wehub-resource-sync
1005 lines
44 KiB
Python
1005 lines
44 KiB
Python
from __future__ import absolute_import, division, print_function
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import json
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import logging
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import math
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import collections
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from io import open
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from os import path as osp
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from tqdm import tqdm
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import bs4
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from bs4 import BeautifulSoup as bs
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from transformers.models.bert.tokenization_bert import BasicTokenizer, whitespace_tokenize
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from torch.utils.data import Dataset
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from lxml import etree
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from markuplmft.data.tag_utils import tags_dict
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logger = logging.getLogger(__name__)
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class StrucDataset(Dataset):
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"""Dataset wrapping tensors.
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Each sample will be retrieved by indexing tensors along the first dimension.
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Arguments:
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*tensors (*torch.Tensor): tensors that have the same size of the first dimension.
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page_ids (list): the corresponding page ids of the input features.
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cnn_feature_dir (str): the direction where the cnn features are stored.
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token_to_tag (torch.Tensor): the mapping from each token to its corresponding tag id.
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"""
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def __init__(self, *tensors):
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tensors = tuple(tensor for tensor in tensors)
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assert all(len(tensors[0]) == len(tensor) for tensor in tensors)
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self.tensors = tensors
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def __getitem__(self, index):
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output = [tensor[index] for tensor in self.tensors]
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return tuple(item for item in output)
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def __len__(self):
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return len(self.tensors[0])
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class SRCExample(object):
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r"""
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The Containers for SRC Examples.
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Arguments:
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doc_tokens (list[str]): the original tokens of the HTML file before dividing into sub-tokens.
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qas_id (str): the id of the corresponding question.
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tag_num (int): the total tag number in the corresponding HTML file, including the additional 'yes' and 'no'.
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question_text (str): the text of the corresponding question.
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orig_answer_text (str): the answer text provided by the dataset.
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all_doc_tokens (list[str]): the sub-tokens of the corresponding HTML file.
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start_position (int): the position where the answer starts in the all_doc_tokens.
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end_position (int): the position where the answer ends in the all_doc_tokens; NOTE that the answer tokens
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include the token at end_position.
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tok_to_orig_index (list[int]): the mapping from sub-tokens (all_doc_tokens) to origin tokens (doc_tokens).
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orig_to_tok_index (list[int]): the mapping from origin tokens (doc_tokens) to sub-tokens (all_doc_tokens).
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tok_to_tags_index (list[int]): the mapping from sub-tokens (all_doc_tokens) to the id of the deepest tag it
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belongs to.
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"""
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# the difference between T-PLM and H-PLM is just add <xx> and </xx> into the
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# original tokens and further-tokenized tokens
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def __init__(self,
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doc_tokens,
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qas_id,
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tag_num, # <xx> ?? </xx> is counted as one tag
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question_text=None,
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html_code=None,
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orig_answer_text=None,
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start_position=None, # in all_doc_tokens
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end_position=None, # in all_doc_tokens
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tok_to_orig_index=None,
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orig_to_tok_index=None,
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all_doc_tokens=None,
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tok_to_tags_index=None,
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xpath_tag_map=None,
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xpath_subs_map=None,
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):
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self.doc_tokens = doc_tokens
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self.qas_id = qas_id
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self.tag_num = tag_num
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self.question_text = question_text
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self.html_code = html_code
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self.orig_answer_text = orig_answer_text
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self.start_position = start_position
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self.end_position = end_position
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self.tok_to_orig_index = tok_to_orig_index
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self.orig_to_tok_index = orig_to_tok_index
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self.all_doc_tokens = all_doc_tokens
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self.tok_to_tags_index = tok_to_tags_index
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self.xpath_tag_map = xpath_tag_map
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self.xpath_subs_map = xpath_subs_map
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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"""
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s = ""
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s += "qas_id: %s" % self.qas_id
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s += ", question_text: %s" % (
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self.question_text)
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s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
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if self.start_position:
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s += ", start_position: %d" % self.start_position
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if self.end_position:
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s += ", end_position: %d" % self.end_position
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"""
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s = "[INFO]\n"
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s += f"qas_id ({type(self.qas_id)}): {self.qas_id}\n"
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s += f"tag_num ({type(self.tag_num)}): {self.tag_num}\n"
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s += f"question_text ({type(self.question_text)}): {self.question_text}\n"
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s += f"html_code ({type(self.html_code)}): {self.html_code}\n"
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s += f"orig_answer_text ({type(self.orig_answer_text)}): {self.orig_answer_text}\n"
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s += f"start_position ({type(self.start_position)}): {self.start_position}\n"
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s += f"end_position ({type(self.end_position)}): {self.end_position}\n"
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s += f"tok_to_orig_index ({type(self.tok_to_orig_index)}): {self.tok_to_orig_index}\n"
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s += f"orig_to_tok_index ({type(self.orig_to_tok_index)}): {self.orig_to_tok_index}\n"
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s += f"all_doc_tokens ({type(self.all_doc_tokens)}): {self.all_doc_tokens}\n"
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s += f"tok_to_tags_index ({type(self.tok_to_tags_index)}): {self.tok_to_tags_index}\n"
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s += f"xpath_tag_map ({type(self.xpath_tag_map)}): {self.xpath_tag_map}\n"
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s += f"xpath_subs_map ({type(self.xpath_subs_map)}): {self.xpath_subs_map}\n"
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s += f"tree_id_map ({type(self.tree_id_map)}): {self.tree_id_map}\n"
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return s
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class InputFeatures(object):
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r"""
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The Container for the Features of Input Doc Spans.
