---
license: mit
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: 0.9497212171554565
---

# CompoundT5 

This model is a re-pretrained version of [google/t5-v1_1-base](https://huggingface.co/microsoft/deberta-base) on the sagawa/ZINC-canonicalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1202
- Accuracy: 0.9497


## Model description

We trained t5 on SMILES from ZINC using masked-language modeling (MLM). Its tokenizer is also trained on ZINC.


## Intended uses & limitations

This model can be used to predict molecules' properties, reactions, or interactions with proteins by changing the way of finetuning.
As an example, We finetuned this model to predict products. The model is [here](https://huggingface.co/sagawa/ZINC-t5-productpredicition), and you can use the demo [here](https://huggingface.co/spaces/sagawa/predictproduct-t5).
Using its encoder, we trained a regression model to predict a reaction yield. You can use this demo [here](https://huggingface.co/spaces/sagawa/predictyield-t5).

## Training and evaluation data

We downloaded [ZINC data](https://drive.google.com/drive/folders/1lSPCqh31zxTVEhuiPde7W3rZG8kPgp-z) and canonicalized them using RDKit. Then, we dropped 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-03
- train_batch_size: 30
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30.0

### Training results

| Training Loss | Step   | Accuracy | Validation Loss |
|:-------------:|:------:|:--------:|:---------------:|
| 0.2471        | 25000  | 0.9843   | 0.2226          |
| 0.1871        | 50000  | 0.9314   | 0.1783          |
| 0.1791        | 75000  | 0.9371   | 0.1619          |
| 0.1596        | 100000 | 0.9401   | 0.1520          |
| 0.1522        | 125000 | 0.9422   | 0.1449          |
| 0.1435        | 150000 | 0.9436   | 0.1404          |
| 0.1421        | 175000 | 0.9447   | 0.1368          |
| 0.1398        | 200000 | 0.9459   | 0.1322          |
| 0.1297        | 225000 | 0.9466   | 0.1299          |
| 0.1324        | 250000 | 0.9473   | 0.1268          |
| 0.1257        | 275000 | 0.9483   | 0.1244          |
| 0.1266        | 300000 | 0.9491   | 0.1216          |
| 0.1301        | 325000 | 0.9497   | 0.1204          |