def test_mask(self):
# We try these two models, because BERT pads tokens with 0, but RoBERTa pads tokens with 1.
for model in ["bert-base-uncased", "roberta-base"]:
allennlp_tokenizer = PretrainedTransformerTokenizer(model)
indexer = PretrainedTransformerIndexer(model_name=model)
string_no_specials = "AllenNLP is great"
allennlp_tokens = allennlp_tokenizer.tokenize(string_no_specials)
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
expected_masks = [1] * len(indexed["token_ids"])
assert indexed["mask"] == expected_masks
max_length = 10
padding_lengths = {key: max_length for key in indexed.keys()}
padded_tokens = indexer.as_padded_tensor_dict(
indexed, padding_lengths)
padding_length = max_length - len(indexed["mask"])
expected_masks = expected_masks + ([0] * padding_length)
assert len(padded_tokens["mask"]) == max_length
assert padded_tokens["mask"].tolist() == expected_masks
assert len(padded_tokens["token_ids"]) == max_length
padding_suffix = [allennlp_tokenizer.tokenizer.pad_token_id
] * padding_length
assert padded_tokens["token_ids"][-padding_length:].tolist(
) == padding_suffix
def test_as_array_produces_token_sequence(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased",
do_lowercase=True)
indexer = PretrainedTransformerIndexer(model_name="bert-base-uncased",
do_lowercase=True)
tokens = tokenizer.tokenize("AllenNLP is great")
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
allennlp_tokens = [Token(token) for token in tokens]
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab, "key")
assert indexed["key"] == expected_ids
def __init__(self,
model_name: str,
namespace: str = "tags",
max_length: int = None,
**kwargs) -> None:
super().__init__(**kwargs)
# The matched version v.s. mismatched
self._matched_indexer = PretrainedTransformerIndexer(
model_name, namespace, max_length, **kwargs)
self._tokenizer = self._matched_indexer._tokenizer
self._num_added_start_tokens = self._matched_indexer._num_added_start_tokens
self._num_added_end_tokens = self._matched_indexer._num_added_end_tokens
def test_as_array_produces_token_sequence_bert_cased_sentence_pair(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
allennlp_tokenizer = PretrainedTransformerTokenizer("bert-base-cased")
indexer = PretrainedTransformerIndexer(model_name="bert-base-cased")
default_format = "[CLS] AllenNLP is great! [SEP] Really it is! [SEP]"
tokens = tokenizer.tokenize(default_format)
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
allennlp_tokens = allennlp_tokenizer.tokenize_sentence_pair(
"AllenNLP is great!", "Really it is!")
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
assert indexed["token_ids"] == expected_ids
def test_as_array_produces_token_sequence_roberta_sentence_pair(self):
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
allennlp_tokenizer = PretrainedTransformerTokenizer("roberta-base")
indexer = PretrainedTransformerIndexer(model_name="roberta-base")
default_format = "<s> AllenNLP is great! </s> </s> Really it is! </s>"
tokens = tokenizer.tokenize(default_format)
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
allennlp_tokens = allennlp_tokenizer.tokenize_sentence_pair(
"AllenNLP is great!", "Really it is!")
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab, "key")
assert indexed["key"] == expected_ids
def check_vocab_size(model_name: str):
namespace = "tags"
tokenizer = AutoTokenizer.from_pretrained(model_name)
allennlp_tokenizer = PretrainedTransformerTokenizer(model_name)
indexer = PretrainedTransformerIndexer(model_name=model_name,
namespace=namespace)
allennlp_tokens = allennlp_tokenizer.tokenize("AllenNLP is great!")
vocab = Vocabulary()
# here we copy entire transformers vocab
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
del indexed
assert vocab.get_vocab_size(
namespace=namespace) == tokenizer.vocab_size
def test_transformers_vocab_sizes(self, model_name):
namespace = "tags"
tokenizer = cached_transformers.get_tokenizer(model_name)
allennlp_tokenizer = PretrainedTransformerTokenizer(model_name)
indexer = PretrainedTransformerIndexer(model_name=model_name,
namespace=namespace)
allennlp_tokens = allennlp_tokenizer.tokenize("AllenNLP is great!")
vocab = Vocabulary()
# here we copy entire transformers vocab
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
del indexed
assert vocab.get_vocab_size(
namespace=namespace) == tokenizer.vocab_size
def test_transformers_vocabs_added_correctly(self):
namespace, model_name = "tags", "roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
allennlp_tokenizer = PretrainedTransformerTokenizer(model_name)
indexer = PretrainedTransformerIndexer(model_name=model_name,
namespace=namespace)
allennlp_tokens = allennlp_tokenizer.tokenize("AllenNLP is great!")
