Kaggle competition: TensorFlow 2.0 Question Answering
<SOS> metric: NQMicroF1 Kaggle data source: tensorflow-2.0-question-answering<data_type_conversions>
!pip install --upgrade.. /input/tokenizers-repo/tokenizers-0.0.11-cp36-cp36m-manylinux1_x86_64.whl
!pip install --upgrade.. /input/sacremoses-repo/sacremoses-0.0.35-py3-none-any.whl
!pip install --upgrade.. /input/transformers-repo/transformers-2.3.0-py3-none-any.whl
train['Census_OSBuildNumber'] = train['Census_OSBuildNumber'].astype('category')
 test['Census_OSBuildNumber'] = test['Census_OSBuildNumber'].astype('category' )<data_type_conversions>


logger = logging.getLogger(__name__)
 NQExample = collections.namedtuple("NQExample", [
"qas_id", "question_text", "doc_tokens", "orig_answer_text",
"start_position", "end_position", "long_position",
"short_is_impossible", "long_is_impossible", "crop_start"])
 Crop = collections.namedtuple("Crop", ["unique_id", "example_index", "doc_span_index",
"tokens", "token_to_orig_map", "token_is_max_context",
"input_ids", "attention_mask", "token_type_ids",
"paragraph_len", "start_position", "end_position", "long_position",
"short_is_impossible", "long_is_impossible"])
 LongAnswerCandidate = collections.namedtuple('LongAnswerCandidate', [
'start_token', 'end_token', 'top_level'])
 UNMAPPED = -123
CLS_INDEX = 0
def get_add_tokens(do_enumerate):
tags = ['Dd', 'Dl', 'Dt', 'H1', 'H2', 'H3', 'Li', 'Ol', 'P', 'Table', 'Td', 'Th', 'Tr', 'Ul']
opening_tags = [f'<{tag}>' for tag in tags]
closing_tags = [f'</{tag}>' for tag in tags]
added_tags = opening_tags + closing_tags
special_tokens = ['<P>', '<Table>']
if do_enumerate:
for special_token in special_tokens:
for j in range(11):
added_tags.append(f'<{special_token[1: -1]}{j}>')
 add_tokens = ['Td_colspan', 'Th_colspan', '``',