Comments (1)
Hi @LTYRK
- What are the shapes of
finetuned_attention_values
andpretrained_attention_values
that you obtain? - I'm not sure I understand your question. The global attention is, by definition, global, meaning that they don't correspond to any specific location. That's one of the main differences between ProteinBERT's architecture to that of other BERT-like models (which typically use self-attention, which is location-specific).
from protein_bert.
Related Issues (20)
- Failing to get the weights from the dedicated github repo HOT 5
- Use ProteinBERT with Own Dataset HOT 3
- Original h5 file HOT 5
- loss plot during pretraining HOT 1
- signal peptide detection HOT 1
- KeyError: "Unable to open object (object 'test_set_mask' doesn't exist)" HOT 6
- How to extract the embedding of an amino acid? HOT 10
- Graph execution error HOT 6
- Extract local and global representation using finetune model HOT 1
- Running Benchmarks HOT 4
- Evaluation on larger data set HOT 6
- Using vector representations in the "weights" parameter in the "embedding" section of an LSTM model after fine-tuning my own data HOT 1
- Failing to extract global embedding (1,15599) -> (1,512) HOT 1
- What do the settings mean? HOT 3
- Error when trying to run the finetuning code given in the jupyter notebook HOT 2
- ValueError, set_weights error
- model_generation.py list is not callable error HOT 2
- GO annotations during fine tuning HOT 1
- Missing MajorPTMs train CSV file HOT 1
- Can't get proteinBERT to run on GPU HOT 1
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from protein_bert.