def test_lowercase_empty_string(self):
txt = [
"",
]
expected = [
"",
]
self.assertAllEqual(expected, text.case_fold_utf8(txt))
def test_lowercase_one_string(self):
txt = [
" TExt to loWERcase! ",
]
expected = [
" text to lowercase! ",
]
self.assertAllEqual(expected, text.case_fold_utf8(txt))
def preprocess(text):
"""Normalize the text, and return tokens."""
assert len(text.get_shape().as_list()) == 2
assert text.get_shape().as_list()[-1] == 1
text = tf.reshape(text, [-1])
text = tf_text.case_fold_utf8(text)
tokenizer = tflite_text_api.WhitespaceTokenizer()
return tokenizer.tokenize(text)
def test_lowercase_text(self):
txt = [
"Punctuation and digits: -*/+$#%@%$123456789#^$*%&",
"Non-latin UTF8 chars: ΘͽʦȺЩ",
"Accented chars: ĎÔPQRŔSŠoóôpqrŕsštťuúvwxyý",
"Non-UTF8-letters: e.g. ◆, ♥, and the emoji symbol ( ͡° ͜ʖ ͡°)",
"Folded: ßς", ""
]
expected = [
"punctuation and digits: -*/+$#%@%$123456789#^$*%&",
"non-latin utf8 chars: θͽʦⱥщ",
"accented chars: ďôpqrŕsšoóôpqrŕsštťuúvwxyý",
"non-utf8-letters: e.g. ◆, ♥, and the emoji symbol ( ͡° ͜ʖ ͡°)",
"folded: ssσ", ""
]
self.assertAllEqual(expected, text.case_fold_utf8(txt))
def basic_tokenize(text_input, lower_case=False, keep_whitespace=False):
"""Performs basic word tokenization for BERT.
Args:
text_input: A Tensor of untokenized strings.
lower_case: A bool indicating whether or not to perform lowercasing. Default
is False.
keep_whitespace: A bool indicating whether or not whitespace tokens should
be kept in the output
"""
# lowercase and strip accents (if option is set)
if lower_case:
text_input = tf_text.case_fold_utf8(text_input)
# normalize by NFD
text_input = tf_text.normalize_utf8(text_input, "NFD")
# strip out control characters
text_input = tf.strings.regex_replace(text_input, r"\p{Cc}|\p{Cf}|\p{Mn}",
"")
# For chinese and emoji characters, tokenize by unicode codepoints
script_tokenized = tf_text.unicode_script_tokenize(
text_input, keep_whitespace=keep_whitespace, name="UTF-8")
token_script_ids = tf.strings.unicode_script(
tf.strings.unicode_decode(script_tokenized.flat_values, "UTF-8"))
is_chinese = tf.equal(token_script_ids, _CHINESE_SCRIPT_ID)[:, :1].values
is_emoji = tf_text.wordshape(script_tokenized.flat_values,
tf_text.WordShape.HAS_EMOJI)
is_punct = tf_text.wordshape(script_tokenized.flat_values,
tf_text.WordShape.IS_PUNCT_OR_SYMBOL)
split_cond = is_chinese | is_emoji | is_punct
unicode_char_split = tf.strings.unicode_split(script_tokenized, "UTF-8")
unicode_split_tokens = tf.where(split_cond,
y=tf.expand_dims(
script_tokenized.flat_values, 1),
x=unicode_char_split.values)
# Pack back into a [batch, (num_tokens), (num_unicode_chars)] RT
chinese_mix_tokenized = tf.RaggedTensor.from_row_lengths(
values=unicode_split_tokens,
row_lengths=script_tokenized.row_lengths())
# Squeeze out to a [batch, (num_tokens)] RT
return collapse_dims(chinese_mix_tokenized)
def call(self, inputs: tf.Tensor):
"""Calls `text.SentencepieceTokenizer` on inputs.
Args:
inputs: A string Tensor of shape `(batch_size,)`.
Returns:
One or three of RaggedTensors if tokenize_with_offsets is False or True,
respectively. These are
tokens: A RaggedTensor of shape `[batch_size, (pieces)]` and type `int32`.
