---
license: mit
datasets:
- sagawa/pubchem-10m-canonicalized
metrics:
- accuracy
model-index:
- name: PubChem-10m-t5
  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: 0.9189779162406921
---

# PubChem-10m-t5

This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co./microsoft/deberta-base) on the sagawa/pubchem-10m-canonicalized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2165
- Accuracy: 0.9190


## Model description

We trained t5 on SMILES from PubChem using the task of masked-language modeling (MLM). Compared to PubChem-10m-t5, PubChem-10m-t5-v2 uses a character-level tokenizer, and it was also 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-03
- 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 | Step   | Accuracy | Validation Loss |
|:-------------:|:------:|:--------:|:---------------:|
| 0.2592        | 100000 | 0.8997   | 0.2784          |
| 0.2790        | 200000 | 0.9095   | 0.2468          |
| 0.2278        | 300000 | 0.9162   | 0.2256          |