Fined-tuned checkpoints -> Code clone detection

[Chinmayrane16]

Hi,

I am hoping to reproduce the results on code clone detection task.
This might seem like a silly question but the fined-tuned checkpoints released doesn't include the RobertaClassificationHead parameters, right?
I am able to load only the T5ForConditionalGeneration model using the provided checkpoints for the task.

So, how do I go about loading the entire CloneModel?

[yuewang-cuhk]

Hi, thanks for pointing out this. We will update this finetuned checkpoint with the RobertaClassificationHead parameters.

[sethun]

Hi!

Not sure if it is too-early, I downloaded the latest checkpoint (Uploaded an hour ago). Seems there is some-mismatch between ClonedModel's shape and checkpoint's shape

RuntimeError: Error(s) in loading state_dict for CloneModel: size mismatch for encoder.shared.weight: copying a param with shape torch.Size([32000, 768]) from checkpoint, the shape in current model is torch.Size([32100, 768]). size mismatch for encoder.encoder.embed_tokens.weight: copying a param with shape torch.Size([32000, 768]) from checkpoint, the shape in current model is torch.Size([32100, 768]). size mismatch for encoder.decoder.embed_tokens.weight: copying a param with shape torch.Size([32000, 768]) from checkpoint, the shape in current model is torch.Size([32100, 768]). size mismatch for encoder.lm_head.weight: copying a param with shape torch.Size([32000, 768]) from checkpoint, the shape in current model is torch.Size([32100, 768]).

[yuewang-cuhk]

Hi, we just updated this finetuned checkpoint yesterday. You can resolve this via adding this line. Following the instructions here, you will be able to reproduce the results of: [best-f1] test-f1: 0.9500, precision: 0.9526, recall: 0.9474.
This result is different from the reported one in the paper due to that we report micro-f1 instead of macro-f1 following GraphCodeBERT.