def build_vocab(instances: Iterable[Instance] = None,
from_transformer: bool = False) -> Vocabulary:
"""
Build the Vocabulary object from the instances only,
or from the pretrained transformer, based on boolean flag
:param instances: Iterable of allennlp instances.
:param from_transformer: Whether to initialize vocab from
pretrained transformer, or from
instances directly.
:return Vocabulary: The Vocabulary object.
"""
# log.debug("Building the vocabulary.")
if from_transformer:
vocab = Vocabulary.from_pretrained_transformer(
model_name="bert-base-uncased")
elif instances:
vocab = Vocabulary.from_instances(instances)
else:
print("No instances to create vocab with, and pretrained"
" transformer isn't being used.")
raise UnskippableSituationError()
return vocab
def pretrained_t5_for_generation(cls,
model_name: str = "t5-base"
) -> "Seq2SeqPredictor":
"""
A helper method for creating a predictor for a pretrained T5 generation model.
# Examples
```python
from allennlp_models.generation.predictors import Seq2SeqPredictor
ARTICLE_TO_SUMMARIZE = '''
summarize: The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building,
and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side.
During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest
man-made structure in the world, a title it held for 41 years until the Chrysler Building in
New York City was finished in 1930. It was the first structure to reach a height of 300 metres.
Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller
than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is
the second tallest free-standing structure in France after the Millau Viaduct.
'''.strip().replace(
"\n", " "
)
predictor = Seq2SeqPredictor.pretrained_t5_for_generation("t5-small")
predictor.predict(ARTICLE_TO_SUMMARIZE)
```
"""
from allennlp.data import Vocabulary
from allennlp.data.tokenizers import PretrainedTransformerTokenizer
from allennlp.data.token_indexers import PretrainedTransformerIndexer
from allennlp_models.generation.dataset_readers import Seq2SeqDatasetReader
from allennlp_models.generation.models import T5
tokenizer, token_indexer = (
PretrainedTransformerTokenizer(model_name),
PretrainedTransformerIndexer(model_name),
)
reader = Seq2SeqDatasetReader(
source_tokenizer=tokenizer,
source_token_indexers={"tokens": token_indexer},
source_add_start_token=False,
source_add_end_token=False,
target_add_start_token=False,
target_add_end_token=False,
)
vocab = Vocabulary.from_pretrained_transformer(model_name)
model = T5(vocab, model_name)
return cls(model, reader)
