### 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](https://arxiv.org/abs/2309.05203).


### 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

```python
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](https://arxiv.org/abs/2309.05203)

```bibtex
@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}
}
```
{"language": ["en"], "license": "apache-2.0", "tags": ["chemistry", "biology", "medical"]}
transformers pytorch t5 text2text-generation chemistry biology medical en arxiv:2309.05203 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us