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Arguments:
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unique_id (int): the unique id of the input doc span.
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example_index (int): the index of the corresponding SRC Example of the input doc span.
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page_id (str): the id of the corresponding web page of the question.
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doc_span_index (int): the index of the doc span among all the doc spans which corresponding to the same SRC
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Example.
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tokens (list[str]): the sub-tokens of the input sequence, including cls token, sep tokens, and the sub-tokens
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of the question and HTML file.
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token_to_orig_map (dict[int, int]): the mapping from the HTML file's sub-tokens in the sequence tokens (tokens)
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to the origin tokens (all_tokens in the corresponding SRC Example).
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token_is_max_context (dict[int, bool]): whether the current doc span contains the max pre- and post-context for
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each HTML file's sub-tokens.
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input_ids (list[int]): the ids of the sub-tokens in the input sequence (tokens).
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input_mask (list[int]): use 0/1 to distinguish the input sequence from paddings.
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segment_ids (list[int]): use 0/1 to distinguish the question and the HTML files.
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paragraph_len (int): the length of the HTML file's sub-tokens.
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start_position (int): the position where the answer starts in the input sequence (0 if the answer is not fully
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in the input sequence).
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end_position (int): the position where the answer ends in the input sequence; NOTE that the answer tokens
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include the token at end_position (0 if the answer is not fully in the input sequence).
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token_to_tag_index (list[int]): the mapping from sub-tokens of the input sequence to the id of the deepest tag
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it belongs to.
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is_impossible (bool): whether the answer is fully in the doc span.
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"""
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def __init__(self,
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unique_id,
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example_index,
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page_id,
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doc_span_index,
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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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input_ids,
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input_mask,
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segment_ids,
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paragraph_len,
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start_position=None,
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end_position=None,
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token_to_tag_index=None,
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is_impossible=None,
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xpath_tags_seq=None,
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xpath_subs_seq=None
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):
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self.unique_id = unique_id
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self.example_index = example_index
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self.page_id = page_id
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self.doc_span_index = doc_span_index
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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.token_is_max_context = token_is_max_context
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.paragraph_len = paragraph_len
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self.start_position = start_position
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self.end_position = end_position
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self.token_to_tag_index = token_to_tag_index
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self.is_impossible = is_impossible
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self.xpath_tags_seq = xpath_tags_seq
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self.xpath_subs_seq = xpath_subs_seq
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def html_escape(html):
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r"""
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replace the special expressions in the html file for specific punctuation.