`tokens[i,j]` contains the j-th piece in the i-th input.
start_offsets, limit_offsets: If `tokenize_with_offsets` is True,
RaggedTensors of type `int64` with the same indices as tokens.
Element `[i,j]` contains the byte offset at the start, or past the
end, resp., for the j-th piece in the i-th input.
"""
if self._strip_diacritics:
if self.tokenize_with_offsets:
raise ValueError(
"`tokenize_with_offsets` is not supported yet when "
"`strip_diacritics` is set to True (b/181866850).")
inputs = text.normalize_utf8(inputs, "NFD")
inputs = tf.strings.regex_replace(inputs, r"\p{Mn}", "")
if self._lower_case:
inputs = text.case_fold_utf8(inputs)
# Prepare to reshape the result to work around broken shape inference.
batch_size = tf.shape(inputs)[0]
def _reshape(rt):
values = rt.values
row_splits = rt.row_splits
row_splits = tf.reshape(row_splits, [batch_size + 1])
return tf.RaggedTensor.from_row_splits(values, row_splits)
# Call the tokenizer.
if self.tokenize_with_offsets:
tokens, start_offsets, limit_offsets = (
self._tokenizer.tokenize_with_offsets(inputs))
return _reshape(tokens), _reshape(start_offsets), _reshape(
limit_offsets)
else:
tokens = self._tokenizer.tokenize(inputs)
return _reshape(tokens)
def __call__(self, x):
# Constrained sequence
cs_scores = np.array([[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0,
10.0]])
cs_input = np.array([cs_scores, cs_scores, cs_scores],
dtype=np.float32)
cs_transition_weights = np.array(
[[-1.0, 1.0, -2.0, 2.0, 0.0], [3.0, -3.0, 4.0, -4.0, 0.0],
[5.0, 1.0, 10.0, 1.0, 1.0], [-7.0, 7.0, -8.0, 8.0, 0.0],
[0.0, 1.0, 2.0, 3.0, 0.0]],
dtype=np.float32)
cs_allowed_transitions = np.array([[True, True, True, True, True],
[True, True, True, True, True],
[True, False, True, False, False],
[True, True, True, True, True],
[True, False, True, True, True]])
constrained_sequence = text.viterbi_constrained_sequence(
cs_input, [2, 2, 2],
allowed_transitions=cs_allowed_transitions,
transition_weights=cs_transition_weights,
use_log_space=True,
use_start_and_end_states=True)
# Max Spanning Tree
mst_num_nodes = tf.constant([4, 3], tf.int32)
mst_scores = tf.constant(
[[[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 4]],
[[4, 3, 2, 9], [0, 0, 2, 9], [0, 0, 0, 9], [9, 9, 9, 9]]],
tf.int32) # pyformat: disable
(max_spanning_tree,
_) = text.max_spanning_tree(mst_num_nodes, mst_scores)
# Normalize
normalized = text.case_fold_utf8(['A String'])
normalized = text.normalize_utf8(normalized)
# Regex split
regex_split = text.regex_split(input=['Yo dawg!'],
delim_regex_pattern=r'\s')
# Rouge-L
rl_hypotheses = tf.ragged.constant(
[['captain', 'of', 'the', 'delta', 'flight'],
['the', '1990', 'transcript']])
rl_references = tf.ragged.constant(
[['delta', 'air', 'lines', 'flight'],
['this', 'concludes', 'the', 'transcript']])
(rouge_l, _, _) = text.metrics.rouge_l(rl_hypotheses, rl_references)
# Sentence breaking version 1 (token dependent)
sb_token_word = [['Welcome', 'to', 'the', 'U.S.', '!', 'Harry'],
['Wu', 'Tang', 'Clan', ';', 'ain\'t', 'nothing']]
sb_token_properties = [[0, 0, 0, 256, 0, 0], [0, 0, 0, 0, 0, 0]]
sb_token_starts = []
sb_token_ends = []
for sentence in sb_token_word:
sentence_string = ''
sentence_start = []
sentence_end = []
for word in sentence:
