793 lines
33 KiB
Python
793 lines
33 KiB
Python
from tqdm import tqdm
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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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from ...file_utils import is_tf_available
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if is_tf_available():
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import tensorflow as tf
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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=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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mask_padding_with_zero=True,
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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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cls_token = tokenizer.cls_token
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sep_token = tokenizer.sep_token
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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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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(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 if not sequence_a_is_doc else all_doc_tokens,
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all_doc_tokens if not sequence_a_is_doc else truncated_query,
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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' if not sequence_a_is_doc else 'only_first'
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)
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ids = encoded_dict['input_ids']
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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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index = len(truncated_query) + sequence_added_tokens + i if not sequence_a_is_doc else i
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token_to_orig_map[index] = 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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encoded_dict = tokenizer.encode_plus(
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truncated_query if not sequence_a_is_doc else overflowing_tokens,
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overflowing_tokens if not sequence_a_is_doc else truncated_query,
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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' if not sequence_a_is_doc else 'only_first'
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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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# print(encoded_dict["input_ids"].index(tokenizer.pad_token_id) if tokenizer.pad_token_id in encoded_dict["input_ids"] else None)
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# print(len(spans) * doc_stride, len(all_doc_tokens))
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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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if tokenizer.pad_token_id in encoded_dict['input_ids']:
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non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
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else:
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non_padded_ids = encoded_dict['input_ids']
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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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index = len(truncated_query) + sequence_added_tokens + i if not sequence_a_is_doc else i
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token_to_orig_map[index] = 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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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 = j if sequence_a_is_doc else 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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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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p_mask = np.minimum(p_mask, 1)
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if not sequence_a_is_doc:
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# Limit positive values to one
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p_mask = 1 - p_mask
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p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
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# Set the CLS index to '0'
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p_mask[cls_index] = 0
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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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tok_start_position = -1
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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(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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(tok_start_position, tok_end_position) = _improve_answer_span(
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all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
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example.orig_answer_text)
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# The -3 accounts for [CLS], [SEP] and [SEP]
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max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
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# We can have documents that are longer than the maximum sequence length.
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# To deal with this we do a sliding window approach, where we take chunks
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# of the up to our max length with a stride of `doc_stride`.
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_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
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"DocSpan", ["start", "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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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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# print("Start offset is", start_offset, len(all_doc_tokens), "length is", length)
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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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break
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start_offset += min(length, doc_stride)
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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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token_is_max_context = {}
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segment_ids = []
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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 = []
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# CLS token at the beginning
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if not cls_token_at_end:
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tokens.append(cls_token)
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segment_ids.append(cls_token_segment_id)
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p_mask.append(0)
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cls_index = 0
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# XLNet: P SEP Q SEP CLS
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# Others: CLS Q SEP P SEP
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if not sequence_a_is_doc:
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# Query
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tokens += query_tokens
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segment_ids += [sequence_a_segment_id] * len(query_tokens)
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p_mask += [1] * len(query_tokens)
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(1)
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# Paragraph
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for i in range(doc_span.length):
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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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if not sequence_a_is_doc:
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segment_ids.append(sequence_b_segment_id)
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else:
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(0)
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paragraph_len = doc_span.length
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if sequence_a_is_doc:
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_a_segment_id)
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p_mask.append(1)
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tokens += query_tokens
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segment_ids += [sequence_b_segment_id] * len(query_tokens)
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p_mask += [1] * len(query_tokens)
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# SEP token
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tokens.append(sep_token)
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segment_ids.append(sequence_b_segment_id)
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p_mask.append(1)
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# CLS token at the end
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if cls_token_at_end:
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tokens.append(cls_token)
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segment_ids.append(cls_token_segment_id)
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p_mask.append(0)
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cls_index = len(tokens) - 1 # Index of classification token
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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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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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 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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# For training, if our document chunk does not contain an annotation
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# we throw it out, since there is nothing to predict.
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doc_start = doc_span.start
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doc_end = doc_span.start + doc_span.length - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and
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tok_end_position <= doc_end):
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out_of_span = True
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if out_of_span:
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start_position = 0
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end_position = 0
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span_is_impossible = True
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else:
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if sequence_a_is_doc:
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doc_offset = 0
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else:
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doc_offset = len(query_tokens) + 2
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start_position = tok_start_position - doc_start + doc_offset
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end_position = tok_end_position - doc_start + doc_offset
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if is_training and span_is_impossible:
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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" % 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]))
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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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features.append(
|
|
SquadFeatures(
|
|
unique_id=unique_id,
|
|
example_index=example_index,
|
|
doc_span_index=doc_span_index,
|
|
tokens=tokens,
|
|
token_to_orig_map=token_to_orig_map,
|
|
token_is_max_context=token_is_max_context,
|
|
input_ids=input_ids,
|
|
input_mask=input_mask,
|
|
segment_ids=segment_ids,
|
|
cls_index=cls_index,
|
|
p_mask=p_mask,
|
|
paragraph_len=paragraph_len,
|
|
start_position=start_position,
|
|
end_position=end_position,
|
|
is_impossible=span_is_impossible))
|
|
unique_id += 1
|
|
|
|
assert len(features) == len(new_features)
|
|
|
|
assert len(features) == len(new_features)
|
|
for i in range(len(features)):
|
|
feature, new_feature = features[i], new_features[i]
|
|
|
|
input_ids = [f if f not in [3,4,5] else 0 for f in 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 = [f if f not in [3,4,5] else 0 for f in 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
|
|
|
|
tokens = [t if tokenizer.convert_tokens_to_ids(t) is not tokenizer.unk_token_id else tokenizer.unk_token for t in tokens]
|
|
|
|
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):
|
|
"""Read a SQuAD json file into a list of SquadExample."""
|
|
with open(input_file, "r", encoding='utf-8') as reader:
|
|
input_data = 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(
|
|
whitespace_tokenize(orig_answer_text))
|
|
if actual_text.find(cleaned_answer_text) == -1:
|
|
logger.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
|
|
|
|
|
|
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.
|
|
For examples without an answer, the start and end position are -1.
|
|
"""
|
|
|
|
def __init__(self,
|
|
qas_id,
|
|
question_text,
|
|
doc_tokens,
|
|
orig_answer_text=None,
|
|
start_position=None,
|
|
end_position=None,
|
|
is_impossible=None):
|
|
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" % (self.qas_id)
|
|
s += ", question_text: %s" % (
|
|
self.question_text)
|
|
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
|
|
if self.start_position:
|
|
s += ", start_position: %d" % (self.start_position)
|
|
if self.end_position:
|
|
s += ", end_position: %d" % (self.end_position)
|
|
if self.is_impossible:
|
|
s += ", is_impossible: %r" % (self.is_impossible)
|
|
return s
|
|
|
|
|
|
class SquadFeatures(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,
|
|
cls_index,
|
|
p_mask,
|
|
paragraph_len,
|
|
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.cls_index = cls_index
|
|
self.p_mask = p_mask
|
|
self.paragraph_len = paragraph_len
|
|
self.start_position = start_position
|
|
self.end_position = end_position
|
|
self.is_impossible = is_impossible
|
|
|
|
def __eq__(self, other):
|
|
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.attention_mask and \
|
|
self.p_mask == other.p_mask and \
|
|
self.paragraph_len == other.paragraph_len 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 |