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"""
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html = html.replace('"', '"')
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html = html.replace('&', '&')
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html = html.replace('<', '<')
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html = html.replace('>', '>')
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html = html.replace(' ', ' ')
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return html
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def get_xpath4tokens(html_fn: str, unique_tids: set):
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xpath_map = {}
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tree = etree.parse(html_fn, etree.HTMLParser())
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nodes = tree.xpath('//*')
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for node in nodes:
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tid = node.attrib.get("tid")
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if int(tid) in unique_tids:
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xpath_map[int(tid)] = tree.getpath(node)
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xpath_map[len(nodes)] = "/html"
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xpath_map[len(nodes) + 1] = "/html"
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return xpath_map
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def get_xpath_and_treeid4tokens(html_code, unique_tids, max_depth):
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unknown_tag_id = len(tags_dict)
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pad_tag_id = unknown_tag_id + 1
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max_width = 1000
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width_pad_id = 1001
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pad_x_tag_seq = [pad_tag_id] * max_depth
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pad_x_subs_seq = [width_pad_id] * max_depth
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def xpath_soup(element):
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xpath_tags = []
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xpath_subscripts = []
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tree_index = []
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child = element if element.name else element.parent
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for parent in child.parents: # type: bs4.element.Tag
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siblings = parent.find_all(child.name, recursive=False)
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para_siblings = parent.find_all(True, recursive=False)
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xpath_tags.append(child.name)
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xpath_subscripts.append(
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0 if 1 == len(siblings) else next(i for i, s in enumerate(siblings, 1) if s is child))
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tree_index.append(next(i for i, s in enumerate(para_siblings, 0) if s is child))
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child = parent
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xpath_tags.reverse()
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xpath_subscripts.reverse()
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tree_index.reverse()
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return xpath_tags, xpath_subscripts, tree_index
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xpath_tag_map = {}
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xpath_subs_map = {}
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for tid in unique_tids:
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element = html_code.find(attrs={'tid': tid})
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if element is None:
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xpath_tags = pad_x_tag_seq
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xpath_subscripts = pad_x_subs_seq
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xpath_tag_map[tid] = xpath_tags
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xpath_subs_map[tid] = xpath_subscripts
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continue
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xpath_tags, xpath_subscripts, tree_index = xpath_soup(element)
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assert len(xpath_tags) == len(xpath_subscripts)
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assert len(xpath_tags) == len(tree_index)
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if len(xpath_tags) > max_depth:
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xpath_tags = xpath_tags[-max_depth:]
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xpath_subscripts = xpath_subscripts[-max_depth:]
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xpath_tags = [tags_dict.get(name, unknown_tag_id) for name in xpath_tags]
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xpath_subscripts = [min(i, max_width) for i in xpath_subscripts]
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# we do not append them to max depth here
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xpath_tags += [pad_tag_id] * (max_depth - len(xpath_tags))
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xpath_subscripts += [width_pad_id] * (max_depth - len(xpath_subscripts))
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xpath_tag_map[tid] = xpath_tags
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xpath_subs_map[tid] = xpath_subscripts
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return xpath_tag_map, xpath_subs_map
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def read_squad_examples(input_file, root_dir, is_training, tokenizer, simplify=False, max_depth=50):
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r"""
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pre-process the data in json format into SRC Examples.
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Arguments:
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split_flag:
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attention_width:
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input_file (str): the inputting data file in json format.
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root_dir (str): the root directory of the raw WebSRC dataset, which contains the HTML files.
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is_training (bool): True if processing the training set, else False.
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tokenizer (Tokenizer): the tokenizer for PLM in use.
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method (str): the name of the method in use, choice: ['T-PLM', 'H-PLM', 'V-PLM'].
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simplify (bool): when setting to Ture, the returned Example will only contain document tokens, the id of the
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question-answers, and the total tag number in the corresponding html files.
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Returns:
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list[SRCExamples]: the resulting SRC Examples, contained all the needed information for the feature generation
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process, except when the argument simplify is setting to True;
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set[str]: all the tag names appeared in the processed dataset, e.g. <div>, <img/>, </p>, etc..
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"""
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with open(input_file, "r", encoding='utf-8') as reader:
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input_data = json.load(reader)["data"]
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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 html_to_text_list(h):
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tag_num, text_list = 0, []
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for element in h.descendants:
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if (type(element) == bs4.element.NavigableString) and (element.strip()):
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text_list.append(element.strip())
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if type(element) == bs4.element.Tag:
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tag_num += 1
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return text_list, tag_num + 2 # + 2 because we treat the additional 'yes' and 'no' as two special tags.