sentence_start.append(len(sentence_string))
sentence_string = sentence_string.join([word, ' '])
sentence_end.append(len(sentence_string))
sb_token_starts.append(sentence_start)
sb_token_ends.append(sentence_end)
sb_token_starts = tf.constant(sb_token_starts, dtype=tf.int64)
sb_token_ends = tf.constant(sb_token_ends, dtype=tf.int64)
sb_token_properties = tf.ragged.constant(sb_token_properties,
dtype=tf.int64)
(sentence_breaking, _, _,
_) = text.sentence_fragments(sb_token_word, sb_token_starts,
sb_token_ends, sb_token_properties)
# Sentence breaking version 2 (StateBasedSentenceBreaker)
sbv2_text_input = [['Welcome to the U.S.! Harry'],
['Wu Tang Clan; ain\'t nothing']]
sentence_breaker_v2 = text.StateBasedSentenceBreaker()
sbv2_fragment_text, _, _ = (
sentence_breaker_v2.break_sentences_with_offsets(sbv2_text_input))
# Sentencepiece tokenizer
sp_model_file = (
'third_party/tensorflow_text/python/ops/test_data/test_oss_model.model'
)
sp_model = open(sp_model_file, 'rb').read()
sp_tokenizer = text.SentencepieceTokenizer(sp_model)
sentencepiece = sp_tokenizer.tokenize(['A sentence of things.'])
sentencepiece = sp_tokenizer.detokenize(sentencepiece)
(sentencepiece, _,
_) = sp_tokenizer.tokenize_with_offsets(sentencepiece)
sentencepiece_size = sp_tokenizer.vocab_size()
sentencepiece_id = sp_tokenizer.id_to_string(1)
# Split merge tokenizer
sm_tokenizer = text.SplitMergeTokenizer()
split_merge = sm_tokenizer.tokenize(b'IloveFlume!',
[0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0])
# Split merge from logits tokenizer
smfl_tokenizer = text.SplitMergeFromLogitsTokenizer()
split_merge_from_logits = smfl_tokenizer.tokenize(
b'IloveFlume!',
# One pair of logits for each Unicode character from the text. Each
# pair indicates a "split" action if the first component is greater than
# the second one, and a "merge" otherwise.
[
[2.7, -0.3], # I: split
[4.1, 0.82], # l: split
[-2.3, 4.3], # o: merge
[3.1, 12.2], # v: merge
[-3.0, 4.7], # e: merge
[2.7, -0.7], # F: split
[0.7, 15.0], # l: merge
[1.6, 23.0], # u: merge
[2.1, 11.0], # m: merge
[0.0, 20.0], # e: merge
[18.0, 0.7], # !: split
])
# Confirm TF unicode_script op that requires ICU works
tf_unicode_script = tf.strings.unicode_script(
[ord('a'), 0x0411, 0x82b8, ord(',')])
# Unicode script tokenizer
us_tokenizer = text.UnicodeScriptTokenizer()
unicode_script = us_tokenizer.tokenize(['a string'])
# Whitespace tokenizer
ws_tokenizer = text.WhitespaceTokenizer()
whitespace = ws_tokenizer.tokenize(['a string'])
# Wordpiece tokenizer
wp_initializer = tf.lookup.KeyValueTensorInitializer(
['i'], [1], key_dtype=tf.string, value_dtype=tf.int64)
self.wp_vocab_table = tf.lookup.StaticHashTable(wp_initializer,
default_value=-1)
wp_tokenizer = text.WordpieceTokenizer(self.wp_vocab_table)
wordpiece = wp_tokenizer.tokenize(['i am'])
# Wordshape
wordshapes = text.wordshape([u'a-b', u'a\u2010b'.encode('utf-8')],
text.WordShape.HAS_PUNCTUATION_DASH)
# Assertion method
def assert_check(tensor):
return tf.assert_equal(tensor, tf.identity(tensor))
# Assertions
constrained_sequence_assert = assert_check(
constrained_sequence.to_tensor())
max_spanning_tree_assert = assert_check(max_spanning_tree)
normalized_assert = assert_check(normalized)
regex_split_assert = assert_check(regex_split.to_tensor())
