Comments (4)
Thanks for your questions.
- I believe these are validation numbers since the test set is not public. That is also done by prior work.
- Nope, that's not a typo. You can verify it with our checkpoint :)
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Wow, thanks for your quick response. I got two more questions.
- If I understand correctly, in table 2, the numbers of
BitFit
were taken from the original paper. But actually, there are some numbers I can not find in the original paper. For example, you mentioned theRoBbase (BitFit) on MRPC task
results in 92.7, but I think the original paper reported this number as 92.0 in their Table 2. Could you specify more details about this? - Do you fine-tune the bias terms? cause, I understand you don't require gradients for the weight terms, but I did not see you turn off this for bias terms.
Line 116 in 33b9536
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You are right. I can't remember where we got 92.7, and it should be 92.0.
Yes, the bias term is learnable here, even though it is not in the code used in our experiments. This seems to be a good idea in practice and has a minimal overhead. The checkpointing utility functions should take care of saving/loading biases. Please let me know if you encounter any issues :)
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Thanks~
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Related Issues (20)
- Code samples for "UNDERSTANDING THE LOW-RANK UPDATES" chapter (chapter 7). HOT 2
- why use alpha/r in stead of alpha? HOT 2
- Error in MergedLinear HOT 5
- RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)` HOT 4
- Pre-trained conv weight is not same as that self.conv.weight HOT 3
- matmul ordering in MergedLinear HOT 1
- How does this paper search the hyperparameters on GLUE datasets with Roberta? HOT 1
- PyPi version and GitHub version give different results HOT 1
- Embedding reset_parameters() implement wrong HOT 3
- Conv1d and Conv3d are not working HOT 2
- fine tuning RoBERTa-base with LoRA (ValueError: Classification metrics can't handle a mix of binary and multilabel-indicator targets)
- The content on pypi does not seem to be updated HOT 3
- Using gradient checkpoint with LoRA
- Probably a bug in the lora embeding class in loralib/layers.py HOT 2
- Cannot use lora for a pre-trained model HOT 2
- Some questions about LoRA for pre-trained model
- Question about reproducing RoBERTa base Fine-tune HOT 1
- T(w) problem
- batch=1,why adapter latency so much vs. LoRA in paper??? HOT 3
- AB matrix initialization in layers.py does not conform to the description of the paper HOT 2
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