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def e_id_to_t_id(e_id, html):
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t_id = 0
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for element in html.descendants:
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if type(element) == bs4.element.NavigableString and element.strip():
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t_id += 1
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if type(element) == bs4.element.Tag:
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if int(element.attrs['tid']) == e_id:
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break
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return t_id
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def calc_num_from_raw_text_list(t_id, l):
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n_char = 0
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for i in range(t_id):
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n_char += len(l[i]) + 1
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return n_char
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def word_tag_offset(html):
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cnt, w_t, t_w, tags, tags_tids = 0, [], [], [], []
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for element in html.descendants:
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if type(element) == bs4.element.Tag:
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content = ' '.join(list(element.strings)).split()
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t_w.append({'start': cnt, 'len': len(content)})
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tags.append('<' + element.name + '>')
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tags_tids.append(element['tid'])
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elif type(element) == bs4.element.NavigableString and element.strip():
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text = element.split()
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tid = element.parent['tid']
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ind = tags_tids.index(tid)
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for _ in text:
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w_t.append(ind)
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cnt += 1
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assert cnt == len(w_t)
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w_t.append(len(t_w))
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w_t.append(len(t_w) + 1)
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return w_t
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def subtoken_tag_offset(html, s_tok):
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w_t = word_tag_offset(html)
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s_t = []
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unique_tids = set()
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for i in range(len(s_tok)):
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s_t.append(w_t[s_tok[i]])
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unique_tids.add(w_t[s_tok[i]])
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return s_t, unique_tids
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examples = []
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all_tag_list = set()
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total_num = sum([len(entry["websites"]) for entry in input_data])
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with tqdm(total=total_num, desc="Converting websites to examples") as t:
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for entry in input_data:
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domain = entry["domain"]
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for website in entry["websites"]:
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# Generate Doc Tokens
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page_id = website["page_id"]
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curr_dir = osp.join(root_dir, domain, page_id[0:2], 'processed_data')
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html_fn = osp.join(curr_dir, page_id + '.html')
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html_file = open(html_fn).read()
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html_code = bs(html_file, "html.parser")
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raw_text_list, tag_num = html_to_text_list(html_code) # 字符列表及标签数
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doc_tokens = []
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char_to_word_offset = []
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page_text = ' '.join(raw_text_list)
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prev_is_whitespace = True
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for c in page_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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doc_tokens.append('no')
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char_to_word_offset.append(len(doc_tokens) - 1)
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doc_tokens.append('yes')
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char_to_word_offset.append(len(doc_tokens) - 1)
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tag_list = []
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assert len(doc_tokens) == char_to_word_offset[-1] + 1, (len(doc_tokens), char_to_word_offset[-1])
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if simplify:
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for qa in website["qas"]:
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qas_id = qa["id"]
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example = SRCExample(doc_tokens=doc_tokens, qas_id=qas_id, tag_num=tag_num)
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examples.append(example)
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t.update(1)
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else:
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# Tokenize all doc tokens
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# tokenize sth like < / >
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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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if token in tag_list:
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sub_tokens = [token]
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else:
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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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# Generate extra information for features
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tok_to_tags_index, unique_tids = subtoken_tag_offset(html_code, tok_to_orig_index)
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xpath_tag_map, xpath_subs_map = get_xpath_and_treeid4tokens(html_code,
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unique_tids,
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max_depth=max_depth)
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assert tok_to_tags_index[-1] == tag_num - 1, (tok_to_tags_index[-1], tag_num - 1)
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|
|
|
# Process each qas, which is mainly calculate the answer position
|
|
for qa in website["qas"]:
|
|
qas_id = qa["id"]
|
|
question_text = qa["question"]
|
|
start_position = None
|
|
end_position = None
|
|
orig_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]
|
|
orig_answer_text = answer["text"]
|
|
if answer["element_id"] == -1:
|
|
num_char = len(char_to_word_offset) - 2
|
|
else:
|
|
num_char = calc_num_from_raw_text_list(e_id_to_t_id(answer["element_id"], html_code),
|
|
raw_text_list)
|
|
answer_offset = num_char + answer["answer_start"]
|
|
answer_length = len(orig_answer_text) if answer["element_id"] != -1 else 1
|
|
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([w for w in doc_tokens[start_position:(end_position + 1)]
|
|
if (w[0] != '<' or w[-1] != '>')])
|
|
cleaned_answer_text = " ".join(whitespace_tokenize(orig_answer_text))
|
|
if actual_text.find(cleaned_answer_text) == -1:
|
|
logger.warning("Could not find answer of question %s: '%s' vs. '%s'",
|
|
qa['id'], actual_text, cleaned_answer_text)
|
|
continue
|
|
|
|
example = SRCExample(
|
|
doc_tokens=doc_tokens,
|
|
qas_id=qas_id,
|
|
tag_num=tag_num,
|
|
question_text=question_text,
|
|
html_code=html_code,
|
|
orig_answer_text=orig_answer_text,
|
|
start_position=start_position,
|
|
end_position=end_position,
|
|
tok_to_orig_index=tok_to_orig_index,
|
|
orig_to_tok_index=orig_to_tok_index,
|
|
all_doc_tokens=all_doc_tokens,
|
|
tok_to_tags_index=tok_to_tags_index,
|
|
xpath_tag_map=xpath_tag_map,
|
|
xpath_subs_map=xpath_subs_map,
|
|
)
|
|
|
|
examples.append(example)
|
|
|
|
t.update(1)
|
|
return examples, all_tag_list
|
|
|
|
|
|
def convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training,
|
|
cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
|
|
sequence_a_segment_id=0, sequence_b_segment_id=1,
|
|
cls_token_segment_id=0, pad_token_segment_id=0,
|
|
mask_padding_with_zero=True, max_depth=50):
|
|
r"""
|
|
Converting the SRC Examples further into the features for all the input doc spans.
