---
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
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# 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