rouge_l_assert = assert_check(rouge_l)
sentence_breaking_assert = assert_check(sentence_breaking.to_tensor())
sentence_breaking_v2_assert = assert_check(
sbv2_fragment_text.to_tensor())
sentencepiece_assert = assert_check(sentencepiece.to_tensor())
sentencepiece_id_assert = assert_check(sentencepiece_id)
sentencepiece_size_assert = assert_check(sentencepiece_size)
split_merge_assert = assert_check(split_merge)
split_merge_from_logits_assert = assert_check(split_merge_from_logits)
tf_unicode_script_assert = assert_check(tf_unicode_script)
unicode_script_assert = assert_check(unicode_script.to_tensor())
whitespace_assert = assert_check(whitespace.to_tensor())
wordpiece_assert = assert_check(wordpiece.to_tensor())
wordshapes_assert = assert_check(wordshapes)
with tf.control_dependencies([
constrained_sequence_assert, max_spanning_tree_assert,
normalized_assert, regex_split_assert, rouge_l_assert,
sentence_breaking_assert, sentence_breaking_v2_assert,
sentencepiece_assert, sentencepiece_id_assert,
sentencepiece_size_assert, split_merge_assert,
split_merge_from_logits_assert, tf_unicode_script_assert,
unicode_script_assert, whitespace_assert, wordpiece_assert,
wordshapes_assert
]):
y = tf.add(x, [1])
return {'y': y}
def _do_lower_case(t):
t = tf_text.case_fold_utf8(t)
t = tf_text.normalize_utf8(t, "NFD")
t = tf.regex_replace(t, r"\p{Mn}", "")
return t
def preprocess_text(text, label):
standardized = tf_text.case_fold_utf8(text)
tokenized = tokenizer.tokenize(standardized)
vectorized = vocab_table.lookup(tokenized)
return vectorized, label
def tokenize(text, unused_label):
lower_case = tf_text.case_fold_utf8(text)
return tokenizer.tokenize(lower_case)
def __call__(self, x):
# Constrained sequence
cs_scores = np.array([[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0,
10.0]])
cs_input = np.array([cs_scores, cs_scores, cs_scores],
dtype=np.float32)
cs_transition_weights = np.array(
[[-1.0, 1.0, -2.0, 2.0, 0.0], [3.0, -3.0, 4.0, -4.0, 0.0],
[5.0, 1.0, 10.0, 1.0, 1.0], [-7.0, 7.0, -8.0, 8.0, 0.0],
[0.0, 1.0, 2.0, 3.0, 0.0]],
dtype=np.float32)
cs_allowed_transitions = np.array([[True, True, True, True, True],
[True, True, True, True, True],
[True, False, True, False, False],
[True, True, True, True, True],
[True, False, True, True, True]])
constrained_sequence = text.viterbi_constrained_sequence(
cs_input, [2, 2, 2],
allowed_transitions=cs_allowed_transitions,
transition_weights=cs_transition_weights,
use_log_space=True,
use_start_and_end_states=True)
# Max Spanning Tree
mst_num_nodes = tf.constant([4, 3], tf.int32)
mst_scores = tf.constant(
[[[0, 0, 0, 0], [1, 0, 0, 0], [1, 2, 0, 0], [1, 2, 3, 4]],
[[4, 3, 2, 9], [0, 0, 2, 9], [0, 0, 0, 9], [9, 9, 9, 9]]],
tf.int32) # pyformat: disable
(max_spanning_tree,
_) = text.max_spanning_tree(mst_num_nodes, mst_scores)
# Normalize
normalized = text.case_fold_utf8(['A String'])
normalized = text.normalize_utf8(normalized)
# Regex split
regex_split = text.regex_split(input=['Yo dawg!'],
delim_regex_pattern=r'\s')
# Rouge-L
rl_hypotheses = tf.ragged.constant(
[['captain', 'of', 'the', 'delta', 'flight'],
['the', '1990', 'transcript']])
rl_references = tf.ragged.constant(
[['delta', 'air', 'lines', 'flight'],
['this', 'concludes', 'the', 'transcript']])
