context = _check_is_max_context(doc_spans, doc_span_index,
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# split_token_index)
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# token_is_max_context[len(tokens)] = is_max_context
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# tokens.append(all_doc_tokens[split_token_index])
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spans.append(encoded_dict)
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for doc_span_index in range(len(spans)):
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for j in range(spans[doc_span_index]["paragraph_len"]):
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is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
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index = spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
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spans[doc_span_index]["token_is_max_context"][index] = is_max_context
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print("new span", len(spans))
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for span in spans:
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# Identify the position of the CLS token
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cls_index = span['input_ids'].index(tokenizer.cls_token_id)
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# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
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# Original TF implem also keep the classification token (set to 0) (not sure why...)
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p_mask = np.array(span['token_type_ids'])
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# Convert all SEP indices to '0' before inversion
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p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 0
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# Limit positive values to one
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p_mask = 1 - np.minimum(p_mask, 1)
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# Set the CLS index to '0'
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p_mask[cls_index] = 0
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print("new features length", len(new_features))
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new_features.append(NewSquadFeatures(
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span['input_ids'],
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span['attention_mask'],
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span['token_type_ids'],
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cls_index,
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p_mask.tolist(),
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example_index=example_index,
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unique_id=unique_id,
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paragraph_len=span['paragraph_len'],
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token_is_max_context=span["token_is_max_context"],
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tokens=span["tokens"],
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token_to_orig_map=span["token_to_orig_map"]
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))
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unique_id += 1
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# tokenize ...
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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tok_start_position = None
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tok_end_position = None
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if is_training and example.is_impossible:
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@ -82,7 +268,7 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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tok_end_position = -1
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if is_training and not example.is_impossible:
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tok_start_position = orig_to_tok_index[example.start_position]
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if example.end_position < len(example.doc_tokens) - 1:
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if example.end_position < len(doc_tokens) - 1:
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tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
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else:
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tok_end_position = len(all_doc_tokens) - 1
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@ -101,14 +287,19 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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doc_spans = []
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start_offset = 0
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while start_offset < len(all_doc_tokens):
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print("OLD DOC CREATION BEGIN", start_offset, len(all_doc_tokens))
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length = len(all_doc_tokens) - start_offset
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if length > max_tokens_for_doc:
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length = max_tokens_for_doc
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doc_spans.append(_DocSpan(start=start_offset, length=length))
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if start_offset + length == len(all_doc_tokens):
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print("Done with this doc span, breaking out.", start_offset, length)
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break
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print("CHOOSING OFFSET", length, doc_stride)
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start_offset += min(length, doc_stride)
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print("OLD DOC CREATION END", start_offset)
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print("old span", len(doc_spans))
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for (doc_span_index, doc_span) in enumerate(doc_spans):
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tokens = []
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token_to_orig_map = {}
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@ -183,18 +374,20 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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# tokens are attended to.
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input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(pad_token)
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input_mask.append(0 if mask_padding_with_zero else 1)
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segment_ids.append(pad_token_segment_id)
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p_mask.append(1)
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print("[OLD] Ids computed; position of the first padding", input_ids.index(tokenizer.pad_token_id) if tokenizer.pad_token_id in input_ids else None)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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span_is_impossible = example.is_impossible
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span_is_impossible = example.is_impossible if hasattr(example, "is_impossible") else False
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start_position = None
