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test_tokenization.py
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test_tokenization.py
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import logging
from farm.modeling.tokenization import Tokenizer, tokenize_with_metadata, truncate_sequences
from transformers import BertTokenizer, BertTokenizerFast, RobertaTokenizer, XLNetTokenizer
from transformers import ElectraTokenizerFast
import re
def test_basic_loading(caplog):
caplog.set_level(logging.CRITICAL)
tokenizer = Tokenizer.load(
pretrained_model_name_or_path="bert-base-cased",
do_lower_case=True
)
assert type(tokenizer) == BertTokenizer
assert tokenizer.basic_tokenizer.do_lower_case == True
tokenizer = Tokenizer.load(
pretrained_model_name_or_path="xlnet-base-cased",
do_lower_case=True
)
assert type(tokenizer) == XLNetTokenizer
assert tokenizer.do_lower_case == True
tokenizer = Tokenizer.load(
pretrained_model_name_or_path="roberta-base"
)
assert type(tokenizer) == RobertaTokenizer
def test_bert_tokenizer_all_meta(caplog):
caplog.set_level(logging.CRITICAL)
lang_model = "bert-base-cased"
tokenizer = Tokenizer.load(
pretrained_model_name_or_path=lang_model,
do_lower_case=False
)
basic_text = "Some Text with neverseentokens plus !215?#. and a combined-token_with/chars"
# original tokenizer from transformer repo
tokenized = tokenizer.tokenize(basic_text)
assert tokenized == ['Some', 'Text', 'with', 'never', '##see', '##nto', '##ken', '##s', 'plus', '!', '215', '?', '#', '.', 'and', 'a', 'combined', '-', 'token', '_', 'with', '/', 'ch', '##ars']
# ours with metadata
tokenized_meta = tokenize_with_metadata(text=basic_text, tokenizer=tokenizer)
assert tokenized_meta["tokens"] == tokenized
assert tokenized_meta["offsets"] == [0, 5, 10, 15, 20, 23, 26, 29, 31, 36, 37, 40, 41, 42, 44, 48, 50, 58, 59, 64, 65, 69, 70, 72]
assert tokenized_meta["start_of_word"] == [True, True, True, True, False, False, False, False, True, True, False, False, False, False, True, True, True, False, False, False, False, False, False, False]
def test_save_load(caplog):
caplog.set_level(logging.CRITICAL)
lang_names = ["bert-base-cased", "roberta-base", "xlnet-base-cased"]
tokenizers = []
for lang_name in lang_names:
t = Tokenizer.load(lang_name, lower_case=False)
t.add_tokens(new_tokens=["neverseentokens"])
tokenizers.append(t)
basic_text = "Some Text with neverseentokens plus !215?#. and a combined-token_with/chars"
for tokenizer in tokenizers:
save_dir = f"testsave"
tokenizer_type = tokenizer.__class__.__name__
tokenizer.save_pretrained(save_dir)
tokenizer_loaded = Tokenizer.load(save_dir, tokenizer_class=tokenizer_type)
tokenized_before = tokenize_with_metadata(text=basic_text, tokenizer=tokenizer)
tokenized_after = tokenize_with_metadata(text=basic_text, tokenizer=tokenizer_loaded)
assert tokenized_before == tokenized_after
def test_truncate_sequences(caplog):
caplog.set_level(logging.CRITICAL)
lang_names = ["bert-base-cased", "roberta-base", "xlnet-base-cased"]
tokenizers = []
for lang_name in lang_names:
t = Tokenizer.load(lang_name, lower_case=False)
tokenizers.append(t)
# artificial sequences (could be tokens, offsets, or anything else)
seq_a = list(range(10))
seq_b = list(range(15))
max_seq_len = 20
for tokenizer in tokenizers:
for strategy in ["longest_first", "only_first","only_second"]:
trunc_a, trunc_b, overflow = truncate_sequences(seq_a=seq_a,seq_b=seq_b,tokenizer=tokenizer,
max_seq_len=max_seq_len, truncation_strategy=strategy)
assert len(trunc_a) + len(trunc_b) + tokenizer.num_special_tokens_to_add(pair=True) == max_seq_len
def test_fast_tokenizer(caplog):
fast_tokenizer = Tokenizer.load("bert-base-cased", lower_case=False, use_fast=True)
tokenizer = Tokenizer.load("bert-base-cased", lower_case=False, use_fast=False)
texts = [
"This is a sentence",
"Der entscheidende Pass",
"This is a sentence with multiple spaces",
"力加勝北区ᴵᴺᵀᵃছজটডণত",
"Thiso text is included tolod makelio sure Unicodeel is handled properly:",
"This is a sentence...",
"Let's see all on this text and. !23# neverseenwordspossible",
"""This is a sentence.