|
|
|
|
Arguments:
|
|
examples (list[SRCExample]): the list of SRC Examples to process.
|
|
tokenizer (Tokenizer): the tokenizer for PLM in use.
|
|
max_seq_length (int): the max length of the total sub-token sequence, including the question, cls token, sep
|
|
tokens, and documents; if the length of the input is bigger than max_seq_length, the input
|
|
will be cut into several doc spans.
|
|
doc_stride (int): the stride length when the input is cut into several doc spans.
|
|
max_query_length (int): the max length of the sub-token sequence of the questions; the question will be truncate
|
|
if it is longer than max_query_length.
|
|
is_training (bool): True if processing the training set, else False.
|
|
cls_token (str): the cls token in use, default is '[CLS]'.
|
|
sep_token (str): the sep token in use, default is '[SEP]'.
|
|
pad_token (int): the id of the padding token in use when the total sub-token length is smaller that
|
|
max_seq_length, default is 0 which corresponding to the '[PAD]' token.
|
|
sequence_a_segment_id: the segment id for the first sequence (the question), default is 0.
|
|
sequence_b_segment_id: the segment id for the second sequence (the html file), default is 1.
|
|
cls_token_segment_id: the segment id for the cls token, default is 0.
|
|
pad_token_segment_id: the segment id for the padding tokens, default is 0.
|
|
mask_padding_with_zero: determine the pattern of the returned input mask; 0 for padding tokens and 1 for others
|
|
when True, and vice versa.
|
|
Returns:
|
|
list[InputFeatures]: the resulting input features for all the input doc spans
|
|
"""
|
|
|
|
pad_x_tag_seq = [216] * max_depth
|
|
pad_x_subs_seq = [1001] * max_depth
|
|
|
|
unique_id = 1000000000
|
|
features = []
|
|
for (example_index, example) in enumerate(tqdm(examples, desc="Converting examples to features")):
|
|
|
|
xpath_tag_map = example.xpath_tag_map
|
|
xpath_subs_map = example.xpath_subs_map
|
|
|
|
query_tokens = tokenizer.tokenize(example.question_text)
|
|
if len(query_tokens) > max_query_length:
|
|
query_tokens = query_tokens[0:max_query_length]
|
|
|
|
tok_start_position = None
|
|
tok_end_position = None
|
|
if is_training:
|
|
tok_start_position = example.orig_to_tok_index[example.start_position]
|
|
if example.end_position < len(example.doc_tokens) - 1:
|
|
tok_end_position = example.orig_to_tok_index[example.end_position + 1] - 1
|
|
else:
|
|
tok_end_position = len(example.all_doc_tokens) - 1
|
|
(tok_start_position, tok_end_position) = _improve_answer_span(
|
|
example.all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
|
|
example.orig_answer_text)
|
|
|
|
# The -3 accounts for [CLS], [SEP] and [SEP]
|
|
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
|
|
|
|
# We can have documents that are longer than the maximum sequence length.
|
|
# To deal with this we do a sliding window approach, where we take chunks
|
|
# of the up to our max length with a stride of `doc_stride`.
|
|
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
|
|
"DocSpan", ["start", "length"])
|
|
doc_spans = []
|
|
start_offset = 0
|
|
while start_offset < len(example.all_doc_tokens):
|
|
length = len(example.all_doc_tokens) - start_offset
|
|
if length > max_tokens_for_doc:
|
|
length = max_tokens_for_doc
|
|
doc_spans.append(_DocSpan(start=start_offset, length=length))
|
|
if start_offset + length == len(example.all_doc_tokens):
|
|
break
|
|
start_offset += min(length, doc_stride)
|
|
|
|
for (doc_span_index, doc_span) in enumerate(doc_spans):
|
|
tokens = []
|
|
token_to_orig_map = {}
|
|
token_is_max_context = {}
|
|
segment_ids = []
|
|
token_to_tag_index = []
|
|
|
|
# CLS token at the beginning
|
|
tokens.append(cls_token)
|
|
segment_ids.append(cls_token_segment_id)
|
|
token_to_tag_index.append(example.tag_num)
|
|
|
|
# Query
|
|
tokens += query_tokens
|
|
segment_ids += [sequence_a_segment_id] * len(query_tokens)
|
|
token_to_tag_index += [example.tag_num] * len(query_tokens)
|
|
|
|
# SEP token
|
|
tokens.append(sep_token)
|
|
segment_ids.append(sequence_a_segment_id)
|
|
token_to_tag_index.append(example.tag_num)
|
|
|
|
# Paragraph
|
|
for i in range(doc_span.length):
|
|
split_token_index = doc_span.start + i
|
|
token_to_orig_map[len(tokens)] = example.tok_to_orig_index[split_token_index]
|
|
token_to_tag_index.append(example.tok_to_tags_index[split_token_index])
|
|
|
|
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
|
|
split_token_index)
|
|
token_is_max_context[len(tokens)] = is_max_context
|
|
tokens.append(example.all_doc_tokens[split_token_index])
|
|
segment_ids.append(sequence_b_segment_id)
|
|
paragraph_len = doc_span.length
|
|
|
|
# SEP token
|
|
tokens.append(sep_token)
|
|
segment_ids.append(sequence_b_segment_id)
|
|
token_to_tag_index.append(example.tag_num)
