alu/cse151b-final-project
1
0
Fork 0
mirror of https://github.com/ltcptgeneral/cse151b-final-project.git synced 2024-12-26 18:19:08 +00:00
Code Issues Packages Projects Releases Wiki Activity

feasible training times by dropping to bert-base

Browse Source
This commit is contained in:
ltcptgeneral 2024-03-06 21:48:09 -08:00
parent 6a4027afb7
commit 6cbffcdec1
1 changed files with 2 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

5
test.py
View File

@ -8,9 +8,9 @@ import matplotlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device) print(device)
encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102) encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-base-uncased", bos_token_id=101, eos_token_id=102)
# add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
decoder = BertGenerationDecoder.from_pretrained("google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102) decoder = BertGenerationDecoder.from_pretrained("google-bert/bert-base-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102)
model = EncoderDecoderModel(encoder=encoder, decoder=decoder) model = EncoderDecoderModel(encoder=encoder, decoder=decoder)
# create tokenizer... # create tokenizer...
@ -43,7 +43,6 @@ for i in range(epochs):
for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)): for idx, (question, answer) in progress_bar(enumerate(dataloader), total=len(dataloader)):
print(question)
input_ids = tokenizer(question, add_special_tokens=False, return_tensors="pt").input_ids.to(device) input_ids = tokenizer(question, add_special_tokens=False, return_tensors="pt").input_ids.to(device)
label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device) label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device)