Skip to content

longt5 #35

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
somakaushik98 merged 1 commit into from
Jun 24, 2022
Merged

longt5 #35

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion simplet5/simplet5.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,9 @@
PreTrainedTokenizer,
T5TokenizerFast as T5Tokenizer,
MT5TokenizerFast as MT5Tokenizer,
AutoTokenizer,
LongT5ForConditionalGeneration
)
from transformers import AutoTokenizer
from torch.optim import AdamW
from torch.utils.data import Dataset, DataLoader
from transformers import AutoModelWithLMHead, AutoTokenizer
Expand Down Expand Up @@ -311,6 +312,9 @@ def from_pretrained(self, model_type="t5", model_name="t5-base") -> None:
self.model = T5ForConditionalGeneration.from_pretrained(
f"{model_name}", return_dict=True
)
elif model_type == 'longt5':
self.model = LongT5ForConditionalGeneration.from_pretrained(f"{model_name}")
self.tokenizer = AutoTokenizer.from_pretrained(f"{model_name}")

def train(
self,
Expand Down Expand Up @@ -413,6 +417,9 @@ def load_model(
elif model_type == "byt5":
self.model = T5ForConditionalGeneration.from_pretrained(f"{model_dir}")
self.tokenizer = ByT5Tokenizer.from_pretrained(f"{model_dir}")
elif model_type == 'longt5':
self.model = LongT5ForConditionalGeneration.from_pretrained(f"{model_dir}")
self.tokenizer = AutoTokenizer.from_pretrained(f"{model_dir}")

if use_gpu:
if torch.cuda.is_available():
Expand Down