|
|
|
|
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
# The mask has 1 for real tokens and 0 for padding tokens. Only real
|
|
# tokens are attended to.
|
|
input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
|
|
|
|
# Zero-pad up to the sequence length.
|
|
while len(input_ids) < max_seq_length:
|
|
input_ids.append(pad_token)
|
|
input_mask.append(0 if mask_padding_with_zero else 1)
|
|
segment_ids.append(pad_token_segment_id)
|
|
token_to_tag_index.append(example.tag_num)
|
|
|
|
assert len(input_ids) == max_seq_length
|
|
assert len(input_mask) == max_seq_length
|
|
assert len(segment_ids) == max_seq_length
|
|
assert len(token_to_tag_index) == max_seq_length
|
|
|
|
span_is_impossible = False
|
|
start_position = None
|
|
end_position = None
|
|
if is_training:
|
|
# For training, if our document chunk does not contain an annotation
|
|
# we throw it out, since there is nothing to predict.
|
|
doc_start = doc_span.start
|
|
doc_end = doc_span.start + doc_span.length - 1
|
|
out_of_span = False
|
|
if not (tok_start_position >= doc_start and
|
|
tok_end_position <= doc_end):
|
|
out_of_span = True
|
|
if out_of_span:
|
|
span_is_impossible = True
|
|
start_position = 0
|
|
end_position = 0
|
|
else:
|
|
doc_offset = len(query_tokens) + 2
|
|
start_position = tok_start_position - doc_start + doc_offset
|
|
end_position = tok_end_position - doc_start + doc_offset
|
|
|
|
|
|
xpath_tags_seq = [xpath_tag_map.get(tid, pad_x_tag_seq) for tid in token_to_tag_index] # ok
|
|
xpath_subs_seq = [xpath_subs_map.get(tid, pad_x_subs_seq) for tid in token_to_tag_index] # ok
|
|
|
|
# we need to get extended_attention_mask
|
|
|
|
features.append(
|
|
InputFeatures(
|
|
unique_id=unique_id,
|
|
example_index=example_index,
|
|
page_id=example.qas_id[:-5],
|
|
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,
|
|
paragraph_len=paragraph_len,
|
|
start_position=start_position,
|
|
end_position=end_position,
|
|
token_to_tag_index=token_to_tag_index,
|
|
is_impossible=span_is_impossible,
|
|
xpath_tags_seq=xpath_tags_seq,
|
|
xpath_subs_seq=xpath_subs_seq,
|
|
))
|
|
unique_id += 1
|
|
|
|
return features
|
|
|
|
|
|
# ---------- copied ! --------------
|
|
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer, orig_answer_text):
|
|
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
|
|
|
|
for new_start in range(input_start, input_end + 1):
|
|
for new_end in range(input_end, new_start - 1, -1):
|
|
text_span = " ".join([w for w in doc_tokens[new_start:(new_end + 1)]
|
|
if w[0] != '<' or w[-1] != '>'])
|
|
if text_span == tok_answer_text:
|
|
return new_start, new_end
|
|
|
|
return input_start, input_end
|
|
|
|
|
|
# ---------- copied ! --------------
|
|
def _check_is_max_context(doc_spans, cur_span_index, position):
|
|
best_score = None
|
|
best_span_index = None
|
|
for (span_index, doc_span) in enumerate(doc_spans):
|
|
end = doc_span.start + doc_span.length - 1
|
|
if position < doc_span.start:
|
|
continue
|
|
if position > end:
|
|
continue
|
|
num_left_context = position - doc_span.start
|
|
num_right_context = end - position
|
|
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
|
|
if best_score is None or score > best_score:
|
|
best_score = score
|
|
best_span_index = span_index
|
|
|
|
return cur_span_index == best_span_index
|
|
|
|
|
|
RawResult = collections.namedtuple("RawResult",
|
|
["unique_id", "start_logits", "end_logits"])
|
|
|
|
|
|
def write_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case,
|
|
output_prediction_file, output_tag_prediction_file,
|
|
output_nbest_file, verbose_logging, tokenizer):
|
|
r"""
|
|
Compute and write down the final results, including the n best results.