vocab = Vocabulary()
# here we copy entire transformers vocab
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
del indexed
assert vocab.get_token_to_index_vocabulary(
namespace=namespace) == tokenizer.encoder
def test_as_array_produces_token_sequence_roberta(self):
tokenizer = AutoTokenizer.from_pretrained("roberta-base")
allennlp_tokenizer = PretrainedTransformerTokenizer("roberta-base")
indexer = PretrainedTransformerIndexer(model_name="roberta-base")
string_specials = "<s> AllenNLP is great </s>"
string_no_specials = "AllenNLP is great"
tokens = tokenizer.tokenize(string_specials)
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
# tokens tokenized with our pretrained tokenizer have indices in them
allennlp_tokens = allennlp_tokenizer.tokenize(string_no_specials)
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab, "key")
assert indexed["key"] == expected_ids
def test_as_array_produces_token_sequence_bert_cased(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
allennlp_tokenizer = PretrainedTransformerTokenizer("bert-base-cased")
indexer = PretrainedTransformerIndexer(model_name="bert-base-cased")
string_specials = "[CLS] AllenNLP is great [SEP]"
string_no_specials = "AllenNLP is great"
tokens = tokenizer.tokenize(string_specials)
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
# tokens tokenized with our pretrained tokenizer have indices in them
allennlp_tokens = allennlp_tokenizer.tokenize(string_no_specials)
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
assert indexed["token_ids"] == expected_ids
def test_indices_to_tokens(self):
allennlp_tokenizer = PretrainedTransformerTokenizer(
"bert-base-uncased")
indexer_max_length = PretrainedTransformerIndexer(
model_name="bert-base-uncased", max_length=4)
indexer_no_max_length = PretrainedTransformerIndexer(
model_name="bert-base-uncased")
string_no_specials = "AllenNLP is great"
allennlp_tokens = allennlp_tokenizer.tokenize(string_no_specials)
vocab = Vocabulary()
indexed = indexer_no_max_length.tokens_to_indices(
allennlp_tokens, vocab)
tokens_from_indices = indexer_no_max_length.indices_to_tokens(
indexed, vocab)
self._assert_tokens_equal(allennlp_tokens, tokens_from_indices)
indexed = indexer_max_length.tokens_to_indices(allennlp_tokens, vocab)
tokens_from_indices = indexer_max_length.indices_to_tokens(
indexed, vocab)
# For now we are not removing special tokens introduced from max_length
sep_cls = [allennlp_tokens[-1], allennlp_tokens[0]]
expected = (allennlp_tokens[:3] + sep_cls + allennlp_tokens[3:5] +
sep_cls + allennlp_tokens[5:])
self._assert_tokens_equal(expected, tokens_from_indices)
def test_type_ids_when_folding(self):
allennlp_tokenizer = PretrainedTransformerTokenizer(
"bert-base-uncased", add_special_tokens=False)
indexer = PretrainedTransformerIndexer(model_name="bert-base-uncased",
max_length=6)
first_string = "How do trees get online?"
second_string = "They log in!"
tokens = allennlp_tokenizer.add_special_tokens(
allennlp_tokenizer.tokenize(first_string),
allennlp_tokenizer.tokenize(second_string))
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(tokens, vocab)
assert min(indexed["type_ids"]) == 0
assert max(indexed["type_ids"]) == 1
def test_as_array_produces_token_sequence_roberta_sentence_pair(self):
tokenizer = cached_transformers.get_tokenizer("roberta-base")
allennlp_tokenizer = PretrainedTransformerTokenizer(
"roberta-base", add_special_tokens=False)
indexer = PretrainedTransformerIndexer(model_name="roberta-base")
default_format = "<s> AllenNLP is great! </s> </s> Really it is! </s>"
tokens = tokenizer.tokenize(default_format)
expected_ids = tokenizer.convert_tokens_to_ids(tokens)
allennlp_tokens = allennlp_tokenizer.add_special_tokens(
allennlp_tokenizer.tokenize("AllenNLP is great!"),
allennlp_tokenizer.tokenize("Really it is!"),
)
vocab = Vocabulary()
indexed = indexer.tokens_to_indices(allennlp_tokens, vocab)
assert indexed["token_ids"] == expected_ids
def test_end_to_end(self, train_parameters: bool, last_layer_only: bool):
tokenizer = PretrainedTransformerTokenizer(
model_name="bert-base-uncased")
token_indexer = PretrainedTransformerIndexer(
model_name="bert-base-uncased")
sentence1 = "A, AllenNLP sentence."