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end_position = None
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if is_training and not span_is_impossible:
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@ -222,31 +415,32 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
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start_position = cls_index
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end_position = cls_index
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if example_index < 20:
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logger.info("*** Example ***")
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logger.info("unique_id: %s" % (unique_id))
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logger.info("example_index: %s" % (example_index))
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logger.info("doc_span_index: %s" % (doc_span_index))
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logger.info("tokens: %s" % " ".join(tokens))
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logger.info("token_to_orig_map: %s" % " ".join([
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"%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
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logger.info("token_is_max_context: %s" % " ".join([
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"%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
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]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info(
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"input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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if is_training and span_is_impossible:
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logger.info("impossible example")
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if is_training and not span_is_impossible:
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answer_text = " ".join(tokens[start_position:(end_position + 1)])
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logger.info("start_position: %d" % (start_position))
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logger.info("end_position: %d" % (end_position))
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logger.info(
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"answer: %s" % (answer_text))
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# if example_index < 20:
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# logger.info("*** Example ***")
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# logger.info("unique_id: %s" % (unique_id))
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# logger.info("example_index: %s" % (example_index))
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# logger.info("doc_span_index: %s" % (doc_span_index))
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# logger.info("tokens: %s" % str(tokens))
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# logger.info("token_to_orig_map: %s" % " ".join([
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# "%d:%d" % (x, y) for (x, y) in token_to_orig_map.items()]))
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# logger.info("token_is_max_context: %s" % " ".join([
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# "%d:%s" % (x, y) for (x, y) in token_is_max_context.items()
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# ]))
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# logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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# logger.info(
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# "input_mask: %s" % " ".join([str(x) for x in input_mask]))
|
||||
# logger.info(
|
||||
# "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
|
||||
# if is_training and span_is_impossible:
|
||||
# logger.info("impossible example")
|
||||
# if is_training and not span_is_impossible:
|
||||
# answer_text = " ".join(tokens[start_position:(end_position + 1)])
|
||||
# logger.info("start_position: %d" % (start_position))
|
||||
# logger.info("end_position: %d" % (end_position))
|
||||
# logger.info(
|
||||
# "answer: %s" % (answer_text))
|
||||
|
||||
print("features length", len(features))
|
||||
features.append(
|
||||
SquadFeatures(
|
||||
unique_id=unique_id,
|
||||
@ -266,7 +460,48 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
is_impossible=span_is_impossible))
|
||||
unique_id += 1
|
||||
|
||||
return features
|
||||
assert len(features) == len(new_features)
|
||||
|
||||
assert len(features) == len(new_features)
|
||||
for i in range(len(features)):
|
||||
print(i)
|
||||
feature, new_feature = features[i], new_features[i]
|
||||
|
||||
input_ids = feature.input_ids
|
||||
input_mask = feature.input_mask
|
||||
segment_ids = feature.segment_ids
|
||||
cls_index = feature.cls_index
|
||||
p_mask = feature.p_mask
|
||||
example_index = feature.example_index
|
||||
paragraph_len = feature.paragraph_len
|
||||
token_is_max_context = feature.token_is_max_context
|
||||
tokens = feature.tokens
|
||||
token_to_orig_map = feature.token_to_orig_map
|
||||
|
||||
new_input_ids = new_feature.input_ids
|
||||
new_input_mask = new_feature.attention_mask
|
||||
new_segment_ids = new_feature.token_type_ids
|
||||
new_cls_index = new_feature.cls_index
|
||||
new_p_mask = new_feature.p_mask
|
||||
new_example_index = new_feature.example_index
|
||||
new_paragraph_len = new_feature.paragraph_len
|
||||
new_token_is_max_context = new_feature.token_is_max_context
|
||||
new_tokens = new_feature.tokens
|
||||
new_token_to_orig_map = new_feature.token_to_orig_map
|
||||
|
||||
assert input_ids == new_input_ids
|
||||
assert input_mask == new_input_mask
|
||||
assert segment_ids == new_segment_ids
|
||||
assert cls_index == new_cls_index
|
||||
assert p_mask == new_p_mask
|
||||
assert example_index == new_example_index
|
||||
assert paragraph_len == new_paragraph_len
|
||||
assert token_is_max_context == new_token_is_max_context
|
||||
assert tokens == new_tokens
|
||||
assert token_to_orig_map == new_token_to_orig_map
|
||||
|
||||
|
||||
return new_features
|
||||
|
||||
|
||||
def read_squad_examples(input_file, is_training, version_2_with_negative):
|
||||
@ -347,6 +582,124 @@ def read_squad_examples(input_file, is_training, version_2_with_negative):
|
||||
return examples
|
||||
|
||||
|
||||
class SquadV1Processor(DataProcessor):
|
||||
"""Processor for the SQuAD data set."""
|
||||
|
||||
def get_example_from_tensor_dict(self, tensor_dict):
|
||||
"""See base class."""
|
||||
return NewSquadExample(
|
||||
tensor_dict['id'].numpy(),
|
||||
tensor_dict['question'].numpy().decode('utf-8'),
|
||||
tensor_dict['context'].numpy().decode('utf-8'),
|
||||
tensor_dict['answers']['text'].numpy().decode('utf-8'),
|
||||
tensor_dict['answers']['answers_start'].numpy().decode('utf-8'),
|
||||
tensor_dict['title'].numpy().decode('utf-8')
|
||||
)
|
||||
|
||||
def get_train_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
with open(os.path.join(data_dir, "train-v1.1.json"), "r", encoding='utf-8') as reader:
|
||||
input_data = json.load(reader)["data"]
|
||||
return self._create_examples(input_data, "train")
|
||||
|
||||
def get_dev_examples(self, data_dir):
|
||||
"""See base class."""
|
||||
with open(os.path.join(data_dir, "dev-v1.1.json"), "r", encoding='utf-8') as reader:
|
||||
input_data = json.load(reader)["data"]
|
||||
return self._create_examples(input_data, "dev")
|
||||
|
||||
def get_labels(self):
|
||||
"""See base class."""