|
|
|
|
Arguments:
|
|
all_examples (list[SRCExample]): all the SRC Example of the dataset; note that we only need it to provide the
|
|
mapping from example index to the question-answers id.
|
|
all_features (list[InputFeatures]): all the features for the input doc spans.
|
|
all_results (list[RawResult]): all the results from the models.
|
|
n_best_size (int): the number of the n best buffer and the final n best result saved.
|
|
max_answer_length (int): constrain the model to predict the answer no longer than it.
|
|
do_lower_case (bool): whether the model distinguish upper and lower case of the letters.
|
|
output_prediction_file (str): the file which the best answer text predictions will be written to.
|
|
output_tag_prediction_file (str): the file which the best answer tag predictions will be written to.
|
|
output_nbest_file (str): the file which the n best answer predictions including text, tag, and probabilities
|
|
will be written to.
|
|
verbose_logging (bool): if true, all of the warnings related to data processing will be printed.
|
|
"""
|
|
logger.info("Writing predictions to: %s" % output_prediction_file)
|
|
logger.info("Writing nbest to: %s" % output_nbest_file)
|
|
|
|
example_index_to_features = collections.defaultdict(list)
|
|
for feature in all_features:
|
|
example_index_to_features[feature.example_index].append(feature)
|
|
|
|
unique_id_to_result = {}
|
|
for result in all_results:
|
|
unique_id_to_result[result.unique_id] = result
|
|
|
|
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"PrelimPrediction",
|
|
["feature_index", "start_index", "end_index", "start_logit", "end_logit", "tag_ids"])
|
|
|
|
all_predictions = collections.OrderedDict()
|
|
all_tag_predictions = collections.OrderedDict()
|
|
all_nbest_json = collections.OrderedDict()
|
|
|
|
for (example_index, example) in enumerate(all_examples):
|
|
features = example_index_to_features[example_index]
|
|
|
|
prelim_predictions = []
|
|
|
|
for (feature_index, feature) in enumerate(features):
|
|
|
|
result = unique_id_to_result[feature.unique_id]
|
|
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
|
|
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
|
|
# if we could have irrelevant answers, get the min score of irrelevant
|
|
for start_index in start_indexes:
|
|
for end_index in end_indexes:
|
|
# We could hypothetically create invalid predictions, e.g., predict
|
|
# that the start of the span is in the question. We throw out all
|
|
# invalid predictions.
|
|
if start_index >= len(feature.tokens):
|
|
continue
|
|
if end_index >= len(feature.tokens):
|
|
continue
|
|
if start_index not in feature.token_to_orig_map:
|
|
continue
|
|
if end_index not in feature.token_to_orig_map:
|
|
continue
|
|
if not feature.token_is_max_context.get(start_index, False):
|
|
continue
|
|
if end_index < start_index:
|
|
continue
|
|
length = end_index - start_index + 1
|
|
if length > max_answer_length:
|
|
continue
|
|
tag_ids = set(feature.token_to_tag_index[start_index: end_index + 1])
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_logit=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index],
|
|
tag_ids=list(tag_ids)))
|
|
prelim_predictions = sorted(
|
|
prelim_predictions,
|
|
key=lambda x: (x.start_logit + x.end_logit),
|
|
reverse=True)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_logit", "end_logit", "tag_ids"])
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
if pred.start_index > 0: # this is a non-null prediction
|
|
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
|
|
tok_text = " ".join(tok_tokens)
