metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - sagawa/pubchem-10m-canonicalized
metrics:
  - accuracy
model-index:
  - name: PubChem-10m-deberta
    results:
      - task:
          name: Masked Language Modeling
          type: fill-mask
        dataset:
          name: sagawa/pubchem-10m-canonicalized
          type: sagawa/pubchem-10m-canonicalized
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9741235263046233

PubChem10m-deberta-base-output

This model is a fine-tuned version of microsoft/deberta-base on the sagawa/pubchem-10m-canonicalized dataset. It achieves the following results on the evaluation set:

Loss: 0.0698
Accuracy: 0.9741

Model description

We trained deberta-base on SMILES from PubChem using the task of masked-language modeling (MLM). Its tokenizer is a character-level tokenizer trained on PubChem.

Intended uses & limitations

This model can be used for the prediction of molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.

Training and evaluation data

We downloaded PubChem data and canonicalized them using RDKit. Then, we dropped duplicates. The total number of data is 9999960, and they were randomly split into train:validation=10:1.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 30
eval_batch_size: 48
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.0855	3.68	100000	0.0801	0.9708
0.0733	7.37	200000	0.0702	0.9740

Framework versions

Transformers 4.22.0.dev0
Pytorch 1.12.0
Datasets 2.4.1.dev0
Tokenizers 0.11.6