hubiodatalab / selformer Goto Github PK

Hello,
Thank you very much for the great work! When I use the pre-trained model on BindingDB dataset, some molecule goes well, but some molecule show the following error:
RuntimeError: The expanded size of the tensor (570) must match the existing size (514) at non-singleton dimension 1. Target sizes: [1, 570]. Tensor sizes: [1, 514]
How can I solve the problem, Thank you!

Hi,

I would like to generate embeddings from the pretrained model. I follow the instructions in the readme file, but it report weights not initialized and get stuck at this stage. How do I solve this problem?

Thanks!

MacBook-Pro SELFormer % python3 produce_embeddings.py --selfies_dataset=data/molecule_dataset_selfies.csv --model_file=data/pretrained_models/modelO --embed_file=data/embeddings.csv
Some weights of RobertaModel were not initialized from the model checkpoint at ./data/pretrained_models/modelM and are newly initialized: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Starting
INFO: Pandarallel will run on 1 workers.
INFO: Pandarallel will use standard multiprocessing data transfer (pipe) to transfer data between the main process and workers.
We strongly recommend passing in an attention_mask since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked.

Thank you for your great work!
when I try to use the model from hugging face like this:

# Load model directly
        from transformers import AutoTokenizer, AutoModelForMaskedLM

        tokenizer = AutoTokenizer.from_pretrained("HUBioDataLab/SELFormer")
        model = AutoModelForMaskedLM.from_pretrained("HUBioDataLab/SELFormer")

I got the following error message. Am I use it in a wrong way?

Exception has occurred: OSError
We couldn't connect to 'https://huggingface.co' to load this file, couldn't find it in the cached files and it looks like HUBioDataLab/SELFormer is not the path to a directory containing a file named config.json.
Checkout your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.
huggingface_hub.utils._errors.LocalEntryNotFoundError: Connection error, and we cannot find the requested files in the disk cache. Please try again or make sure your Internet connection is on.

Hi, I want to do regression task with your model.
You uploaded 3 models in cloud, modelO, modelC, modelM.

What is different in those 3 models?

I fine-tuned modelO, but it doesn't do well with it.
So I will try all of the model, but I want to be clear.

Thanks

Hello,

very nice publication and work. can you please guide if I were to use your pre-training and fine-tuning model for generating new molecules, how would i go about it?

would i just use pre-training model and use another method for design? i have my own dataset of structure and its activity and i want to generate new molecules. i am very new leaner in this ml field. really appreciate your guidance,
JL

Hello.
I want to generate embeddings for my dataset.

I run this command:

python3 produce_embeddings.py --selfies_dataset=data/molecule_dataset_selfies.csv --model_file=data/pretrained_models/modelO --embed_file=data/embeddings.csv

but there is an error:
OSError: Can't load the configuration of 'data/pretrained_models/modelO'. If you were trying to load it from 'https://huggingface.co/models', make sure you don't have a local directory with the same name. Otherwise, make sure 'data/pretrained_models/modelO' is the correct path to a directory containing a config.json file

How to resolve this error?

Hello,
I tried to install SELFormer through conda, however, I get an error stating that "Your installed version is: not available". Do you have any suggestions for the installation?

**(SELFormer_env) [sg6615@della-gpu SELFormer]$ conda env update --file ./data/requirements.yml

Collecting package metadata (repodata.json): - WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.6.0., but conda is ignoring the . and treating it as 1.6.0

WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.7.1., but conda is ignoring the . and treating it as 1.7.1

WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.8.0., but conda is ignoring the . and treating it as 1.8.0

WARNING conda.models.version:get_matcher(556): Using .* with relational operator is superfluous and deprecated and will be removed in a future version of conda. Your spec was 1.9.0., but conda is ignoring the . and treating it as 1.9.0

done

Solving environment: -

Found conflicts! Looking for incompatible packages.

This can take several minutes. Press CTRL-C to abort.

Examining conflict for black google-auth-oauthlib flask __unix sacremoses urllib3: : 289it [3Examining conflict for aws-sdk-cpp aws-c-event-stream aws-c-io: : 290it [38:42, 3.50s/it] Examining conflict for aws-sdk-cpp aws-checksums aws-c-event-stream: : 291it [38:42, 2.23s/iExamining conflict for mypy_extensions typed-argument-parser typing_inspect black: : 292it [3Examining conflict for s2n aws-c-event-stream aws-c-io: : 293it [38:42, 2.23s/it] Examining conflict for google-auth-oauthlib tensorboard requests-oauthlib: : 296it [39:06, 1Examining conflict for google-auth-oauthlib tensorboard requests-oauthlib: : 297it [39:06, 4Examining conflict for google-auth-oauthlib flask sacremoses black urllib3: : 297it [39:07, Examining conflict for google-auth-oauthlib flask sacremoses black urllib3: : 298it [39:07, Examining conflict for aws-c-cal aws-c-event-stream aws-c-io: : 298it [39:08, 3.99s/it] failed

Solving environment: \

Found conflicts! Looking for incompatible packages.

This can take several minutes. Press CTRL-C to abort.