|
|
|
|
# De-tokenize WordPieces that have been split off.
|
|
tok_text = tok_text.replace(" ##", "")
|
|
tok_text = tok_text.replace("##", "")
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
final_text = _get_final_text(tok_text, orig_text, do_lower_case, verbose_logging)
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
else:
|
|
final_text = ""
|
|
seen_predictions[final_text] = True
|
|
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text=final_text,
|
|
start_logit=pred.start_logit,
|
|
end_logit=pred.end_logit,
|
|
tag_ids=pred.tag_ids))
|
|
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(
|
|
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0, tag_ids=[-1]))
|
|
|
|
assert len(nbest) >= 1
|
|
|
|
total_scores = []
|
|
best_non_null_entry = None
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_logit + entry.end_logit)
|
|
if not best_non_null_entry:
|
|
if entry.text:
|
|
best_non_null_entry = entry
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for (i, entry) in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_logit"] = entry.start_logit
|
|
output["end_logit"] = entry.end_logit
|
|
output["tag_ids"] = entry.tag_ids
|
|
nbest_json.append(output)
|
|
|
|
assert len(nbest_json) >= 1
|
|
|
|
best = nbest_json[0]["text"].split()
|
|
best = ' '.join([w for w in best
|
|
if (w[0] != '<' or w[-1] != '>')
|
|
and w != "<end-of-node>"
|
|
and w != tokenizer.sep_token
|
|
and w != tokenizer.cls_token])
|
|
all_predictions[example.qas_id] = best
|
|
all_tag_predictions[example.qas_id] = nbest_json[0]["tag_ids"]
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
with open(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with open(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
with open(output_tag_prediction_file, 'w') as writer:
|
|
writer.write(json.dumps(all_tag_predictions, indent=4) + '\n')
|
|
return
|
|
|
|
|
|
def _get_final_text(pred_text, orig_text, do_lower_case, verbose_logging=False):
|
|
def _strip_spaces(text):
|
|
ns_chars = []
|
|
ns_to_s_map = collections.OrderedDict()
|
|
for (i, c) in enumerate(text):
|
|
if c == " ":
|
|
continue
|
|
ns_to_s_map[len(ns_chars)] = i
|
|
ns_chars.append(c)
|
|
ns_text = "".join(ns_chars)
|
|
return ns_text, ns_to_s_map
|
|
|
|
# We first tokenize `orig_text`, strip whitespace from the result
|
|
# and `pred_text`, and check if they are the same length. If they are
|
|
# NOT the same length, the heuristic has failed. If they are the same
|
|
# length, we assume the characters are one-to-one aligned.
|
|
tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
|
|
|
|
tok_text = " ".join(tokenizer.tokenize(orig_text))
|
|
|
|
start_position = tok_text.find(pred_text)
|
|
if start_position == -1:
|
|
if verbose_logging:
|
|
logger.info(
|
|
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
|
|
return orig_text
|
|
end_position = start_position + len(pred_text) - 1
|
|
|
|
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
|
|
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
|
|
|
|
if len(orig_ns_text) != len(tok_ns_text):
|
|
if verbose_logging:
|
|
logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
|
|
orig_ns_text, tok_ns_text)
|
|
return orig_text
|
|
|
|
# We then project the characters in `pred_text` back to `orig_text` using
|
|
# the character-to-character alignment.
|
|
tok_s_to_ns_map = {}
|
|
for (i, tok_index) in tok_ns_to_s_map.items():
|
|
tok_s_to_ns_map[tok_index] = i
|
|
|
|
orig_start_position = None
|
|
if start_position in tok_s_to_ns_map:
|
|
ns_start_position = tok_s_to_ns_map[start_position]
|
|
if ns_start_position in orig_ns_to_s_map:
|
|
orig_start_position = orig_ns_to_s_map[ns_start_position]
|
|
|
|
if orig_start_position is None:
|
|
if verbose_logging:
|
|
logger.info("Couldn't map start position")
|
|
return orig_text
|
|
|
|
orig_end_position = None
|
|
if end_position in tok_s_to_ns_map:
|
|
ns_end_position = tok_s_to_ns_map[end_position]
|
|
if ns_end_position in orig_ns_to_s_map:
|
|
orig_end_position = orig_ns_to_s_map[ns_end_position]
|
|
|
|
if orig_end_position is None:
|
|
if verbose_logging:
|
|
logger.info("Couldn't map end position")
|
|
return orig_text
|
|
|
|
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
|
|
return output_text
|
|
|
|
|
|
def _get_best_indexes(logits, n_best_size):
|
|
"""Get the n-best logits from a list."""
|
|
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
|
|
|
|
best_indexes = []
|
|
for i in range(len(index_and_score)):
|
|
if i >= n_best_size:
|
|
break
|
|
best_indexes.append(index_and_score[i][0])
|
|
return best_indexes
|
|
|
|
|
|
def _compute_softmax(scores):
|
|
"""Compute softmax probability over raw logits."""
|
|
if not scores:
|
|
return []
|
|
|
|
max_score = None
|
|
for score in scores:
|
|
if max_score is None or score > max_score:
|
|
max_score = score
|
|
|
|
exp_scores = []
|
|
total_sum = 0.0
|
|
for score in scores:
|
|
x = math.exp(score - max_score)
|
|
exp_scores.append(x)
|
|
total_sum += x
|
|
|
|
probs = []
|
|
for score in exp_scores:
|
|
probs.append(score / total_sum)
|
|
return probs
|