def _predict(self, strings):
sequences = [
encode_sentencepiece(
self._tokenizer.sp,
string,
return_unicode=False,
sample=False,
) for string in strings
]
batch_x = [self._tokenizer.encode(string) + [1] for string in strings]
batch_x = padding_sequence(batch_x)
r = self._execute(
inputs=[batch_x],
input_labels=['x_placeholder'],
output_labels=['greedy', 'tag_greedy'],
)
p, tag = r['greedy'], r['tag_greedy']
results = []
nonzero = (p != 0).sum(axis=-1)
for i in range(len(p)):
r = self._tokenizer.decode(p[i].tolist())
t = tag[i, :nonzero[i]]
s = encode_sentencepiece(self._tokenizer.sp,
r,
return_unicode=False,
sample=False)
merged = merge_sentencepiece_tokens_tagging(s + ['<cls>'],
t,
model='xlnet')
results.append(list(zip(merged[0], merged[1])))
return results
def convert_examples_to_features_bert(
examples,
tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=False,
do_lower_case=False,
):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
unique_id = 1000000000
max_n, max_m = 1024, 1024
f = np.zeros((max_n, max_m), dtype=np.float32)
features = []
for n in range(len(examples)):
example_index = n
example = examples[n]
query_tokens = encode_sentencepiece_ids(
tokenizer,
preprocess_text(example.question_text, lower=do_lower_case),
)
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = encode_sentencepiece(
tokenizer.sp_model,
preprocess_text(example.paragraph_text, lower=do_lower_case),
return_unicode=False,
)
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
para_tokens = [
six.ensure_text(token, 'utf-8') for token in para_tokens
]
for i, token in enumerate(para_tokens):
new_token = six.ensure_text(token).replace(SPIECE_UNDERLINE, ' ')
chartok_to_tok_index.extend([i] * len(new_token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(new_token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = ''.join(para_tokens).replace(SPIECE_UNDERLINE, ' ')
n, m = len(paragraph_text), len(tok_cat_text)
if n > max_n or m > max_m:
max_n = max(n, max_n)
max_m = max(m, max_m)
f = np.zeros((max_n, max_m), dtype=np.float32)
g = {}
def _lcs_match(max_dist, n=n, m=m):
f.fill(0)
g.clear()
for i in range(n):
for j in range(i - max_dist, i + max_dist):
if j >= m or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (preprocess_text(
paragraph_text[i],
lower=do_lower_case,
remove_space=False,
) == tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
max_dist = abs(n - m) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[n - 1, m - 1] > 0.8 * n:
break
max_dist *= 2
orig_to_chartok_index = [None] * n
chartok_to_orig_index = [None] * m
i, j = n - 1, m - 1
while i >= 0 and j >= 0:
if (i, j) not in g:
break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index)
or f[n - 1, m - 1] < 0.8 * n):
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(chartok_to_orig_index,
start_chartok_pos,
n,
is_start=True)
end_orig_pos = _convert_index(chartok_to_orig_index,
end_chartok_pos,
n,
is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = 0
tok_end_position = 0
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(orig_to_chartok_index,
start_position,
is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(orig_to_chartok_index,
end_position,
is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
if six.PY2 and isinstance(x, six.text_type):
x = six.ensure_binary(x, 'utf-8')
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
_DocSpan = collections.namedtuple('DocSpan', ['start', 'length'])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(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(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
p_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
tokens.append(tokenizer.sp_model.PieceToId('[CLS]'))
segment_ids.append(0)
p_mask.append(0)
for token in query_tokens:
tokens.append(token)
segment_ids.append(0)
p_mask.append(1)
tokens.append(tokenizer.sp_model.PieceToId('[SEP]'))
segment_ids.append(0)
p_mask.append(1)
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_to_orig_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(all_doc_tokens[split_token_index])
segment_ids.append(1)
p_mask.append(0)
tokens.append(tokenizer.sp_model.PieceToId('[SEP]'))
segment_ids.append(1)
p_mask.append(1)
paragraph_len = len(tokens)
input_ids = tokens
input_mask = [1] * len(input_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
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:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
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
if is_training and span_is_impossible:
