499 lines
19 KiB
Python
499 lines
19 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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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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unique_id = 1000000000
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features = []
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for (example_index, example) in enumerate(tqdm(examples)):
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if is_training and not example.is_impossible:
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# Get start and end position
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start_position = example.start_position
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end_position = example.end_position
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# If the answer cannot be found in the text, then skip this example.
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actual_text = " ".join(example.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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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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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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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, example.answer_text
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)
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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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span_doc_tokens = all_doc_tokens
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while len(spans) * doc_stride < len(all_doc_tokens):
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encoded_dict = tokenizer.encode_plus(
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truncated_query if not sequence_a_is_doc else span_doc_tokens,
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span_doc_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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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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if "overflowing_tokens" not in encoded_dict:
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break
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span_doc_tokens = encoded_dict["overflowing_tokens"]
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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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span_is_impossible = example.is_impossible
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start_position = 0
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end_position = 0
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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 = span["start"]
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doc_end = span["start"] + span["length"] - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and 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 = cls_index
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end_position = cls_index
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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(truncated_query) + sequence_added_tokens
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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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features.append(SquadFeatures(
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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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start_position=start_position,
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end_position=end_position
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))
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unique_id += 1
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return features
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class SquadProcessor(DataProcessor):
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"""Processor for the SQuAD data set."""
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train_file = None
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dev_file = None
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def get_example_from_tensor_dict(self, tensor_dict):
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return SquadExample(
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tensor_dict['id'].numpy().decode("utf-8"),
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tensor_dict['question'].numpy().decode('utf-8'),
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tensor_dict['context'].numpy().decode('utf-8'),
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tensor_dict['answers']['text'][0].numpy().decode('utf-8'),
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tensor_dict['answers']['answer_start'][0].numpy(),
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tensor_dict['title'].numpy().decode('utf-8')
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)
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def get_examples_from_dataset(self, dataset):
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"""See base class."""
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examples = []
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for tensor_dict in tqdm(dataset):
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examples.append(self.get_example_from_tensor_dict(tensor_dict))
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return examples
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def get_train_examples(self, data_dir, only_first=None):
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"""See base class."""
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if self.train_file is None:
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raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
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with open(os.path.join(data_dir, self.train_file), "r", encoding='utf-8') as reader:
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input_data = json.load(reader)["data"]
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return self._create_examples(input_data, "train", only_first)
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def get_dev_examples(self, data_dir, only_first=None):
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"""See base class."""
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if self.dev_file is None:
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raise ValueError("SquadProcessor should be instantiated via SquadV1Processor or SquadV2Processor")
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with open(os.path.join(data_dir, self.dev_file), "r", encoding='utf-8') as reader:
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input_data = json.load(reader)["data"]
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return self._create_examples(input_data, "dev", only_first)
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def get_labels(self):
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"""See base class."""
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return ["0", "1"]
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def _create_examples(self, input_data, set_type, only_first=None):
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"""Creates examples for the training and dev sets."""
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is_training = set_type == "train"
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examples = []
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for entry in tqdm(input_data):
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title = entry['title']
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for paragraph in entry["paragraphs"]:
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context_text = paragraph["context"]
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question_text = qa["question"]
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start_position_character = None
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answer_text = None
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if "is_impossible" in qa:
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is_impossible = qa["is_impossible"]
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else:
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is_impossible = False
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if not is_impossible and is_training:
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if (len(qa["answers"]) != 1):
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raise ValueError(
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"For training, each question should have exactly 1 answer.")
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answer = qa["answers"][0]
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answer_text = answer['text']
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start_position_character = answer['answer_start']
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example = SquadExample(
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qas_id=qas_id,
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question_text=question_text,
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context_text=context_text,
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answer_text=answer_text,
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start_position_character=start_position_character,
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title=title,
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is_impossible=is_impossible
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)
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examples.append(example)
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if only_first is not None and len(examples) > only_first:
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return examples
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return examples
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class SquadV1Processor(SquadProcessor):
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train_file = "train-v1.1.json"
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dev_file = "dev-v1.1.json"
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class SquadV2Processor(SquadProcessor):
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train_file = "train-v2.0.json"
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dev_file = "dev-v2.0.json"
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class SquadExample(object):
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"""
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A single training/test example for the Squad dataset, as loaded from disk.
