metadata
license: apache-2.0
language:
- en
tags:
- chemistry
- biology
- medical
Pre-trained T5-small model on PseudoMD-1M datasets.
PseudoMD-1M dataset is the first artificially-real dataset for cross-modal molecule discovery, which consists of 1,020,139 pseudo molecule-description pairs. Every molecule is represented using its Canonical SMILES notation, sourced from PubChem via the PUG View API. On average, each description within PseudoMD-1M contains 5.11 sentences, 106.47 words, and 165.07 tokens. We provide five examples in Appendix A in the paper.
Pre-training details
| Parameters | N |
|---|---|
| Corpus Size | 1,020,139 |
| Training Steps | 100,000 |
| Learning Rate | 1e-3 |
| Batch Size | 128 |
| Warm-up Steps | 1000 |
| Weight decay | 0.1 |
Example Usage
from transformers import AutoTokenizer, T5ForConditionalGeneration
model = T5ForConditionalGeneration.from_pretrained("SCIR-HI/ada-t5-small")
tokenizer = AutoTokenizer.from_pretrained("SCIR-HI/ada-t5-small", model_max_length=512)
Citation
@article{chen2023artificially,
title={From Artificially Real to Real: Leveraging Pseudo Data from Large Language Models for Low-Resource Molecule Discovery},
author={Chen, Yuhan and Xi, Nuwa and Du, Yanrui and Wang, Haochun and Jianyu, Chen and Zhao, Sendong and Qin, Bing},
journal={arXiv preprint arXiv:2309.05203},
year={2023}
}