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"""
T5 model trained to generate text from text
---------------------------------------------------------------------
"""
import json
import os
import torch
import transformers
from textattack.model_args import TEXTATTACK_MODELS
from textattack.models.tokenizers import T5Tokenizer
class T5ForTextToText(torch.nn.Module):
"""A T5 model trained to generate text from text.
For more information, please see the T5 paper, "Exploring the Limits of
Transfer Learning with a Unified Text-to-Text Transformer".
Appendix D contains information about the various tasks supported
by T5.
For usage information, see HuggingFace Transformers documentation section
on text-to-text with T5:
https://huggingface.co./transformers/usage.html.
Args:
mode (string): Name of the T5 model to use.
output_max_length (int): The max length of the sequence to be generated.
Between 1 and infinity.
input_max_length (int): Max length of the input sequence.
num_beams (int): Number of beams for beam search. Must be between 1 and
infinity. 1 means no beam search.
early_stopping (bool): if set to `True` beam search is stopped when at
least `num_beams` sentences finished per batch. Defaults to `True`.
"""
def __init__(
self,
mode="english_to_german",
output_max_length=20,
input_max_length=64,
num_beams=1,
early_stopping=True,
):
super().__init__()
self.model = transformers.T5ForConditionalGeneration.from_pretrained("t5-base")
self.model.eval()
self.tokenizer = T5Tokenizer(mode, max_length=output_max_length)
self.mode = mode
self.output_max_length = output_max_length
self.input_max_length = input_max_length
self.num_beams = num_beams
self.early_stopping = early_stopping
def __call__(self, *args, **kwargs):
# Generate IDs from the model.
output_ids_list = self.model.generate(
*args,
**kwargs,
max_length=self.output_max_length,
num_beams=self.num_beams,
early_stopping=self.early_stopping,
)
# Convert ID tensor to string and return.
return [self.tokenizer.decode(ids) for ids in output_ids_list]
def save_pretrained(self, output_dir):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
config = {
"mode": self.mode,
"output_max_length": self.output_max_length,
"input_max_length": self.input_max_length,
"num_beams": self.num_beams,
"early_stoppping": self.early_stopping,
}
# We don't save it as `config.json` b/c that name conflicts with HuggingFace's `config.json`.
with open(os.path.join(output_dir, "t5-wrapper-config.json"), "w") as f:
json.dump(config, f)
self.model.save_pretrained(output_dir)
@classmethod
def from_pretrained(cls, name_or_path):
"""Load trained LSTM model by name or from path.
Args:
name_or_path (str): Name of the model (e.g. "t5-en-de") or model saved via `save_pretrained`.
"""
if name_or_path in TEXTATTACK_MODELS:
t5 = cls(TEXTATTACK_MODELS[name_or_path])
return t5
else:
config_path = os.path.join(name_or_path, "t5-wrapper-config.json")
with open(config_path, "r") as f:
config = json.load(f)
t5 = cls.__new__(cls)
for key in config:
setattr(t5, key, config[key])
t5.model = transformers.T5ForConditionalGeneration.from_pretrained(
name_or_path
)
t5.tokenizer = T5Tokenizer(t5.mode, max_length=t5.output_max_length)
return t5
def get_input_embeddings(self):
return self.model.get_input_embeddings()
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