With linebreak""",
"""Sentence with multiple
newlines
""",
"and another one\n\n\nwithout space",
"This is a sentence with tab",
"This is a sentence with multiple tabs",
]
for text in texts:
# plain tokenize function
tokenized = tokenizer.tokenize(text)
fast_tokenized = fast_tokenizer.tokenize(text)
assert tokenized == fast_tokenized
# our tokenizer with metadata on "whitespace tokenized words"
tokenized_meta = tokenize_with_metadata(text=text, tokenizer=tokenizer)
fast_tokenized_meta = tokenize_with_metadata(text=text, tokenizer=fast_tokenizer)
# verify that tokenization on full sequence is the same as the one on "whitespace tokenized words"
assert tokenized_meta == fast_tokenized_meta, f"Failed using {tokenizer.__class__.__name__}"
def test_all_tokenizer_on_special_cases(caplog):
caplog.set_level(logging.CRITICAL)
lang_names = ["bert-base-cased", "roberta-base", "xlnet-base-cased"]
tokenizers = []
for lang_name in lang_names:
t = Tokenizer.load(lang_name, lower_case=False)
tokenizers.append(t)
texts = [
"This is a sentence",
"Der entscheidende Pass",
"This is a sentence with multiple spaces",
"力加勝北区ᴵᴺᵀᵃছজটডণত",
"Thiso text is included tolod makelio sure Unicodeel is handled properly:",
"This is a sentence...",
"Let's see all on this text and. !23# neverseenwordspossible",
"""This is a sentence.
With linebreak""",
"""Sentence with multiple
newlines
""",
"and another one\n\n\nwithout space",
"This is a sentence with tab",
"This is a sentence with multiple tabs",
]
for tokenizer in tokenizers:
for text in texts:
# Important: we don't assume to preserve whitespaces after tokenization.
# This means: \t, \n " " etc will all resolve to a single " ".
# This doesn't make a difference for BERT + XLNet but it does for roBERTa
# 1. original tokenize function from transformer repo on full sentence
standardized_whitespace_text = ' '.join(text.split()) # remove multiple whitespaces
tokenized = tokenizer.tokenize(standardized_whitespace_text)
# 2. our tokenizer with metadata on "whitespace tokenized words"
tokenized_meta = tokenize_with_metadata(text=text, tokenizer=tokenizer)
# verify that tokenization on full sequence is the same as the one on "whitespace tokenized words"
assert tokenized_meta["tokens"] == tokenized, f"Failed using {tokenizer.__class__.__name__}"
# verify that offsets align back to original text
if text == "力加勝北区ᴵᴺᵀᵃছজটডণত":
# contains [UNK] that are impossible to match back to original text space
continue
for tok, offset in zip(tokenized_meta["tokens"], tokenized_meta["offsets"]):
#subword-tokens have special chars depending on model type. In order to align with original text we need to get rid of them
tok = re.sub(r"^(##|Ġ|▁)", "", tok)
#tok = tokenizer.decode(tokenizer.convert_tokens_to_ids(tok))
original_tok = text[offset:offset+len(tok)]
assert tok == original_tok, f"Offset alignment wrong for {tokenizer.__class__.__name__} and text '{text}'"
def test_bert_custom_vocab(caplog):
caplog.set_level(logging.CRITICAL)
lang_model = "bert-base-cased"
tokenizer = Tokenizer.load(
pretrained_model_name_or_path=lang_model,
do_lower_case=False
)
#deprecated: tokenizer.add_custom_vocab("samples/tokenizer/custom_vocab.txt")
tokenizer.add_tokens(new_tokens=["neverseentokens"])
basic_text = "Some Text with neverseentokens plus !215?#. and a combined-token_with/chars"
# original tokenizer from transformer repo
tokenized = tokenizer.tokenize(basic_text)
assert tokenized == ['Some', 'Text', 'with', 'neverseentokens', 'plus', '!', '215', '?', '#', '.', 'and', 'a', 'combined', '-', 'token', '_', 'with', '/', 'ch', '##ars']
# ours with metadata
tokenized_meta = tokenize_with_metadata(text=basic_text, tokenizer=tokenizer)
assert tokenized_meta["tokens"] == tokenized
assert tokenized_meta["offsets"] == [0, 5, 10, 15, 31, 36, 37, 40, 41, 42, 44, 48, 50, 58, 59, 64, 65, 69, 70, 72]
assert tokenized_meta["start_of_word"] == [True, True, True, True, True, True, False, False, False, False, True, True, True, False, False, False, False, False, False, False]
def test_fast_bert_custom_vocab(caplog):
caplog.set_level(logging.CRITICAL)
lang_model = "bert-base-cased"
tokenizer = Tokenizer.load(
pretrained_model_name_or_path=lang_model,
do_lower_case=False, use_fast=True
)
#deprecated: tokenizer.add_custom_vocab("samples/tokenizer/custom_vocab.txt")
tokenizer.add_tokens(new_tokens=["neverseentokens"])
basic_text = "Some Text with neverseentokens plus !215?#. and a combined-token_with/chars"
# original tokenizer from transformer repo
tokenized = tokenizer.tokenize(basic_text)
assert tokenized == ['Some', 'Text', 'with', 'neverseentokens', 'plus', '!', '215', '?', '#', '.', 'and', 'a', 'combined', '-', 'token', '_', 'with', '/', 'ch', '##ars']
# ours with metadata
tokenized_meta = tokenize_with_metadata(text=basic_text, tokenizer=tokenizer)
assert tokenized_meta["tokens"] == tokenized
assert tokenized_meta["offsets"] == [0, 5, 10, 15, 31, 36, 37, 40, 41, 42, 44, 48, 50, 58, 59, 64, 65, 69, 70, 72]
assert tokenized_meta["start_of_word"] == [True, True, True, True, True, True, False, False, False, False, True, True, True, False, False, False, False, False, False, False]
def test_fast_bert_tokenizer(caplog):
caplog.set_level(logging.CRITICAL)
tokenizer = Tokenizer.load("bert-base-german-cased", use_fast=True)
assert type(tokenizer) is BertTokenizerFast
def test_fast_bert_tokenizer_strip_accents(caplog):
caplog.set_level(logging.CRITICAL)
tokenizer = Tokenizer.load("dbmdz/bert-base-german-uncased",
use_fast=True,
strip_accents=False)
assert type(tokenizer) is BertTokenizerFast
assert tokenizer._tokenizer._parameters['strip_accents'] is False
assert tokenizer._tokenizer._parameters['lowercase']
def test_fast_electra_tokenizer(caplog):
caplog.set_level(logging.CRITICAL)
tokenizer = Tokenizer.load("dbmdz/electra-base-german-europeana-cased-discriminator",
use_fast=True)
assert type(tokenizer) is ElectraTokenizerFast
if __name__ == "__main__":
test_all_tokenizer_on_special_cases()