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---
license: mit
tags:
- generated_from_trainer
datasets:
- sagawa/ZINC-canonicalized
metrics:
- accuracy
model-index:
- name: ZINC-deberta
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: sagawa/ZINC-canonicalized
type: sagawa/ZINC-canonicalized
metrics:
- name: Accuracy
type: accuracy
value: 0.9900059572833486
---
<!-- 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. -->
# ZINC-deberta-base-output
This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0237
- Accuracy: 0.9900
## Model description
We trained deberta-base on SMILES from ZINC using the task of masked-language modeling (MLM). Its tokenizer is a character-level tokenizer trained on ZINC.
## 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 [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we droped duplicates. The total number of data is 22992522, 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: 20
- eval_batch_size: 32
- 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 | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 0.045 | 1.06 | 100000 | 0.9842 | 0.0409 |
| 0.0372 | 2.13 | 200000 | 0.9864 | 0.0346 |
| 0.0337 | 3.19 | 300000 | 0.9874 | 0.0314 |
| 0.0318 | 4.25 | 400000 | 0.9882 | 0.0293 |
| 0.0296 | 5.31 | 500000 | 0.0277 | 0.9887 |
| 0.0289 | 6.38 | 600000 | 0.0264 | 0.9891 |
| 0.0267 | 7.44 | 700000 | 0.0253 | 0.9894 |
| 0.0261 | 8.5 | 800000 | 0.0243 | 0.9898 |
| 0.025 | 9.57 | 900000 | 0.0238 | 0.9900 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.12.0
- Datasets 2.4.1.dev0
- Tokenizers 0.11.6
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