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"""
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def __init__(self,
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qas_id,
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question_text,
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context_text,
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answer_text,
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start_position_character,
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title,
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is_impossible=False):
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self.qas_id = qas_id
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self.question_text = question_text
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self.context_text = context_text
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self.answer_text = answer_text
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self.title = title
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self.is_impossible = is_impossible
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self.start_position, self.end_position = 0, 0
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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 self.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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self.doc_tokens = doc_tokens
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self.char_to_word_offset = char_to_word_offset
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# Start end end positions only has a value during evaluation.
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if start_position_character is not None and not is_impossible:
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self.start_position = char_to_word_offset[start_position_character]
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self.end_position = char_to_word_offset[start_position_character + len(answer_text) - 1]
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class SquadFeatures(object):
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"""
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Single squad example features to be fed to a model.
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Those features are model-specific.
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"""
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def __init__(self,
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input_ids,
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attention_mask,
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token_type_ids,
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cls_index,
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p_mask,
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example_index,
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unique_id,
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paragraph_len,
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token_is_max_context,
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tokens,
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token_to_orig_map,
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start_position,
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end_position
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):
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self.input_ids = input_ids
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self.attention_mask = attention_mask
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self.token_type_ids = token_type_ids
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self.cls_index = cls_index
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self.p_mask = p_mask
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self.example_index = example_index
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self.unique_id = unique_id
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self.paragraph_len = paragraph_len
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self.token_is_max_context = token_is_max_context
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self.tokens = tokens
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self.token_to_orig_map = token_to_orig_map
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self.start_position = start_position
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self.end_position = end_position
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class SquadResult(object):
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"""
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Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset.
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Args:
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result: The result output by a model on a SQuAD inference. These results may be complex (5 values) as the ones output by
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XLNet or XLM or may be simple like the other models (2 values). They may be passed as a list or as a dict, with the
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following accepted formats:
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`dict` output by a simple model:
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{
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"start_logits": int,
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"end_logits": int,
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"unique_id": string
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}
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`list` output by a simple model:
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[start_logits, end_logits, unique_id]
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`dict` output by a complex model:
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{
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"start_top_log_probs": float,
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"start_top_index": int,
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"end_top_log_probs": float,
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"end_top_index": int,
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"cls_logits": int,
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"unique_id": string
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}
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`list` output by a complex model:
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[start_top_log_probs, start_top_index, end_top_log_probs, end_top_index, cls_logits, unique_id]
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See `run_squad.py` for an example.
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"""
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def __init__(self, result):
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if isinstance(result, dict):
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if "start_logits" in result and "end_logits" in result:
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self.start_logits = result["start_logits"]
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self.end_logits = result["end_logits"]
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elif "start_top_log_probs" in result and "start_top_index" in result:
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self.start_top_log_probs = result["start_top_log_probs"]
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self.start_top_index = result["start_top_index"]
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self.end_top_log_probs = result["end_top_log_probs"]
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self.end_top_index = result["end_top_index"]
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self.cls_logits = result["cls_logits"]
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else:
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raise ValueError("SquadResult instantiated with wrong values.")
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self.unique_id = result["unique_id"]
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elif isinstance(result, list):
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if len(result) == 3:
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self.start_logits = result[0]
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self.end_logits = result[1]
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elif len(result) == 6:
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self.start_top_log_probs = result[0]
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self.start_top_index = result[1]
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self.end_top_log_probs = result[2]
|
|
self.end_top_index = result[3]
|
|
self.cls_logits = result[4]
|
|
|
|
else:
|
|
raise ValueError("SquadResult instantiated with wrong values.")
|
|
|
|
self.unique_id = result[-1]
|
|
|
|
else:
|
|
raise ValueError("SquadResult instantiated with wrong values. Should be a dictionary or a list.")
|