tokens1 = tokenizer.tokenize(sentence1)
expected_tokens1 = [
"[CLS]", "a", ",", "allen", "##nl", "##p", "sentence", ".", "[SEP]"
]
assert [t.text for t in tokens1] == expected_tokens1
sentence2 = "AllenNLP is great"
tokens2 = tokenizer.tokenize(sentence2)
expected_tokens2 = [
"[CLS]", "allen", "##nl", "##p", "is", "great", "[SEP]"
]
assert [t.text for t in tokens2] == expected_tokens2
vocab = Vocabulary()
params = Params({
"token_embedders": {
"bert": {
"type": "pretrained_transformer",
"model_name": "bert-base-uncased",
"train_parameters": train_parameters,
"last_layer_only": last_layer_only,
}
}
})
token_embedder = BasicTextFieldEmbedder.from_params(vocab=vocab,
params=params)
instance1 = Instance(
{"tokens": TextField(tokens1, {"bert": token_indexer})})
instance2 = Instance(
{"tokens": TextField(tokens2, {"bert": token_indexer})})
batch = Batch([instance1, instance2])
batch.index_instances(vocab)
padding_lengths = batch.get_padding_lengths()
tensor_dict = batch.as_tensor_dict(padding_lengths)
tokens = tensor_dict["tokens"]
max_length = max(len(tokens1), len(tokens2))
assert tokens["bert"]["token_ids"].shape == (2, max_length)
assert tokens["bert"]["mask"].tolist() == [
[True, True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, True, False, False],
]
# Attention mask
bert_vectors = token_embedder(tokens)
assert bert_vectors.size() == (2, 9, 768)
assert bert_vectors.requires_grad == (train_parameters
or not last_layer_only)
def __init__(
self,
pretrained_model_pth: str = None,
):
"""
两阶段数据读取模型
1、先读s， 再读 o与p
2、先读o， 再读 s和p
:param token_indexers:
:param pretrained_model_pth:
:param lazy:
"""
super().__init__(False)
if pretrained_model_pth is None:
self._token_indexers = {"tokens": SingleIdTokenIndexer()}
else:
# 注意，这里要进行替换，因为你的词表已经变了。
self._token_indexers = {
"tokens": PretrainedTransformerIndexer(pretrained_model_pth)
}
if pretrained_model_pth is not None:
self.pretrained_tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_pth)
else:
self.pretrained_tokenizer = None
def __init__(self,
transformer_model_name: str = "bert-base-cased",
length_limit: int = 384,
question_length_limit: int = 64,
stride: int = 128,
raise_errors: bool = False,
tokenizer_kwargs: Dict[str, Any] = None,
one_instance_per_query: bool = False,
max_instances: int = None,
**kwargs) -> None:
"""
Initialize the RecordTaskReader.
"""
super(RecordTaskReader,
self).__init__(manual_distributed_sharding=True, **kwargs)
# Save the values passed to __init__ to protected attributes
self._tokenizer = PretrainedTransformerTokenizer(
transformer_model_name,
add_special_tokens=False,
tokenizer_kwargs=tokenizer_kwargs,
)
self._token_indexers = {
"tokens":
PretrainedTransformerIndexer(transformer_model_name,
tokenizer_kwargs=tokenizer_kwargs)
}
self._length_limit = length_limit
self._query_len_limit = question_length_limit
self._stride = stride
self._raise_errors = raise_errors
self._cls_token = '@placeholder'
self._max_instances = max_instances
self._one_instance_per_query = one_instance_per_query
def __init__(
self,
lazy: bool = False,
cache_directory: Optional[str] = None,
max_instances: Optional[int] = None,
min_num_candidate: int = 3,
max_num_candidate: int = 5,
transformer_model_name_or_archive_path: str = "bert-base-uncased",
) -> None:
super().__init__(lazy=lazy,
