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feasible training times by dropping to bert-base

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ltcptgeneral 2024-03-06 21:48:09 -08:00
parent 6a4027afb7
commit 6cbffcdec1
1 changed files with 2 additions and 3 deletions
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test.py
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@ -8,9 +8,9 @@ import matplotlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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
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)
# create tokenizer...
@ -43,7 +43,6 @@ for i in range(epochs):
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)
label_ids = tokenizer(answer, return_tensors="pt").input_ids.to(device)