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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# PubChem10m-deberta-base-output
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/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](https://drive.google.com/file/d/1ygYs8dy1-vxD1Vx6Ux7ftrXwZctFjpV3/view) 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
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