Examining conflict for hyperopt numpy tensorflow-base mkl_random chemprop xarray torchvision pandasExamining conflict for hyperopt charset-normalizer colorama tensorboard-data-server numpy pyyaml mkExamining conflict for hyperopt charset-normalizer colorama tensorboard-data-server numpy pyyaml mkExamining conflict for hyperopt numpy jupyter_client tensorflow-base mkl_random chemprop xarray oauExamining conflict for hyperopt numpy jupyter_client tensorflow-base mkl_random chemprop xarray oauExamining conflict for hyperopt charset-normalizer colorama tensorboard-data-server numpy pyyaml mkExamining conflict for hyperopt charset-normalizer colorama tensorboard-data-server numpy pyyaml mkExamining conflict for hyperopt charset-normalizer colorama numpy mkl_random chemprop parso filelocExamining conflict for hyperopt charset-normalizer colorama numpy mkl_random chemprop parso filelocExamining conflict for hyperopt charset-normalizer colorama tensorboard-data-server numpy pyyaml m!
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  • orc==1.6.7=h89a63ab_2 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • pandas==1.3.4=py38h8c16a72_0 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • parquet-cpp==1.5.1=2 -> arrow-cpp[version='>=0.11.0'] -> __cuda[version='>=10.2|>=11.2']

  • pcre==8.45=h9c3ff4c_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • pillow==6.2.1=py38h6b7be26_0 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • pixman==0.38.0=h516909a_1003 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • protobuf==3.16.0=py38h709712a_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • pthread-stubs==0.4=h36c2ea0_1001 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • pyarrow==3.0.0=py38hc9229eb_13_cpu -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • pycairo==1.20.1=py38hf61ee4a_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • pymongo==4.0.2=py38hfa26641_0 -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']

  • python-xxhash==3.0.0=py38h0a891b7_0 -> libgcc-ng[version='>=10.3.0'] -> __glibc[version='>=2.17']

  • python==3.8.12=h12debd9_0 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • pytorch==1.10.1=py3.8_cuda11.3_cudnn8.2.0_0 -> cudatoolkit[version='>=11.3,<11.4'] -> __glibc[version='>=2.17,<3.0.a0']

  • pyyaml==5.3.1=py38h7b6447c_1 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • pyzmq==22.3.0=py38h2035c66_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • rdkit==2021.09.2=py38h8c3fb5a_0 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • re2==2021.04.01=h9c3ff4c_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • readline==8.1=h27cfd23_0 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • regex==2021.11.10=py38h497a2fe_0 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • reportlab==3.5.68=py38hadf75a6_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • s2n==1.0.10=h9b69904_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • sacremoses==0.0.46=pyhd8ed1ab_0 -> click -> __unix

  • sacremoses==0.0.46=pyhd8ed1ab_0 -> click -> __win

  • scikit-learn==1.0.2=py38h51133e4_1 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • scipy==1.7.1=py38h292c36d_2 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • sleef==3.5.1=h9b69904_2 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • snappy==1.1.8=he1b5a44_3 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • sqlalchemy==1.4.27=py38h497a2fe_0 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • sqlite==3.36.0=hc218d9a_0 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • tensorboard-data-server==0.6.0=py38h3e25421_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • tensorflow-base==2.4.1=mkl_py38h43e0292_0 -> libgcc-ng[version='>=5.4.0'] -> __glibc[version='>=2.17']

  • tensorflow-estimator==2.6.0=py38h709712a_0 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • tensorflow==2.4.1=mkl_py38hb2083e0_0 -> tensorflow-estimator[version='>=2.4.1'] -> __glibc[version='>=2.17']

  • tk==8.6.11=h1ccaba5_0 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • tokenizers==0.10.3=py38hb63a372_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • torchaudio==0.10.1=py38_cu113 -> cudatoolkit[version='>=11.3,<11.4'] -> __glibc[version='>=2.17|>=2.17,<3.0.a0']

  • torchvision==0.11.2=py38_cu113 -> cudatoolkit[version='>=11.3,<11.4'] -> __glibc[version='>=2.17|>=2.17,<3.0.a0']

  • tornado==6.1=py38h497a2fe_2 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • transformers==4.15.0=pyhd8ed1ab_0 -> pytorch -> __cuda[version='>=11.8']

  • transformers==4.15.0=pyhd8ed1ab_0 -> pytorch -> __glibc[version='>=2.17|>=2.17,<3.0.a0']

  • typed-ast==1.4.3=py38h7f8727e_1 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • unicodedata2==13.0.0.post2=py38h497a2fe_4 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • urllib3==1.26.7=pyhd8ed1ab_0 -> pysocks[version='>=1.5.6,<2.0,!=1.5.7'] -> __unix

  • urllib3==1.26.7=pyhd8ed1ab_0 -> pysocks[version='>=1.5.6,<2.0,!=1.5.7'] -> __win

  • wrapt==1.13.3=py38h497a2fe_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • xorg-kbproto==1.0.7=h7f98852_1002 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libice==1.0.10=h7f98852_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libsm==1.2.3=hd9c2040_1000 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libx11==1.7.2=h7f98852_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libxau==1.0.9=h7f98852_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libxdmcp==1.1.3=h7f98852_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libxext==1.3.4=h7f98852_1 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-libxrender==0.9.10=h7f98852_1003 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-renderproto==0.11.1=h7f98852_1002 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-xextproto==7.3.0=h7f98852_1002 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xorg-xproto==7.0.31=h7f98852_1007 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xxhash==0.8.0=h7f98852_3 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

  • xz==5.2.5=h7b6447c_0 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • yaml==0.2.5=h516909a_0 -> libgcc-ng[version='>=7.5.0'] -> __glibc[version='>=2.17']

  • yarl==1.7.2=py38h497a2fe_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • zeromq==4.3.4=h9c3ff4c_1 -> libgcc-ng[version='>=9.4.0'] -> __glibc[version='>=2.17']

  • zlib==1.2.11=h7b6447c_3 -> libgcc-ng[version='>=7.3.0'] -> __glibc[version='>=2.17']

  • zstd==1.4.9=ha95c52a_0 -> libgcc-ng[version='>=9.3.0'] -> __glibc[version='>=2.17']

Your installed version is: not available**

I see that this repo is already using huggingface packages. It would be a lot more accessible for everyone if these models were hosted in huggingface. I believe using an official huggingface organization page will be highly beneficial.