cache_directory=cache_directory,
max_instances=max_instances)
if "tar.gz" in transformer_model_name_or_archive_path:
config = extract_config_from_archive(
transformer_model_name_or_archive_path)
model_name = config.as_dict(
)["dataset_reader"]["tokenizer"]["model_name"]
else:
model_name = transformer_model_name_or_archive_path
self._tokenizer = PretrainedTransformerTokenizer(
model_name=model_name, add_special_tokens=False)
self._tokenindexer = PretrainedTransformerIndexer(
model_name=model_name)
self._min_num_candidate = min_num_candidate
self._max_num_candidate = max_num_candidate
def test_text_to_instance_with_bert_tokenizer_and_indexer(self):
tokenizer = PretrainedTransformerTokenizer('bert-base-cased',
do_lowercase=False)
token_indexer = PretrainedTransformerIndexer('bert-base-cased',
do_lowercase=False)
reader = MaskedLanguageModelingReader(tokenizer,
{'bert': token_indexer})
instance = reader.text_to_instance(
sentence='This is AllenNLP [MASK] token .', targets=['This'])
assert [t.text for t in instance['tokens']] == [
'[CLS]', 'This', 'is', 'Allen', '##NL', '##P', '[MASK]', 'token',
'.', '[SEP]'
]
assert [i.sequence_index for i in instance['mask_positions']] == [6]
assert [t.text for t in instance['target_ids']] == ['This']
vocab = Vocabulary()
instance.index_fields(vocab)
tensor_dict = instance.as_tensor_dict(instance.get_padding_lengths())
assert tensor_dict.keys() == {'tokens', 'mask_positions', 'target_ids'}
bert_token_ids = tensor_dict['tokens']['bert'].numpy().tolist()
target_ids = tensor_dict['target_ids']['bert'].numpy().tolist()
# I don't know what wordpiece id BERT is going to assign to 'This', but it at least should
# be the same between the input and the target.
assert target_ids[0] == bert_token_ids[1]
def __init__(self,
pretrained_model: str = None,
tokenizer: Optional[Tokenizer] = None,
token_indexers: Dict[str, TokenIndexer] = None,
max_pieces: int = 512,
add_prefix: bool = False,
combine_input_fields: bool = True,
sample: int = -1) -> None:
super().__init__()
if pretrained_model != None:
self._tokenizer = PretrainedTransformerTokenizer(
pretrained_model, max_length=max_pieces)
token_indexer = PretrainedTransformerIndexer(pretrained_model)
self._token_indexers = {'tokens': token_indexer}
else:
self._tokenizer = tokenizer or SpacyTokenizer()
self._token_indexers = token_indexers or {
"tokens": SingleIdTokenIndexer()
}
self._sample = sample
self._add_prefix = add_prefix
self._combine_input_fields = combine_input_fields
self._debug_prints = -1
def test_text_to_instance_with_bert_tokenizer_and_indexer(self):
tokenizer = PretrainedTransformerTokenizer("bert-base-cased")
token_indexer = PretrainedTransformerIndexer("bert-base-cased")
reader = NextTokenLmReader(tokenizer, {"bert": token_indexer})
instance = reader.text_to_instance(sentence="AllenNLP is very", target="very")
assert [t.text for t in instance["tokens"]] == [
"[CLS]",
"Allen",
"##NL",
"##P",
"is",
"very",
"[SEP]",
]
assert [t.text for t in instance["target_ids"]] == ["very"]
vocab = Vocabulary()
instance.index_fields(vocab)
tensor_dict = instance.as_tensor_dict(instance.get_padding_lengths())
assert tensor_dict.keys() == {"tokens", "target_ids"}
bert_token_ids = tensor_dict["tokens"]["bert"].numpy().tolist()
target_ids = tensor_dict["target_ids"]["bert"].numpy().tolist()