(rouge_l, _, _) = text.metrics.rouge_l(rl_hypotheses, rl_references)
# Sentence breaking
sb_token_word = [['Welcome', 'to', 'the', 'U.S.', '!', 'Harry'],
['Wu', 'Tang', 'Clan', ';', 'ain\'t', 'nothing']]
sb_token_properties = [[0, 0, 0, 256, 0, 0], [0, 0, 0, 0, 0, 0]]
sb_token_starts = []
sb_token_ends = []
for sentence in sb_token_word:
sentence_string = ''
sentence_start = []
sentence_end = []
for word in sentence:
sentence_start.append(len(sentence_string))
sentence_string = sentence_string.join([word, ' '])
sentence_end.append(len(sentence_string))
sb_token_starts.append(sentence_start)
sb_token_ends.append(sentence_end)
sb_token_starts = tf.constant(sb_token_starts, dtype=tf.int64)
sb_token_ends = tf.constant(sb_token_ends, dtype=tf.int64)
sb_token_properties = tf.ragged.constant(sb_token_properties,
dtype=tf.int64)
(sentence_breaking, _, _,
_) = text.sentence_fragments(sb_token_word, sb_token_starts,
sb_token_ends, sb_token_properties)
# Sentencepiece tokenizer
sp_model_file = (
'third_party/tensorflow_text/python/ops/test_data/test_oss_model.model'
)
sp_model = open(sp_model_file, 'rb').read()
sp_tokenizer = text.SentencepieceTokenizer(sp_model)
sentencepiece = sp_tokenizer.tokenize(['A sentence of things.'])
sentencepiece = sp_tokenizer.detokenize(sentencepiece)
(sentencepiece, _,
_) = sp_tokenizer.tokenize_with_offsets(sentencepiece)
sentencepiece_size = sp_tokenizer.vocab_size()
sentencepiece_id = sp_tokenizer.id_to_string(1)
# Split merge tokenizer - not in this version
sm_tokenizer = text.SplitMergeTokenizer()
split_merge = sm_tokenizer.tokenize(b'IloveFlume!',
[0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0])
# Unicode script tokenizer
us_tokenizer = text.UnicodeScriptTokenizer()
unicode_script = us_tokenizer.tokenize(['a string'])
# Whitespace tokenizer
ws_tokenizer = text.WhitespaceTokenizer()
whitespace = ws_tokenizer.tokenize(['a string'])
# Wordpiece tokenizer
wp_initializer = tf.lookup.KeyValueTensorInitializer(
['i'], [1], key_dtype=tf.string, value_dtype=tf.int64)
self.wp_vocab_table = tf.lookup.StaticHashTable(wp_initializer,
default_value=-1)
wp_tokenizer = text.WordpieceTokenizer(self.wp_vocab_table)
wordpiece = wp_tokenizer.tokenize(['i am'])
# Wordshape
wordshapes = text.wordshape([u'a-b', u'a\u2010b'.encode('utf-8')],
text.WordShape.HAS_PUNCTUATION_DASH)
with tf.control_dependencies([
constrained_sequence, max_spanning_tree, normalized,
regex_split, rouge_l, sentence_breaking, sentencepiece,
sentencepiece_id, sentencepiece_size, split_merge,
unicode_script, whitespace, wordpiece, wordshapes
]):
y = tf.add(x, [1])
return {'y': y}
def test_lowercase_one_string_ragged(self):
txt = ragged_factory_ops.constant([[" TExt ", "to", " loWERcase! "],
[" TExt to loWERcase! "]])
expected = [[" text ", "to", " lowercase! "], [" text to lowercase! "]]
self.assertRaggedEqual(expected, text.case_fold_utf8(txt))
def preprocess(text): """Normalize the text, and return tokens.""" text = tf.reshape(text, [-1]) text = tf_text.case_fold_utf8(text) tokenizer = tflite_text_api.WhitespaceTokenizer() return tokenizer.tokenize(text)
def unpaired_tokenize(self, texts):
if self.do_lower_case:
texts = case_fold_utf8(texts)
return self.tf_tokenizer.tokenize(texts)