|
||||
return ["0", "1"]
|
||||
|
||||
def _create_examples(self, input_data, set_type):
|
||||
"""Creates examples for the training and dev sets."""
|
||||
|
||||
is_training = set_type == "train"
|
||||
examples = []
|
||||
for entry in input_data:
|
||||
title = entry['title']
|
||||
for paragraph in entry["paragraphs"]:
|
||||
context_text = paragraph["context"]
|
||||
for qa in paragraph["qas"]:
|
||||
qas_id = qa["id"]
|
||||
question_text = qa["question"]
|
||||
start_position = None
|
||||
answer_text = None
|
||||
if is_training:
|
||||
if (len(qa["answers"]) != 1):
|
||||
raise ValueError(
|
||||
"For training, each question should have exactly 1 answer.")
|
||||
answer = qa["answers"][0]
|
||||
answer_text = answer['text']
|
||||
start_position = answer['answer_start']
|
||||
|
||||
example = NewSquadExample(
|
||||
qas_id=qas_id,
|
||||
question_text=question_text,
|
||||
context_text=context_text,
|
||||
answer_text=answer_text,
|
||||
start_position=start_position,
|
||||
title=title
|
||||
)
|
||||
examples.append(example)
|
||||
return examples
|
||||
|
||||
|
||||
|
||||
class NewSquadExample(object):
|
||||
"""
|
||||
A single training/test example for the Squad dataset, as loaded from disk.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
qas_id,
|
||||
question_text,
|
||||
context_text,
|
||||
answer_text,
|
||||
start_position,
|
||||
title):
|
||||
self.qas_id = qas_id
|
||||
self.question_text = question_text
|
||||
self.context_text = context_text
|
||||
self.answer_text = answer_text
|
||||
self.start_position = start_position
|
||||
self.title = title
|
||||
|
||||
|
||||
class NewSquadFeatures(object):
|
||||
"""
|
||||
Single squad example features to be fed to a model.
|
||||
Those features are model-specific.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
token_type_ids,
|
||||
cls_index,
|
||||
p_mask,
|
||||
|
||||
example_index,
|
||||
unique_id,
|
||||
paragraph_len,
|
||||
token_is_max_context,
|
||||
tokens,
|
||||
token_to_orig_map
|
||||
):
|
||||
self.input_ids = input_ids
|
||||
self.attention_mask = attention_mask
|
||||
self.token_type_ids = token_type_ids
|
||||
self.cls_index = cls_index
|
||||
self.p_mask = p_mask
|
||||
|
||||
self.example_index = example_index
|
||||
self.unique_id = unique_id
|
||||
self.paragraph_len = paragraph_len
|
||||
self.token_is_max_context = token_is_max_context
|
||||
self.tokens = tokens
|
||||
self.token_to_orig_map = token_to_orig_map
|
||||
|
||||
class SquadExample(object):
|
||||
"""
|
||||
A single training/test example for the Squad dataset.
|
||||
@ -423,18 +776,22 @@ class SquadFeatures(object):
|
||||
self.is_impossible = is_impossible
|
||||
|
||||
def __eq__(self, other):
|
||||
return self.cls_index == other.cls_index and \
|
||||
self.doc_span_index == other.doc_span_index and \
|
||||
self.end_position == other.end_position and \
|
||||
self.example_index == other.example_index and \
|
||||
print(self.example_index == other.example_index)
|
||||
print(self.input_ids == other.input_ids)
|
||||
print(self.input_mask == other.attention_mask)
|
||||
print(self.p_mask == other.p_mask)
|
||||
print(self.paragraph_len == other.paragraph_len)
|
||||
print(self.segment_ids == other.token_type_ids)
|
||||
print(self.token_is_max_context == other.token_is_max_context)
|
||||
print(self.token_to_orig_map == other.token_to_orig_map)
|
||||
print(self.tokens == other.tokens)
|
||||
|
||||
return self.example_index == other.example_index and \
|
||||
self.input_ids == other.input_ids and \
|
||||
self.input_mask == other.input_mask and \
|
||||
self.is_impossible == other.is_impossible and \
|
||||
self.input_mask == other.attention_mask and \
|
||||
self.p_mask == other.p_mask and \
|
||||
self.paragraph_len == other.paragraph_len and \
|
||||
self.segment_ids == other.segment_ids and \
|
||||
self.start_position == other.start_position and \
|
||||
self.segment_ids == other.token_type_ids and \
|
||||
self.token_is_max_context == other.token_is_max_context and \
|
||||
self.token_to_orig_map == other.token_to_orig_map and \
|
||||
self.tokens == other.tokens and \
|
||||
self.unique_id == other.unique_id
|
||||
self.tokens == other.tokens
|
||||
Loading…
Reference in New Issue
Block a user