# I don't know what wordpiece id BERT is going to assign to 'This', but it at least should
# be the same between the input and the target.
assert target_ids[0] == bert_token_ids[5]
def __init__(self,
pretrained_model: str,
max_pieces: int = 512,
num_choices: int = 4,
add_prefix: Dict[str, str] = None,
sample: int = -1) -> None:
super().__init__()
self._tokenizer = PretrainedTransformerTokenizer(pretrained_model)
self._tokenizer_no_special_tokens = PretrainedTransformerTokenizer(
pretrained_model, add_special_tokens=False)
# self._tokenizer_internal = self._tokenizer._tokenizer
self._tokenizer_internal = self._tokenizer.tokenizer
token_indexer = PretrainedTransformerIndexer(pretrained_model)
self._token_indexers = {'tokens': token_indexer}
self._max_pieces = max_pieces
self._sample = sample
self._num_choices = num_choices
self._add_prefix = add_prefix or {}
for model in [
"roberta", "bert", "openai-gpt", "gpt2", "transfo-xl", "xlnet",
"xlm"
]:
if model in pretrained_model:
self._model_type = model
break
def __init__(self, hparams, em_dict, rm_dict, entity_mentions, tokensD,
all_known_e2, all_known_e1, scoresD_tail, scoresD_head, mode,
max_instances, world_size) -> None:
super().__init__(manual_distributed_sharding=True,
manual_multi_process_sharding=True,
max_instances=max_instances,
world_size=world_size)
self.hparams = hparams
self.em_dict = em_dict
self.rm_dict = rm_dict
self.entity_mentions = entity_mentions
self._tokenizer = PretrainedTransformerTokenizer('bert-base-cased')
self._token_indexer = {
"tokens": PretrainedTransformerIndexer('bert-base-cased')
}
self._tokensD = tokensD
self.random_indexes = list(range(len(em_dict)))
random.shuffle(self.random_indexes)
self.key_index = 0
self.all_known_e1 = all_known_e1
self.all_known_e2 = all_known_e2
self.scoresD_tail = scoresD_tail
self.scoresD_head = scoresD_head
self.mode = mode
self.factsD = None
if hparams.retrieve_facts:
self.factsD = pickle.load(open(hparams.retrieve_facts, 'rb'))
def __init__(self,
transformer_model_name: str = "bert-base-cased",
length_limit: int = 384,
stride: int = 128,
skip_invalid_examples: bool = False,
max_query_length: int = 64,
**kwargs) -> None:
super().__init__(**kwargs)
self._tokenizer = PretrainedTransformerTokenizer(
transformer_model_name,
add_special_tokens=False,
calculate_character_offsets=True)
self._token_indexers = {
"tokens": PretrainedTransformerIndexer(transformer_model_name)
}
self.length_limit = length_limit
self.stride = stride
self.skip_invalid_examples = skip_invalid_examples
self.max_query_length = max_query_length
self.non_content_type_id = max(
self._tokenizer.tokenizer.encode_plus(
"left", "right", return_token_type_ids=True)["token_type_ids"])
# workaround for a bug in the transformers library
if "distilbert" in transformer_model_name:
self.non_content_type_id = 0
def __init__(self,
pretrained_model_pth: str = None,
lazy: bool = False,
mode: str = 'sop'):
"""
三阶段数据读取模型
1、先读s， 再读o，最后读pos
2、先读o， 再读s，最后读p
:param pretrained_model_pth:
:param lazy:
:param mode: 选择模式，先读哪一个
"""
super().__init__(lazy)
if pretrained_model_pth is None:
self._token_indexers = {"tokens": SingleIdTokenIndexer()}
else:
self._token_indexers = {
"tokens": PretrainedTransformerIndexer(pretrained_model_pth)
}
self.pretrained_tokenizer = None
if pretrained_model_pth is not None:
self.pretrained_tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_pth)
assert mode in ['sop', 'osp']
self.mode = mode
def test_text_to_instance_with_bert_tokenizer_and_indexer(self):
tokenizer = PretrainedTransformerTokenizer("bert-base-cased")
token_indexer = PretrainedTransformerIndexer("bert-base-cased")
reader = MaskedLanguageModelingReader(tokenizer,
{"bert": token_indexer})
instance = reader.text_to_instance(
sentence="This is AllenNLP [MASK] token .", targets=["This"])
assert [t.text for t in instance["tokens"]] == [
"[CLS]",
"This",
"is",
"Allen",
"##NL",
"##P",
"[MASK]",
"token",
".",
"[SEP]",
]
assert [i.sequence_index for i in instance["mask_positions"]] == [6]
assert [t.text for t in instance["target_ids"]] == ["This"]
vocab = Vocabulary()
instance.index_fields(vocab)
tensor_dict = instance.as_tensor_dict(instance.get_padding_lengths())
assert tensor_dict.keys() == {"tokens", "mask_positions", "target_ids"}
bert_token_ids = tensor_dict["tokens"]["bert"]["token_ids"].numpy(
).tolist()
target_ids = tensor_dict["target_ids"]["bert"]["token_ids"].numpy(
).tolist()
