Good morning, thanks for the interesting work!
Could you please provide a more detailed explanation as to how a pretrained network can be used for a classification task?
As far as I understood, a user should download at least the ChEMBL_1M_atom models folder and put it into a parent folder inside the project. Then, notebook 05_Pretrained_Models.ipynb should be run entirely. Ultimately, the user can skip the finetuning procedure present in notebook 02_MSPM_TS_finetuning.ipynb and jump directly to running notebook 03_QSAR_Classifcation.ipynb.
I am having troubles running the code present in notebook 03_QSAR_Classifcation.ipynb.
Specifically, I keep getting the following error when running
lm_learner = lm_learner.load_pretrained(*fnames):
KeyError: '0.encoder.weight'.
Even when running the code as-is on a fresh clone on Google Colab without any modification, I cannot get rid of this error.
I only installed RDKit and FastAI (v. 1.0.61) to make the code run.
Thank you in advance for your time!
Good morning, thanks for the interesting work!
Could you please provide a more detailed explanation as to how a pretrained network can be used for a classification task?
As far as I understood, a user should download at least the
ChEMBL_1M_atommodels folder and put it into a parent folder inside the project. Then, notebook05_Pretrained_Models.ipynbshould be run entirely. Ultimately, the user can skip the finetuning procedure present in notebook02_MSPM_TS_finetuning.ipynband jump directly to running notebook03_QSAR_Classifcation.ipynb.I am having troubles running the code present in notebook
03_QSAR_Classifcation.ipynb.Specifically, I keep getting the following error when running
lm_learner = lm_learner.load_pretrained(*fnames):KeyError: '0.encoder.weight'.Even when running the code as-is on a fresh clone on Google Colab without any modification, I cannot get rid of this error.
I only installed RDKit and FastAI (v. 1.0.61) to make the code run.
Thank you in advance for your time!