Comments (1)
This would also work, but we don't need the MLM head after training anymore. So, for the atom-mapping, we only load the AlbertModel part (see also: https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_albert.py#L738)
We experimented with different autoencoding models (e.g. BERT, there are some nice layers/heads visualisations in the SI of our work, explaining the differences). That's why we kept the option to use a different model type.
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Related Issues (20)
- If a low confidence score means that the obtained mapped_rxn is invalid and can not use it? HOT 2
- atom mapping for some reaction is error, how to fix this probrom HOT 4
- AttributeError: 'SmilesTokenizer' object has no attribute 'max_l HOT 4
- Request: All source code to reproduce will be contained in this repository? HOT 1
- False atom mapping result. HOT 2
- Script to finetune model? HOT 1
- segmentation fault HOT 3
- ValueError when processing reaction smiles HOT 2
- Cannot run on M1 Mac
- Training dataset of rxnmapper? HOT 1
- rxn module not present in 0.2.0 release -- ModuleNotFoundError HOT 3
- Imbalanced Stoichiometry/ Missing Atom Mapping HOT 2
- Can not reproduce the result of Fig 4. HOT 2
- Can you share the code to reproduce the figure 4(A)? HOT 2
- pip error with python 3.11 HOT 13
- Error while generating Atom-atom mapping HOT 1
- Could you share the script to train the model? HOT 1
- if the atom mapping is invalid, should we remove this reaction when trainning model? HOT 2
- use get_attention_guided_atom_maps on generic compound HOT 1
- Dubious mapping case HOT 2
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