# I don't know what wordpiece id BERT is going to assign to 'This', but it at least should
# be the same between the input and the target.
assert target_ids[0] == bert_token_ids[1]
def token_indexer_returner(self):
huggingface_name, do_lower_case = self.huggingfacename_returner()
return {
'tokens':
PretrainedTransformerIndexer(model_name=huggingface_name,
do_lowercase=do_lower_case)
}
def __init__(self,
lazy: bool = False,
tokenizer: Tokenizer = None,
token_indexers: Dict[str, TokenIndexer] = None,
max_tokens: int = None,
pseudo: bool = False if args.pseudo == False else True):
super().__init__(lazy)
# self.tokenizer = tokenizer or WhitespaceTokenizer()
# self.token_indexers = token_indexers or {"tokens":SingleIdTokenIndexer()}
# from collections import defaultdict
# ags = defaultdict(list)
ags = {
"additional_special_tokens":
("[pseudo1]", "[pseudo2]", "[pseudo3]", "[pseudo4]", "[pseudo5]",
"[pseudo6]", "[pseudo7]", "[pseudo8]", "[pseudo9]")
}
self.tokenizer = tokenizer or PretrainedTransformerTokenizer(
"bert-large-uncased",
tokenizer_kwargs=(ags if args.pseudo else {}))
self.token_indexers = token_indexers or {
"tokens": PretrainedTransformerIndexer("bert-large-uncased")
}
self.max_tokens = max_tokens
self.pseudo = pseudo
self.tags = tags
def __init__(
self,
token_indexers: Dict[str, TokenIndexer] = None,
domain_identifier: str = None,
bert_model_name: str = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
if token_indexers is not None:
self._token_indexers = token_indexers
elif bert_model_name is not None:
from allennlp.data.token_indexers import PretrainedTransformerIndexer
self._token_indexers = {
"tokens": PretrainedTransformerIndexer(bert_model_name)
}
else:
self._token_indexers = {"tokens": SingleIdTokenIndexer()}
self._domain_identifier = domain_identifier
if bert_model_name is not None:
self.bert_tokenizer = BertTokenizer.from_pretrained(
bert_model_name)
self.lowercase_input = "uncased" in bert_model_name
else:
self.bert_tokenizer = None
self.lowercase_input = False
def __init__(self,
token_indexers: Dict[str, TokenIndexer] = None,
lazy: bool = False) -> None:
super().__init__(lazy)
self.transformer_model = "bert-base-uncased"
self.tokenizer = PretrainedTransformerTokenizer(model_name=self.transformer_model,add_special_tokens=False,max_length=512)
self.token_indexer = PretrainedTransformerIndexer(model_name=self.transformer_model,max_length =512)
def test_end_to_end_t5(
self,
train_parameters: bool,
last_layer_only: bool,
gradient_checkpointing: bool,
):
tokenizer = PretrainedTransformerTokenizer(model_name="patrickvonplaten/t5-tiny-random")
token_indexer = PretrainedTransformerIndexer(model_name="patrickvonplaten/t5-tiny-random")
sentence1 = "A, AllenNLP sentence."
tokens1 = tokenizer.tokenize(sentence1)
expected_tokens1 = ["▁A", ",", "▁Allen", "N", "LP", "▁sentence", ".", "</s>"]
assert [t.text for t in tokens1] == expected_tokens1
sentence2 = "AllenNLP is great"
tokens2 = tokenizer.tokenize(sentence2)
expected_tokens2 = ["▁Allen", "N", "LP", "▁is", "▁great", "</s>"]
assert [t.text for t in tokens2] == expected_tokens2
vocab = Vocabulary()
params = Params(
{
"token_embedders": {
"bert": {
"type": "pretrained_transformer",
"model_name": "patrickvonplaten/t5-tiny-random",
"train_parameters": train_parameters,
"last_layer_only": last_layer_only,
"gradient_checkpointing": gradient_checkpointing,
"sub_module": "encoder",
}
}
}
)
token_embedder = BasicTextFieldEmbedder.from_params(vocab=vocab, params=params)
instance1 = Instance({"tokens": TextField(tokens1, {"bert": token_indexer})})
instance2 = Instance({"tokens": TextField(tokens2, {"bert": token_indexer})})
batch = Batch([instance1, instance2])
batch.index_instances(vocab)
padding_lengths = batch.get_padding_lengths()
tensor_dict = batch.as_tensor_dict(padding_lengths)
tokens = tensor_dict["tokens"]
max_length = max(len(tokens1), len(tokens2))
assert tokens["bert"]["token_ids"].shape == (2, max_length)
assert tokens["bert"]["mask"].tolist() == [
[True, True, True, True, True, True, True, True],
[True, True, True, True, True, True, False, False],
]
# Attention mask
bert_vectors = token_embedder(tokens)
assert bert_vectors.size() == (2, 8, 64)
assert bert_vectors.requires_grad == (train_parameters or not last_layer_only)