start_position = 0
end_position = 0
if example_index < 20:
if is_training and not span_is_impossible:
pieces = [
tokenizer.sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
p_mask=p_mask,
)
features.append(feature)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
return features
def convert_examples_to_features_xlnet(
examples,
tokenizer,
max_seq_length=384,
doc_stride=128,
max_query_length=64,
is_training=False,
):
cnt_pos, cnt_neg = 0, 0
unique_id = 1000000000
max_N, max_M = 1024, 1024
f = np.zeros((max_N, max_M), dtype=np.float32)
features = []
for n in range(len(examples)):
example_index = n
example = examples[n]
query_tokens = encode_sentencepiece_ids(
tokenizer, preprocess_text(example.question_text, lower=False))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = encode_sentencepiece(
tokenizer.sp_model,
preprocess_text(example.paragraph_text, lower=False))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
chartok_to_tok_index.extend([i] * len(token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = ''.join(para_tokens).replace(SPIECE_UNDERLINE, ' ')
N, M = len(paragraph_text), len(tok_cat_text)
if N > max_N or M > max_M:
max_N = max(N, max_N)
max_M = max(M, max_M)
f = np.zeros((max_N, max_M), dtype=np.float32)
gc.collect()
g = {}
def _lcs_match(max_dist):
f.fill(0)
g.clear()
for i in range(N):
for j in range(i - max_dist, i + max_dist):
if j >= M or j < 0:
continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (preprocess_text(
paragraph_text[i],
lower=False,
remove_space=False,
) == tok_cat_text[j] and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
max_dist = abs(N - M) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[N - 1, M - 1] > 0.8 * N:
break
max_dist *= 2
orig_to_chartok_index = [None] * N
chartok_to_orig_index = [None] * M
i, j = N - 1, M - 1
while i >= 0 and j >= 0:
if (i, j) not in g:
break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if (all(v is None for v in orig_to_chartok_index)
or f[N - 1, M - 1] < 0.8 * N):
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(chartok_to_orig_index,
start_chartok_pos,
N,
is_start=True)
end_orig_pos = _convert_index(chartok_to_orig_index,
end_chartok_pos,
N,
is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(orig_to_chartok_index,
start_position,
is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(orig_to_chartok_index,
end_position,
is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
if six.PY2 and isinstance(x, unicode):
x = x.encode('utf-8')
return tokenizer.sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
_DocSpan = collections.namedtuple('DocSpan', ['start', 'length'])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(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(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
p_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_to_orig_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(all_doc_tokens[split_token_index])
segment_ids.append(SEG_ID_P)
p_mask.append(0)
paragraph_len = len(tokens)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_P)
p_mask.append(1)
for token in query_tokens:
tokens.append(token)
segment_ids.append(SEG_ID_Q)
p_mask.append(1)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_Q)
p_mask.append(1)
cls_index = len(segment_ids)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
p_mask.append(0)
input_ids = tokens
input_mask = [0] * len(input_ids)
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(1)
segment_ids.append(SEG_ID_PAD)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(p_mask) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
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:
start_position = 0
end_position = 0
span_is_impossible = True
else:
doc_offset = 0
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and span_is_impossible:
start_position = cls_index
end_position = cls_index
if example_index < 20:
if is_training and span_is_impossible:
pass
if is_training and not span_is_impossible:
pieces = [
tokenizer.sp_model.IdToPiece(token)
for token in tokens[start_position:(end_position + 1)]
]
answer_text = tokenizer.sp_model.DecodePieces(pieces)
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
tokens=[tokenizer.sp_model.IdToPiece(x) for x in tokens],
input_ids=input_ids,
input_mask=input_mask,
p_mask=p_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
cls_index=cls_index,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible,
)
features.append(feature)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
return features