---
license: mit
tags:
- generated_from_trainer
datasets:
- sagawa/ZINC-canonicalized
metrics:
- accuracy
base_model: microsoft/deberta-base
model-index:
- name: ZINC-deberta
  results:
  - task:
      type: fill-mask
      name: Masked Language Modeling
    dataset:
      name: sagawa/ZINC-canonicalized
      type: sagawa/ZINC-canonicalized
    metrics:
    - type: accuracy
      value: 0.9900059572833486
      name: Accuracy
---

<!-- 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